{"id":11935,"date":"2026-09-23T00:09:45","date_gmt":"2026-09-23T03:09:45","guid":{"rendered":"https:\/\/dtmstudio.com.br\/DTM\/?p=11935"},"modified":"2026-09-23T00:09:46","modified_gmt":"2026-09-23T03:09:46","slug":"gpt-6-astra-x-claude-opus-5-5","status":"publish","type":"post","link":"https:\/\/dtmstudio.com.br\/DTM\/gpt-6-astra-x-claude-opus-5-5\/","title":{"rendered":"GPT-6 Astra \u00d7 Claude Opus 5.5"},"content":{"rendered":"\n[et_pb_section fb_built=&#8221;1&#8243; inner_shadow=&#8221;on&#8221; fullwidth=&#8221;on&#8221; custom_padding_last_edited=&#8221;on|desktop&#8221; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.27.4&#8243; background_color=&#8221;#2ea3f2&#8243; background_image=&#8221;https:\/\/dtmstudio.com.br\/DTM\/wp-content\/uploads\/2015\/08\/DTM-Cubos-3D_1680x1050.jpg&#8221; parallax=&#8221;on&#8221; parallax_method=&#8221;off&#8221; max_width_last_edited=&#8221;off|desktop&#8221; custom_padding_tablet=&#8221;50px|0|50px|0&#8243; 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[et_pb_line_break_holder] -->.eyebrow{font-family:var(--display)!important;font-weight:500;letter-spacing:.2em}<!-- [et_pb_line_break_holder] -->.o{color:var(--opus-text)} .pill.po{color:var(--opus-text)}<!-- [et_pb_line_break_holder] -->.sec-head .eyebrow{color:var(--opus-text)!important}<!-- [et_pb_line_break_holder] -->.sheet,.tbl-wrap,.tasks,.why div,.chart,.calc,.rec > div,.proto,.note,.ratio{border-radius:var(--r)!important;overflow:hidden}<!-- [et_pb_line_break_holder] -->input{border-radius:4px}<!-- [et_pb_line_break_holder] -->.pill{border-radius:999px!important;border-width:1px!important}<!-- [et_pb_line_break_holder] -->.sheet{background:#050505!important;color:#F8F5EE;border:0!important}<!-- [et_pb_line_break_holder] -->.sheet .eyebrow,.sheet .muted{color:#8A887F!important}<!-- [et_pb_line_break_holder] -->.sheet .a{color:#F8F5EE} .sheet .o{color:#D4AF37}<!-- [et_pb_line_break_holder] -->.sheet .stamp{border-left:1px solid #2A2927!important}<!-- [et_pb_line_break_holder] -->.sheet .stamp div{border-bottom:1px solid #2A2927!important}<!-- [et_pb_line_break_holder] -->.sheet .stamp .eyebrow{color:#D4AF37!important}<!-- [et_pb_line_break_holder] -->.sheet .verdict,.sheet .verdict div{border-color:#2A2927!important}<!-- [et_pb_line_break_holder] -->.sheet .lede{color:#E5E0D3}<!-- [et_pb_line_break_holder] -->.logo{height:64px;width:auto;display:block}<!-- [et_pb_line_break_holder] -->.prog{display:grid;grid-template-columns:repeat(4,1fr);gap:4px;height:5px}<!-- [et_pb_line_break_holder] -->.prog span:nth-child(1),.prog span:nth-child(2){background:#F8F5EE;opacity:.9}<!-- [et_pb_line_break_holder] -->.prog span:nth-child(3){background:#57564F}.prog span:nth-child(4){background:#D4AF37}<!-- [et_pb_line_break_holder] -->.calc{border:1px solid var(--line)!important}.calc-in{border-color:var(--line)!important}<!-- [et_pb_line_break_holder] -->.note{background:var(--opus-soft)!important;border-left:3px solid var(--opus)!important}<!-- [et_pb_line_break_holder] -->.rec .o-top{border-top-color:var(--opus)!important}.rec .a-top{border-top-color:var(--astra)!important}<!-- [et_pb_line_break_holder] -->.proto{padding:0!important;margin:0}<!-- [et_pb_line_break_holder] -->.presets{display:flex;flex-wrap:wrap;gap:6px;margin-top:4px}<!-- [et_pb_line_break_holder] -->.presets button{font:500 12px var(--body);padding:6px 11px;border:1px solid var(--line);background:var(--ground);color:var(--ink);border-radius:999px;cursor:pointer}<!-- [et_pb_line_break_holder] -->.presets button:hover{border-color:var(--opus)}.presets button:focus-visible{outline:2px solid var(--opus);outline-offset:1px}<!-- [et_pb_line_break_holder] -->.sim{background:var(--paper);border:1px solid var(--line);border-radius:var(--r);display:grid;grid-template-columns:1.15fr 1fr;overflow:hidden}<!-- [et_pb_line_break_holder] -->.sim-in{padding:22px;display:grid;gap:12px;align-content:start;border-right:1px solid var(--line)}<!-- [et_pb_line_break_holder] -->.sim-out{padding:22px;display:grid;gap:16px;align-content:start;background:var(--ground)}<!-- [et_pb_line_break_holder] -->.step{padding-top:4px}<!-- [et_pb_line_break_holder] -->#simTasks{display:grid;gap:0}<!-- [et_pb_line_break_holder] -->.qrow{display:grid;grid-template-columns:1fr auto auto;gap:10px;align-items:center;padding:10px 0;border-bottom:1px solid var(--line2)}<!-- [et_pb_line_break_holder] -->.qrow label{display:grid;gap:2px;font-size:15px}.qrow small{color:var(--muted);font-size:12.5px;line-height:1.35}<!-- [et_pb_line_break_holder] -->.per{font-size:12px;color:var(--muted)}<!-- [et_pb_line_break_holder] -->.stepper{display:flex;align-items:center;border:1px solid var(--line);border-radius:999px;overflow:hidden;background:var(--ground)}<!-- [et_pb_line_break_holder] -->.stepper button{width:32px;height:32px;border:0;background:transparent;color:var(--ink);font:500 18px\/1 var(--body);cursor:pointer}<!-- [et_pb_line_break_holder] -->.stepper button:hover{background:var(--line2)}<!-- [et_pb_line_break_holder] -->.stepper input{width:52px;border:0!important;background:transparent!important;text-align:center!important;padding:6px 0!important;-moz-appearance:textfield;border-radius:0!important}<!-- [et_pb_line_break_holder] -->.stepper input::-webkit-outer-spin-button,.stepper input::-webkit-inner-spin-button{-webkit-appearance:none;margin:0}<!-- [et_pb_line_break_holder] -->.effort{display:grid;grid-template-columns:repeat(3,1fr);gap:8px}<!-- [et_pb_line_break_holder] -->.effort button{display:grid;gap:3px;text-align:left;padding:12px;border:1px solid var(--line);border-radius:var(--r);background:var(--ground);color:var(--ink);cursor:pointer;font:400 12px\/1.35 var(--body)}<!-- [et_pb_line_break_holder] -->.effort button b{font:600 15px var(--display)}<!-- [et_pb_line_break_holder] -->.effort button span{color:var(--muted)}<!-- [et_pb_line_break_holder] -->.effort button[aria-checked=\"true\"]{border-color:var(--opus);box-shadow:inset 0 0 0 1px var(--opus);background:var(--opus-soft)}<!-- [et_pb_line_break_holder] -->.effort button:focus-visible,.stepper button:focus-visible{outline:2px solid var(--opus);outline-offset:1px}<!-- [et_pb_line_break_holder] -->.res{display:grid;grid-template-columns:1fr 1fr;gap:10px}<!-- [et_pb_line_break_holder] -->.res > div{background:var(--paper);border:1px solid var(--line);border-radius:var(--r);padding:14px;display:grid;gap:4px}<!-- [et_pb_line_break_holder] -->.res strong{font:600 clamp(26px,3.4vw,36px)\/1.05 var(--display);font-variant-numeric:tabular-nums}<!-- [et_pb_line_break_holder] -->.res .card-a{border-top:3px solid var(--astra)}.res .card-o{border-top:3px solid var(--opus)}<!-- [et_pb_line_break_holder] -->.res .card-o strong{color:var(--opus-text)}<!-- [et_pb_line_break_holder] -->.ratio b{font-weight:700}<!-- [et_pb_line_break_holder] -->table.mini{min-width:0}<!-- [et_pb_line_break_holder] -->table.mini th,table.mini td{padding:7px 10px;font-size:13px}<!-- [et_pb_line_break_holder] -->.how{background:var(--paper);border:1px solid var(--line);border-radius:var(--r);padding:14px 18px}<!-- [et_pb_line_break_holder] -->.how summary{cursor:pointer;font:600 14px var(--display)}<!-- [et_pb_line_break_holder] -->.how[open]{display:grid;gap:10px}<!-- [et_pb_line_break_holder] -->.how[open] summary{margin-bottom:6px}<!-- [et_pb_line_break_holder] -->@media (max-width:860px){.sim{grid-template-columns:1fr}.sim-in{border-right:0;border-bottom:1px solid var(--line)}}<!-- [et_pb_line_break_holder] -->@media (max-width:480px){.effort{grid-template-columns:1fr}.res{grid-template-columns:1fr}.qrow{grid-template-columns:1fr auto}.per{display:none}}<!-- [et_pb_line_break_holder] -->footer.brandfoot{background:#050505;color:#8A887F;border-radius:var(--r);padding:28px;display:flex;flex-wrap:wrap;gap:16px;align-items:center;justify-content:space-between;font:500 11px\/1.4 var(--display);letter-spacing:.18em;text-transform:uppercase}<!-- [et_pb_line_break_holder] -->@media (max-width:820px){.sheet .stamp{border-left:0!important;border-top:1px solid #2A2927}}<!-- [et_pb_line_break_holder] --><\/style><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><div class=\"wrap\"><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- CARIMBO --><!-- [et_pb_line_break_holder] --><header class=\"sheet\"><!-- [et_pb_line_break_holder] -->  <div class=\"sheet-main\"><!-- [et_pb_line_break_holder] -->    <img decoding=\"async\" class=\"logo\" 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\" alt=\"DTM Academy\"\/><!-- [et_pb_line_break_holder] -->    <span class=\"eyebrow\">Comparativo t\u00e9cnico \u00b7 IA no escrit\u00f3rio de arquitetura<\/span><!-- [et_pb_line_break_holder] -->    <h1><span class=\"a\">GPT-6 Astra<\/span><!\u2013- [et_pb_br_holder] -\u2013>\u00d7 <span class=\"o\">Claude Opus 5.5<\/span><\/h1><!-- [et_pb_line_break_holder] -->    <div class=\"prog\" aria-hidden=\"true\"><span><\/span><span><\/span><span><\/span><span><\/span><\/div><!-- [et_pb_line_break_holder] -->    <pee class=\"lede\">O GPT-6 Astra foi lan\u00e7ado em 03\/09\/2026; o Opus 5.5 chegou hoje, 22\/09\/2026. Este quadro cruza apenas os benchmarks publicados por ambos, traduzidos para as tarefas reais de um escrit\u00f3rio: memoriais, propostas, scripts para Revit\/Dynamo, automa\u00e7\u00f5es, leitura de pranchas e modelagem CAD.<\/pee><!-- [et_pb_line_break_holder] -->    <div class=\"verdict\"><!-- [et_pb_line_break_holder] -->      <div><span class=\"eyebrow\">Custo por token<\/span><strong class=\"o\">Opus 2,5\u00d7 mais barato<\/strong><span class=\"small muted\">US$ 4\/20 contra US$ 10\/50 por 1M tokens<\/span><\/div><!-- [et_pb_line_break_holder] -->      <div><span class=\"eyebrow\">Tarefas de escrit\u00f3rio<\/span><strong class=\"o\">Opus 3 \u00d7 1 Astra<\/strong><span class=\"small muted\">mais 2 empates, nos 6 testes publicados pelos dois<\/span><\/div><!-- [et_pb_line_break_holder] -->      <div><span class=\"eyebrow\">CAD \/ geometria 3D<\/span><strong class=\"a\">Astra lidera<\/strong><span class=\"small muted\">BenchCAD 95,9% \u00b7 Opus 5.5 ainda sem nota publicada<\/span><\/div><!-- [et_pb_line_break_holder] -->    <\/div><!-- [et_pb_line_break_holder] -->  <\/div><!-- [et_pb_line_break_holder] -->  <aside class=\"stamp\" aria-label=\"Carimbo\"><!-- [et_pb_line_break_holder] -->    <div><span class=\"eyebrow\">Projeto<\/span><b>Avalia\u00e7\u00e3o de modelos de IA<\/b><\/div><!-- [et_pb_line_break_holder] -->    <div><span class=\"eyebrow\">Prancha<\/span><b>COMP-01 \u00b7 Astra \u00d7 Opus<\/b><\/div><!-- [et_pb_line_break_holder] -->    <div><span class=\"eyebrow\">Escala<\/span><b class=\"num\">US$ \/ 1M tokens<\/b><\/div><!-- [et_pb_line_break_holder] -->    <div><span class=\"eyebrow\">Data<\/span><b class=\"num\">22.09.2026<\/b><\/div><!-- [et_pb_line_break_holder] -->    <div><span class=\"eyebrow\">Autor<\/span><b>Arq. Jonatas C. Olim<\/b><\/div><!-- [et_pb_line_break_holder] -->    <div><span class=\"eyebrow\">Revis\u00e3o<\/span><b class=\"num\">R00 \u00b7 dados de lan\u00e7amento<\/b><\/div><!-- [et_pb_line_break_holder] -->  <\/aside><!-- [et_pb_line_break_holder] --><\/header><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- FICHA --><!-- [et_pb_line_break_holder] --><section class=\"sec\"><!-- [et_pb_line_break_holder] -->  <div class=\"sec-head\"><span class=\"eyebrow\">01 \u00b7 Ficha t\u00e9cnica<\/span><h2>O que cada um cobra e aguenta<\/h2><\/div><!-- [et_pb_line_break_holder] -->  <div class=\"tbl-wrap\"><table><!-- [et_pb_line_break_holder] -->    <thead><tr><th>Item<\/th><th class=\"a\">GPT-6 Astra<\/th><th class=\"o\">Claude Opus 5.5<\/th><th>Leitura<\/th><\/tr><\/thead><!-- [et_pb_line_break_holder] -->    <tbody><!-- [et_pb_line_break_holder] -->      <tr><td>Entrada (1M tokens)<\/td><td class=\"n\">US$ 10,00<\/td><td class=\"n\">US$ 4,00<\/td><td>Opus 2,5\u00d7 mais barato<\/td><\/tr><!-- [et_pb_line_break_holder] -->      <tr><td>Sa\u00edda (1M tokens)<\/td><td class=\"n\">US$ 50,00<\/td><td class=\"n\">US$ 20,00<\/td><td>Opus 2,5\u00d7 mais barato<\/td><\/tr><!-- [et_pb_line_break_holder] -->      <tr><td>Leitura de cache<\/td><td class=\"n\">US$ 1,00<\/td><td class=\"n\">US$ 0,20<\/td><td>Opus 5\u00d7 mais barato \u2014 pesa em sess\u00f5es longas no Revit<\/td><\/tr><!-- [et_pb_line_break_holder] -->      <tr><td>Modo r\u00e1pido<\/td><td class=\"n\">2\u00d7 pre\u00e7o<\/td><td class=\"n\">US$ 8 \/ 40<\/td><td>Ambos dobram o pre\u00e7o<\/td><\/tr><!-- [et_pb_line_break_holder] -->      <tr><td>Contexto<\/td><td class=\"n\">1,05M tokens<\/td><td class=\"n\">1M tokens<\/td><td>Empate pr\u00e1tico (cabe um caderno de encargos inteiro)<\/td><\/tr><!-- [et_pb_line_break_holder] -->      <tr><td>Sa\u00edda m\u00e1xima<\/td><td class=\"n\">128k<\/td><td class=\"n\">128k<\/td><td>Empate<\/td><\/tr><!-- [et_pb_line_break_holder] -->      <tr><td>N\u00edveis de esfor\u00e7o<\/td><td>low \u2192 max<\/td><td>low \u2192 max (adaptativo)<\/td><td>Esfor\u00e7o alto = mais tokens de racioc\u00ednio nos dois<\/td><\/tr><!-- [et_pb_line_break_holder] -->    <\/tbody><!-- [et_pb_line_break_holder] -->  <\/table><\/div><!-- [et_pb_line_break_holder] --><\/section><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- TAREFAS --><!-- [et_pb_line_break_holder] --><section class=\"sec\"><!-- [et_pb_line_break_holder] -->  <div class=\"sec-head\"><span class=\"eyebrow\">02 \u00b7 Rotina do escrit\u00f3rio<\/span><h2>Tarefa a tarefa<\/h2><!-- [et_pb_line_break_holder] -->    <pee class=\"muted\">Cada linha usa o benchmark que mais se aproxima da tarefa. Barras na mesma escala por linha.<\/pee><\/div><!-- [et_pb_line_break_holder] -->  <div class=\"legend\"><span><i class=\"sw\" style=\"background:var(--astra)\"><\/i>GPT-6 Astra<\/span><span><i class=\"sw\" style=\"background:var(--opus)\"><\/i>Claude Opus 5.5<\/span><span class=\"muted\">n\/d = n\u00e3o publicado pelo fabricante<\/span><\/div><!-- [et_pb_line_break_holder] -->  <div class=\"tasks\" id=\"tasks\"><\/div><!-- [et_pb_line_break_holder] -->  <pee class=\"note\"><b>Operar o Revit\/AutoCAD pela tela<\/b> (computer use): Opus 5.5 marca 81,8% e Astra 72,6% no OSWorld 2.0, mas os dois rodaram subconjuntos diferentes de tarefas \u2014 n\u00e3o d\u00e1 para declarar vencedor. O Astra tem 92,7% no ScreenSpot-Pro (localizar bot\u00f5es em interfaces profissionais), sem nota equivalente do Opus.<\/pee><!-- [et_pb_line_break_holder] --><\/section><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- GASTOS --><!-- [et_pb_line_break_holder] --><section class=\"sec\"><!-- [et_pb_line_break_holder] -->  <div class=\"sec-head\"><span class=\"eyebrow\">03 \u00b7 Gastos<\/span><h2>Por que o Astra parece \u201cqueimar\u201d a cota<\/h2><!-- [et_pb_line_break_holder] -->    <pee class=\"muted\">Sua percep\u00e7\u00e3o tem fundamento, mas o motivo principal \u00e9 pre\u00e7o, n\u00e3o volume de racioc\u00ednio.<\/pee><\/div><!-- [et_pb_line_break_holder] -->  <div class=\"why\"><!-- [et_pb_line_break_holder] -->    <div><span class=\"big a\">2,5\u00d7<\/span><h3>Cada token custa mais<\/h3><pee class=\"small\">Entrada e sa\u00edda custam 2,5\u00d7 o pre\u00e7o do Opus. Planos por assinatura e cr\u00e9ditos do Codex descontam pelo custo, ent\u00e3o a mesma conversa consome a cota bem mais r\u00e1pido.<\/pee><\/div><!-- [et_pb_line_break_holder] -->    <div><span class=\"big a\">~40 min<\/span><h3>Tarefas longas de agente<\/h3><pee class=\"small\">No OSWorld 2.0 o Astra leva cerca de 40 min por tarefa. Em uso de computador cada passo reenvia capturas de tela \u2014 muita entrada, e o cache do Astra \u00e9 5\u00d7 mais caro.<\/pee><\/div><!-- [et_pb_line_break_holder] -->    <div><span class=\"big o\">~4\u00d7<\/span><h3>Mas o Opus \u201cpensa\u201d mais<\/h3><pee class=\"small\">No esfor\u00e7o m\u00e1ximo o Opus 5.5 gerou ~119 mil tokens de sa\u00edda por tarefa, cerca de 4\u00d7 o Astra (Artificial Analysis). Em <i>max<\/i>, o Opus pode sair mais caro. Em <i>medium<\/i>, ele entrega mais qualidade por d\u00f3lar.<\/pee><\/div><!-- [et_pb_line_break_holder] -->  <\/div><!-- [et_pb_line_break_holder] -->  <div class=\"charts\"><!-- [et_pb_line_break_holder] -->    <div class=\"chart\"><h3>Trabalho de escrit\u00f3rio (GDPval)<\/h3><span class=\"small muted\">Qualidade (Elo) \u00d7 custo por tarefa, em US$<\/span><svg id=\"c1\" viewBox=\"0 0 440 260\" role=\"img\" aria-label=\"Gr\u00e1fico GDPval custo versus qualidade\"><\/svg><\/div><!-- [et_pb_line_break_holder] -->    <div class=\"chart\"><h3>Scripts e automa\u00e7\u00e3o (Terminal-Bench 4.0)<\/h3><span class=\"small muted\">Acerto (%) \u00d7 custo por tarefa, em US$<\/span><svg id=\"c2\" viewBox=\"0 0 440 260\" role=\"img\" aria-label=\"Gr\u00e1fico Terminal-Bench custo versus acerto\"><\/svg><\/div><!-- [et_pb_line_break_holder] -->  <\/div><!-- [et_pb_line_break_holder] -->  <pee class=\"small muted\">Leitura: o Opus em esfor\u00e7o <i>medium<\/i> j\u00e1 supera o Astra em <i>max<\/i> no GDPval, gastando ~1\/5. No Terminal-Bench, Opus <i>medium<\/i> empata com Astra <i>high<\/i> custando 40%.<\/pee><!-- [et_pb_line_break_holder] --><\/section><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- CALCULADORA --><!-- [et_pb_line_break_holder] --><section class=\"sec\"><!-- [et_pb_line_break_holder] -->  <div class=\"sec-head\"><span class=\"eyebrow\">04 \u00b7 Simulador<\/span><h2>Quanto seu escrit\u00f3rio gastaria por m\u00eas<\/h2><!-- [et_pb_line_break_holder] -->    <pee class=\"muted\">Informe quantas vezes por m\u00eas a equipe faz cada tarefa com IA e escolha o n\u00edvel de esfor\u00e7o. Os dois modelos recebem exatamente as mesmas tarefas e o mesmo esfor\u00e7o.<\/pee><\/div><!-- [et_pb_line_break_holder] -->  <div class=\"sim\"><!-- [et_pb_line_break_holder] -->    <div class=\"sim-in\"><!-- [et_pb_line_break_holder] -->      <div class=\"step\"><span class=\"eyebrow\">1 \u00b7 Tarefas do m\u00eas<\/span><\/div><!-- [et_pb_line_break_holder] -->      <div id=\"simTasks\"><\/div><!-- [et_pb_line_break_holder] -->      <div class=\"step\"><span class=\"eyebrow\">2 \u00b7 Quanto a IA deve pensar<\/span><\/div><!-- [et_pb_line_break_holder] -->      <div class=\"effort\" role=\"radiogroup\" aria-label=\"N\u00edvel de esfor\u00e7o\"><!-- [et_pb_line_break_holder] -->        <button type=\"button\" role=\"radio\" data-e=\"eco\"><b>R\u00e1pido<\/b><span>Respostas diretas, tarefas simples<\/span><\/button><!-- [et_pb_line_break_holder] -->        <button type=\"button\" role=\"radio\" data-e=\"std\" aria-checked=\"true\"><b>Padr\u00e3o<\/b><span>Uso normal do dia a dia<\/span><\/button><!-- [et_pb_line_break_holder] -->        <button type=\"button\" role=\"radio\" data-e=\"max\"><b>M\u00e1ximo<\/b><span>Problemas dif\u00edceis, pensa muito mais<\/span><\/button><!-- [et_pb_line_break_holder] -->      <\/div><!-- [et_pb_line_break_holder] -->      <pee class=\"small muted\" id=\"effNote\"><\/pee><!-- [et_pb_line_break_holder] -->    <\/div><!-- [et_pb_line_break_holder] -->    <div class=\"sim-out\"><!-- [et_pb_line_break_holder] -->      <div class=\"res\"><!-- [et_pb_line_break_holder] -->        <div class=\"card-a\"><span class=\"eyebrow\">GPT-6 Astra<\/span><strong class=\"num\" id=\"tA\">\u2014<\/strong><span class=\"small muted\">por m\u00eas<\/span><\/div><!-- [et_pb_line_break_holder] -->        <div class=\"card-o\"><span class=\"eyebrow\">Claude Opus 5.5<\/span><strong class=\"num\" id=\"tO\">\u2014<\/strong><span class=\"small muted\">por m\u00eas<\/span><\/div><!-- [et_pb_line_break_holder] -->      <\/div><!-- [et_pb_line_break_holder] -->      <div class=\"ratio\" id=\"ratio\">\u2014<\/div><!-- [et_pb_line_break_holder] -->      <div class=\"tbl-wrap\"><table class=\"mini\"><thead><tr><th>Custo de 1 tarefa<\/th><th>Astra<\/th><th>Opus<\/th><\/tr><\/thead><tbody id=\"perTask\"><\/tbody><\/table><\/div><!-- [et_pb_line_break_holder] -->    <\/div><!-- [et_pb_line_break_holder] -->  <\/div><!-- [et_pb_line_break_holder] -->  <details class=\"how\"><summary>Como o c\u00e1lculo \u00e9 feito<\/summary><!-- [et_pb_line_break_holder] -->    <pee class=\"small\">Cada tarefa tem um volume t\u00edpico de texto lido pela IA (entrada) e escrito por ela (sa\u00edda), medido em tokens \u2014 1.000 tokens equivalem a cerca de 750 palavras. O custo usa o pre\u00e7o de tabela da API: Astra US$ 10 entrada \/ US$ 50 sa\u00edda, Opus US$ 4 \/ US$ 20 por milh\u00e3o de tokens, sem descontos de cache.<\/pee><!-- [et_pb_line_break_holder] -->    <pee class=\"small\">O esfor\u00e7o muda s\u00f3 o volume de sa\u00edda (o racioc\u00ednio da IA). R\u00e1pido: metade da sa\u00edda nos dois. Padr\u00e3o: volume igual nos dois. M\u00e1ximo: Opus 6\u00d7 e Astra 1,5\u00d7 \u2014 propor\u00e7\u00e3o baseada na medi\u00e7\u00e3o da Artificial Analysis, em que o Opus 5.5 no esfor\u00e7o m\u00e1ximo gerou cerca de 4\u00d7 mais tokens que o Astra. Os volumes por tarefa s\u00e3o estimativas e servem para comparar, n\u00e3o para or\u00e7ar.<\/pee><!-- [et_pb_line_break_holder] -->    <div class=\"tbl-wrap\"><table class=\"mini\"><thead><tr><th>Tarefa<\/th><th>Entrada<\/th><th>Sa\u00edda (padr\u00e3o)<\/th><\/tr><\/thead><tbody id=\"tokTbl\"><\/tbody><\/table><\/div><!-- [et_pb_line_break_holder] -->  <\/details><!-- [et_pb_line_break_holder] --><\/section><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- CAD --><!-- [et_pb_line_break_holder] --><section class=\"sec\"><!-- [et_pb_line_break_holder] -->  <div class=\"sec-head\"><span class=\"eyebrow\">05 \u00b7 CAD e Revit<\/span><h2>BenchCAD: reconstruir pe\u00e7as 3D a partir de imagens<\/h2><!-- [et_pb_line_break_holder] -->    <pee class=\"muted\">O BenchCAD mostra vistas renderizadas de uma pe\u00e7a e pede ao modelo que escreva o c\u00f3digo CAD (CadQuery) que a recria; a nota \u00e9 a sobreposi\u00e7\u00e3o volum\u00e9trica (IoU) com a pe\u00e7a original. \u00c9 o teste mais pr\u00f3ximo de \u201cler um desenho e modelar\u201d.<\/pee><\/div><!-- [et_pb_line_break_holder] -->  <div class=\"chart\"><svg id=\"c3\" viewBox=\"0 0 640 230\" role=\"img\" aria-label=\"Ranking BenchCAD\"><\/svg><\/div><!-- [et_pb_line_break_holder] -->  <pee class=\"note\">A Anthropic n\u00e3o publicou nota do Opus 5.5 no BenchCAD. O Opus 5 (anterior) marca 82,1%. Para modelagem param\u00e9trica via c\u00f3digo, o Astra tem vantagem clara hoje. Para Revit, por\u00e9m, o gargalo raramente \u00e9 geometria pura: \u00e9 API, par\u00e2metros, fam\u00edlias e padr\u00f5es \u2014 onde pesam Terminal-Bench e FrontierCode, que favorecem o Opus.<\/pee><!-- [et_pb_line_break_holder] -->  <h3>Protocolo de teste pr\u00e1tico no seu Revit<\/h3><!-- [et_pb_line_break_holder] -->  <pee class=\"small muted\">Benchmarks n\u00e3o substituem o teste no seu pr\u00f3prio modelo. Rode as mesmas 6 tarefas nos dois, mesmo esfor\u00e7o, e anote tempo, acerto e custo.<\/pee><!-- [et_pb_line_break_holder] -->  <ol class=\"proto\"><!-- [et_pb_line_break_holder] -->    <li><span>Listar paredes sem par\u00e2metro de acabamento preenchido e gerar planilha de pend\u00eancias<\/span><span class=\"m\">API \u00b7 leitura<\/span><\/li><!-- [et_pb_line_break_holder] -->    <li><span>Criar script pyRevit que renumera portas por n\u00edvel e ambiente<\/span><span class=\"m\">c\u00f3digo<\/span><\/li><!-- [et_pb_line_break_holder] -->    <li><span>Montar fam\u00edlia param\u00e9trica simples (bancada com largura\/profundidade)<\/span><span class=\"m\">geometria<\/span><\/li><!-- [et_pb_line_break_holder] -->    <li><span>Ler um PDF de prancha e apontar incompatibilidades de cotas<\/span><span class=\"m\">vis\u00e3o<\/span><\/li><!-- [et_pb_line_break_holder] -->    <li><span>Redigir memorial descritivo a partir das tabelas do modelo<\/span><span class=\"m\">texto<\/span><\/li><!-- [et_pb_line_break_holder] -->    <li><span>Aplicar padr\u00e3o de nomenclatura de vistas em 200 vistas (pela tela)<\/span><span class=\"m\">computer use<\/span><\/li><!-- [et_pb_line_break_holder] -->  <\/ol><!-- [et_pb_line_break_holder] --><\/section><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- RECOMENDA\u00c7\u00c3O --><!-- [et_pb_line_break_holder] --><section class=\"sec\"><!-- [et_pb_line_break_holder] -->  <div class=\"sec-head\"><span class=\"eyebrow\">06 \u00b7 Recomenda\u00e7\u00e3o<\/span><h2>Qual usar em cada frente<\/h2><\/div><!-- [et_pb_line_break_holder] -->  <div class=\"rec\"><!-- [et_pb_line_break_holder] -->    <div class=\"o-top\"><h3 class=\"o\">Claude Opus 5.5 como padr\u00e3o<\/h3><!-- [et_pb_line_break_holder] -->      <ul><li>Memoriais, propostas, atas, relat\u00f3rios e planilhas<\/li><li>Scripts Dynamo\/pyRevit e plugins para a API do Revit<\/li><li>Pesquisa t\u00e9cnica e normas<\/li><li>Sess\u00f5es longas e recorrentes (cache 5\u00d7 mais barato)<\/li><li>Use esfor\u00e7o <i>medium\/high<\/i>; reserve <i>max<\/i> para problemas dif\u00edceis<\/li><\/ul><\/div><!-- [et_pb_line_break_holder] -->    <div class=\"a-top\"><h3 class=\"a\">GPT-6 Astra para casos espec\u00edficos<\/h3><!-- [et_pb_line_break_holder] -->      <ul><li>Reconstruir geometria 3D a partir de imagens\/vistas (CAD param\u00e9trico)<\/li><li>C\u00e1lculos e an\u00e1lises cient\u00edficas (conforto, energia)<\/li><li>Recupera\u00e7\u00e3o em documentos muito longos<\/li><li>Localizar elementos em interfaces complexas<\/li><li>Vale o custo quando a tarefa \u00e9 curta e de alto valor<\/li><\/ul><\/div><!-- [et_pb_line_break_holder] -->  <\/div><!-- [et_pb_line_break_holder] --><\/section><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- FONTES --><!-- [et_pb_line_break_holder] --><section class=\"sec\"><!-- [et_pb_line_break_holder] -->  <div class=\"sec-head\"><span class=\"eyebrow\">Fontes e ressalvas<\/span><\/div><!-- [et_pb_line_break_holder] -->  <pee class=\"small muted\">N\u00fameros de lan\u00e7amento, autodeclarados pelos fabricantes, exceto onde indicado. Esfor\u00e7os diferentes entre os testes (Opus em geral em <i>max<\/i>\/<i>xhigh<\/i>, Astra em <i>high<\/i>\/<i>max<\/i>). Na medi\u00e7\u00e3o independente da Artificial Analysis os dois empatam no Terminal-Bench 4.0 (59,6%). Perfis do simulador s\u00e3o estimativas.<\/pee><!-- [et_pb_line_break_holder] -->  <ul class=\"sources\"><!-- [et_pb_line_break_holder] -->    <li><a href=\"https:\/\/openai.com\/pt-BR\/index\/gpt-6-astra\/\">OpenAI \u2014 Apresentando o GPT-6 Astra<\/a><\/li><!-- [et_pb_line_break_holder] -->    <li><a href=\"https:\/\/www.anthropic.com\/claude-opus-5-5\">Anthropic \u2014 Claude Opus 5.5<\/a><\/li><!-- [et_pb_line_break_holder] -->    <li><a href=\"https:\/\/kingy.ai\/blog\/gpt-6-astra-vs-claude-opus-5-5-reasoning-effort\/\">Kingy AI \u2014 esfor\u00e7o, benchmarks e custo por tarefa<\/a><\/li><!-- [et_pb_line_break_holder] -->    <li><a href=\"https:\/\/www.datastudios.org\/post\/claude-opus-5-5-vs-gpt-6-astra-complete-comparison-and-report-on-pricing-benchmarks-effort-levels\">DataStudios \u2014 contexto, tokens por tarefa<\/a><\/li><!-- [et_pb_line_break_holder] -->    <li><a href=\"https:\/\/www.digitalapplied.com\/blog\/claude-opus-5-5-vs-gpt-6-astra-comparison\">Digital Applied \u2014 benchmarks em comum<\/a><\/li><!-- [et_pb_line_break_holder] -->    <li><a href=\"https:\/\/benchcad.com\/leaderboard\">BenchCAD \u2014 leaderboard<\/a><\/li><!-- [et_pb_line_break_holder] -->    <li><a href=\"https:\/\/llm-stats.com\/models\/compare\/claude-opus-5-5-vs-gpt-6-astra\">LLM Stats \u2014 compara\u00e7\u00e3o<\/a><\/li><!-- [et_pb_line_break_holder] -->  <\/ul><!-- [et_pb_line_break_holder] --><\/section><!-- [et_pb_line_break_holder] --><footer class=\"brandfoot\"><img decoding=\"async\" class=\"logo\" style=\"height:40px\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAggAAADkCAYAAAD95dyzAABXGklEQVR42u2deXydVbX3f2vtZzgpbZlBFEEUK7RpGdomKYJYlQsiQtPSOCM4XId71XudwQG5OE9Xr9fpOoOIpm2SVqzghChDm7YMSZoiogjqi8hMh5xn2Gu9f5ykTdKkTdqkPcP6fj5HbJLznPPstfd6fnvttdemrvaGLwVMzxdBBqgooASIKISJCAABgEKZAFIQQ0s\/AwAioPSe\/vcqSKn0N0RQBaCqSgSBkhJBAQCqApBiB6JQJSKBQsEDPwWIoKogEEhVeeDDFcSEHd9FSZlApFDe\/v0AQum9qkoy6HurQlUVSiAB1BORgqCl+1NSJSaA+2+LQYOu298GOnA\/AFSRM2gzkT7ilZ5k6BNgSlTUK+hBBv6S5\/r3AFv+Pmtpb0aD3mvszMb2hu8y0yzvNR\/2KwWRJ0B1cP8csEmp\/3iAfKnPYnuf6f89g5RoUD9WkPTbQ\/t7NOvA31DpD0b5mv02JMGgPsUMQIl0e3cf+rPtthdSLfU57e+wpKo8aOxpqbupbr9OqQPToG\/AGNTnd4yHQf1rYGSCBKqiA+Os\/zOh\/WOF4LZ\/xMDYYYIqSFWVAVUqjZnSmFQaGNeiKgxSsBKEGCUDEZXur\/\/7KGjQOOy\/d2y3IYFIB+5dGQBp\/70SQKpK\/bevUBUMunbp3tD\/rVUhJf8iKL1ne7sRGKJOiYhUVQnCRF5Ut2We3nXqhWv\/uDf99s4VDS+aVuDLksR\/ZObidbfZSDYqjZ4VDS8CgIBAs8OA5+Ve+z3FaOzuaUa7\/B2N6e9H+BmP73N29V13\/R1oDL+j4U8F0KB\/D3gxJkJUcq8lAaVAkoqoYosLaSswdVtPe8O27ja6W4HbHejPQvnfNCxsmnPezY9b9xx4kFN9FLn5aSa7teXg5zeN5drD\/mqkf+mYrjT8\/TS+fs5jHUs0rvvZ1X3tvoVGG+u0m\/sddH3eMz+x+3uiMbX60O+w8293TDNK\/3VMSDKfhLk\/aG\/7LSsV2PGLlXVed3vj17zwl05ects\/bUQb5c76b84NpxwRvDmO+dNbi\/7zASky7xXe22R2gh9vQ90VgZlpOhGmEwhEABHNZsZSImBbHyecZ\/f3tDc8DGA9q\/uVV\/\/X5BHfO+8tG7LabEEV65vGvhGjAIFyV6C972yklOUKIjqwENKlaSav6G5vvGx289qfWEsb5UrXysY5TvE55+hfmAkMSgNrln3ngFR1VBHhmGLnaAYRzwDwfCa8qy+hZMqRYWd3e8PdxNymkm+c3bz+r7XSZkRkysDYp5o+y\/f+MgxyjoE0UySpIgzo2V5w7cb2hrMf29b3njNe021RQqNsuLW1qe7ASN8dEL3HBXRwkgkcl1xvAEI+jkiqMUmIApLrENHAjDgI6IyQ3Bm51zd75cc3rWy4IRf6vRdeXu1hy4H1ZsPYJ\/2NVB3U7+11POmQNdVSNAEUx+6Sw9wBp3Z3NPzn7EWdN1qLG\/ubTe1zZ1OgX2HiM71XZKkME7sKsWYq36hDmgmSVOC9gh0dHIbulaGjrwYkd9+9qul7PSsaLuxpXRpV6YzOBIKxb7tcMDlRK1WgmAgc4yQCfraxo\/FD96w+PrYWN\/YXXW0Nb4ALbggcn5lmAi87d322ZqqgKIOUnIyXklgIArqYHC1D+MDtd69qfPcfrpv37Kq6Yeudxj6G8smNWqWZgonropA\/nqeHdtzR3vgsa3VjX7J+xdyjNnY0fCsO+dsEOqqYjB4jYCWL41ZiZEFEMWDYIKBZUei+oBLc3NvR+I272ptmV37wAEO2IRrGpIsDJSIO9rrPOd6+u3UUoa9IUkEU8jkR4abutnmLrfWNfRI1WNHwsjoX3ByF7k25V8p3kwDOthO\/8sVCniu2FT0UOCqK+C0BsGbjysYfdrWfOreSFYLJA6OaKaaCwNExQeBae1c2fvaO9pMOslYxJoM1VzdM7+1o\/J8w4PbA0bOTVKBjePYzQJaDUCV4X4oqEOmUKODXOApv7W5v\/H41RBQMoxrJcoUIXBzx+0IUftPVNn+BtYoxkXQvb3r+tOn8mzjid6hqmOVjjwowYDGEakNKhZkAIKqL+fUOckt3W+MXe1bNPcZaxzAmWaiL0niiX6pAX1EQhXxK4NzPe1Y2\/Lu1ojERdLXNf6sLdXXoaG5fIpBxPu0ZFsitWgYyp5lpWl3M\/0nedXa1NVx6z+qG6dY6hjEMAnLx+y01Ns0EBBwYMn9l06rG729YvuBYM4qxJ\/S2zT+0d2XTd0PHXyPQ9JGq0Y5RIKjlild7REFK653s+MgD6twns4R+3bWi8axyd9YW2zL2NWG436MPyL0iCvn1ceB\/e2d74xKzijEeNi6f\/wJi+kUc8SWiIC976ki1dKCKURt4rwOhzHlhQD\/7w0+bPtVz45lTy1QfqNphVsY+RBWU+73f1OV017sYxvA90FcUhI6fVXD0496Ops\/d2tpUZxYydsWNN54ZdK2Y\/84gdj8LAndqX9GPKRFxF15YuXRgnVFLJJlAVMMgoA\/q432\/unt50\/PL0VmTLX8Z+1KUElRl792hMulE+NQsV3jRIIrovdMCWbV++dwTzErGSPS2zT\/08CeL18Sh+7IIpibZxOw9sOWF2p0toa8oiENu1FCv721vfLu1imGU3zgtJoJC7F5yQBj+8q62xpdaqxiD6V6xYD4cX18XcUvuFXu+pDCCQFBYoaRaJs0VAE11AX21u63pe+uvmXuYtYpRs0xADgKJ0kT71CQVMOPogNHR3dbwaSvTbABAV3vDW9jJLwNH8\/oSmfA1WYsgGNsTo+oKdPGUqcEvulY2zrFWMWpvug6EynvtY4cf1jRR9B\/6FE2pcx\/Is0OXbbrWyjTXKneuWHDEpo7G70cBf4OZDkyzyUkUYKilIBg7QplhwKc41V92LZvfsl+\/kO1iMPbHOJDy9oci\/TUTAn65TqEbuzqaXmJWqy02tjecEbLcGMX8eu8V3k9el7UIgjGEJBU45iPCmK\/u6Wj4T2sRo2bYz3UQxkMxFTjGs0LCyo0dTe9rbV3qzIDVTWvrUte9suH9ztHPwpBn9hUFOslylkGWgmAMJfcKFURx6L7Y3db44f3kq22bo7HvCSvnq2a5QoEpYUCfnRU+cO3trScfbgasTnpWPf+YE4MHflxw7jOiNC3N9s0JCUxEbF7YGI5oqbJbIeYre9obP2ItYlQ7qgoH9RU1TkWRZoI45qV1cXxd94p5882S1cWm6xac5pD\/ui7mC9NMILLvnthWatnYhcMEskxQiPi\/NrY3fsxaxKhqCCqeK\/LwumIicI4bwij4xV0rmt5kxqx8brzxzKC7rfHDmssNjun4Yir7PKRqOQjGbiMJSWmGcnlPW8Pl1iJG9SpiqDBX7Om2aSaA4qAowLc2djR9r+u62QebUSuTu1Y0HH3kk8VrDqjjK4lo6mTtUjCBYExIJCFJBYWC+9jG\/ZSTYBjG7sm9QlRRiOliSut+012GVVKN3YiD5fNfEIf86yjklm1FmdDCR+MWCGp1koxxiIQ45it7VzZ+wFrEMMp3rJbOXHEnBxFWb+poequ1SiUYDtTT0fBvQcA\/ZaYZxXT\/BrOk\/7AmEwjGmB1PmgmY6dNdKxrfaS1iGOVLlgugmE6Mr3e3N\/xfT2vTIdYq5cn6VXMP29jRcHXg+H+Z9vx45gmPIJBtJTPGoyq1lDkdOnyuq71hkbWIUTUQiEWqatnVS6k2f13s3uwi\/WV3W+OJZujyorfttAV1EtxQiN1r\/ASfpbBX4gBWSdHYE5EgADuOHNNXb29tmGktYlSJPiAVDavtvrZXSQ35VBB+09M+\/9Vm7XKwy+Xc3T7\/TRzKz6OAT+1LJr\/w0Z6IBBMIxrjJckEU0NPDkFq7VjYeOTkjyPqmsc\/cNaAgdq5qE7eLqSBw9LTAuWs2djR8\/uZvnzbN7L5\/uPeGBUdsXHn9taFz31LFgUlWfptnBACLqlgSgrEnJKmiLuZZLPgkJqGeBpFFtwxjIhkIYUehe89hR8h13W2n2JLDPuauZQ1npEW5oS7ilsk+S2HvogekAYHKTrowEwI3nufN3jWwDr4E7cmTjjD4fFdVhRdAfPVPgZNUEAZ0cffyhltnX9j5nQmd05k8MPYZBJCqIy\/Vfqfbty3H\/ALR+Bdd7Q3vmNPc2WF9YPLpaW\/8Vyb9kmOq60vKv6sFKLMlhiAg5LncnSquhfQ\/q6kkYlTEsSNWLf1cFQolASEHqQxMO7e\/r388EMNDSts2CFSqHslQEnglzR2Qq5DAAQp1gJKqC0jFDRzcyopMGfnQkUZMIAfIQQI9hoAjFOQAPQigo0JHR4UBbU\/sE0XZqsW9cTYKcBDyZ7qWL7hlzoW33T1xEQRzKMY+7MtQzT1ltXK\/xUQQBHR0zPyTnvbGLz\/8ROGjCy+5qWg9YeJZ3zr3wLrAfdI5ersqIcsr4zkQlN0XcoQ8x531izr\/q1I7w303nllIt259eprzsb7Ih6vqQmVtJKVnFmI+TNFf0ES0KmbJea6IIz408\/K1e1Yf\/9IZ596bTJD4CJwjuBpZBCvH\/sBMNSHUmAneE4PStJYeXHmuYEZUF\/P7Dj+4+Nz1rXPfNa9lwwP2SJ84ulc0nOQc\/V8UcUOSSgX5fKUAZXgWQ2kWXrkct\/CmIoA\/978AoBUAupbPPaEvdfNJ6SwFziLCkYWYKU0FUuFCIU0FdREvTNJD3g7gvydAHFBPBx7Jc\/1HnuvIvXTQstCov9urEMZuO+rYrqG7\/j2pkhIpAdOIcEA5ORDx+oQCxeHfd2R30p8zohPUvtjxWUqkA+00BgcyYhvv\/Cc7ruUYIMKT5Avbau0BJgL0JYI44kVhEM7a2L7gbbOab\/u1Pdr3no0rG97IxJ8IHB1ZTCpr9UoABFArlLSvmHPhhrsB3A3g6k0dp03L1Z\/rM32ZKprDkKaqlhR9JTIQFVHBFd3XNV4\/+7y1m\/bq2UzQ9avyJQceGuqjfyNEhYyCJ7IhffWQ46cIADx27zYOCjkBQF4M9PCDQvWHhfq3v+3dPR1byOjxYh8PXDc\/KNTgiYzyg0IFgCMOC\/Wfj2R0xCifdfTRwK5+P3BPUw4ruG2P9PlgWuHSOHIfKhdH4hwh9\/qmzRmtHvydi\/+MXRCX2vvAJNjeYf30J7RYqJO0GO6yE0eFHXYcsOlg+w20c1DIyUUHkU9DJTwM4HD44uM60P6DGdw3Djl+igy0d1TIqFDs4y3JVAaAA6cF6qKc0i1b5ClMH3Jy41GHF\/RZL7wpqVX\/lKSCKODnMkvHxvamT23MnvmZlpZlHsa4Wd8698BC6K4ImN8lUmrbSoNBGthpDPuHExfduhnATwD8ZON1C47Ncv96R\/zKQswneq8Vs0Y1GC+KQuym9RX14+u\/OfeV896yYa\/Wc+edv6GmZnPdbfP7ykmtEwAvsvW0lnV9NmJrgzQXMNPUOKJPzMRf5\/aumPvvM5dseNBaZuzc1THveTG7bznHZ6SZVPQyMsPOYtjvzDrvtvvrF3X+11bKXpBn8nYFNhZiHudOjjJxMJkgCrE4PixYYpYd7wO5vHYUKQBHJGaZ2kJEkWSCQkSLKQh+sbG94QxrlTGK\/PbGJU7dr4OAz6isfINRowhm1HJh3vkbHjnhgrVfT58KzigW\/TuyXP4SRwyuoCiPamn3AbNe2fWjSSqgVLUKofwexipsAqEGUS0VVnKO6wPH13W1Nb1VL7d482j8fdXcKb0dDZ8PHV0bBPSMSss3GF0gGGXHnNfc\/Hj94s7\/pVwXJEX5nKr2RWHlmCrLFXHsjqc6saOhx+WUVUywG+VEmgkEmB4F+PqmU5q+c8\/qhunWKkPp6WiY+bgProsj9x4RDSs1j8wEQoVR37LuH\/VL1r5fiM9SkVvjkCtmy1maKULnLupe0XCSWXKMAQQlLrvKWmRLkLXO9uqLEV2cJnTDHe3zT7ZWKdG9fP7ZAfOv4ogX9iWVvxttZ4Fg1erKntkXrLlF5Ilzklw+yUwIgvL32SKKMKDpIHq\/WXCMEQRo2W3vJRMIBnYc+BSF3FRwfENPe9Ora3usgnraGj\/IAbcT4ahK3KVgEYQq4sRFf9hcv2jth3wmS72XvxTi8jddkinCgM7vam+caxYc02y97IyqdtqrMYg0ExDoiCDAVd1tDZ\/vaT1zaq21Qe+KuUfdvbLp2jCiTxFRXZZX7xBhwLKUK4n6JZ3LEbgXppm\/uS4u7yUHEUUQ8FRSvNssNwZ9UCoDXlY4FvMPxhByrxCvrq7g3sNR8vM7VzYcVyv33r18\/tnqgt+HIb0iz0vVcKsZO+65Apl13m33b0v8eVsT\/2PnyrsUbpp6EGHxJosi7F5QleF3UrYIgjFCX92+5ECnB0o3dq2Y31zN97v+m3PDTSubLgsi1xEG9JxiIjVxmJwtMVQo81o2PJk+01+UefkME8BlKhJEgULMhVzwdrWqnbuJIGj5bXNUm0AYo1NMBQHTsUHAy3raGi5Vrb5nSs+qucfER7jlQUCfUNFCmtXOkGDz2BUsEuZtyOov6LzUC32idKhOeVqzP4HnlXcta3iWWW1Xg9GW+4zKI\/cKgFwUuU9uWtnwoztam55RLffW3b7gbNbghkLkzk\/S6tulsFufVI2Kr6ZmnQTtzZ55ee7ly2FQnssNqkAc0xQX6KvNYrtop3IUCLnZxdg9IoosF0SRe0UU6W+6VjSeVcn3s\/rLx8c9bY0fDJ12OKYTqnWXwm4FgsmDyqelZZl\/+Im6D6aZ\/3EhKs+DMEsVFqnlT7+ce6BZbDQvW4bfyVkOgjH2MV5MBIGjGVFEHT3tDVfe2tpUV2n30XPd\/Kcdd+yhy+OIPyWKQjXvUti9QLCgZlWw8JKbinVT\/Vv7ivktdWW4BTLLFVHk5hS3BM83a40C2Xq\/Uflkpez+KYXYffjAUNt6VjYdXynf\/c62+edpSr+PYj4vyaTqdynsXiBYIZSq4TlnbXhSHC1JUt0YlmExJQIgqm8yS402BbMkTqM6EOnf5RDxOQHhhq62pjPLeujdeGawsb3psshxRxjw8X1FmzmXBIJRVcy5YO1DIvrW3OsWV2YpqHmucI7O6P3Z\/BlmqRHdVNkJBGeFkoy9IEkFxPRsx7qqp63h38vxO\/aumHtU7xN9PwlD+gQUrpR0aZhAqFLqF6+92Xt8iLm8wkNeFIXQHSYZvcysNII8sAiCUYVkmYCJpochf2Vje+P3e9vmH1ou362rrelMdeGvC7FbnOW1t0th9wKBbIZQlZGEJWv\/J8n8T8qtJHMuAhW81nbPGEbt4EWRe0Uc8+vh+Jfd7QtO25\/fZ\/0354bdbY0fjgJcFwZ0Yl+NFD6yCIKxnTjk9yap3Be48pmY5l7BRCf0rmycbxYyjNphYJdDGPApodOfd61ovGR\/fI+7VjQcPeVpwTV1MV+piqlpZvkGJhBqkOe9fM3fc48rXRkJBBGgruCmQPUMs1AFOHUrtWxMMEkqUMV05+i7Gzsav3Fzx2nT9tVnd7c3vjAO+YYo4KXFVOBtTWF3AsF2MVQzd\/u1VyWpXBdH5aMF01ygwIV6uQnUwVAZbnPMc6uUZEw8pQezIo74LQdDVnetbJwzqUJXQV1tDZcy4afMNLOYWtRgbALBBFRV09ICL4wPZblsLpdNDSqAKk64++TGY8xCQySCjUajZtix5IDTQ8Vve9oaXjUZn3PXioaje1c1\/qQudp9kIltSGJ9AMKdU7cy5YG2X9\/hRFJbHhN2LIgp5uiidY9YZrA\/KL94ZsrMI4zhxTHbE1ThIMwUxHRyEdHV3R8On16+aO2Wirt29vGFh4PD7KOCliS0p7IFAsCBvbRg6xBf6Ur+Fy8TezhEJ6SkK29o3eDiWG5nYpvA9EMBkvXp85F4hHq4uch+o88F1XcvnPXuvohOtS11ve9N\/hBH\/NAz4WYktKeyZRyIFmweofma9fO0fiXBVuUQRslKY75w720+ysxm2ezV7rBi1i\/QvOcQRLwyj4FfdKxsX78l1en8+96iN4QM\/CkL6b1EcUEvHM0\/8lMW0bs0QAV8tJrK1HKIIogADT4s1OsIs008ZJikGQWB2MfYpxVRAwHEBobWnvfEjq798fDzW93a3z30hpeEvChG3pHaWwt7OWIhhId6aYcaizl4FlpdLFIEdRQL3L2aZ7fqg7LyZqNgipLHPyb1CBS4K+b+e85zDWu9Z0XD0bsVB2\/y3hS64jhn1tkthAuSBgmzw19pDiOnHeSY50X7vfIgCgpLMMasYhrGzOC0tRYaOz88d\/+bOFQ0vGunv1rfOPXDTqgXfDgL3NQUdUMvHM0\/4JA6wfNuaInnqN5nHPeVQXVEUIKVj1n9zbmiGAdRqkhjGsDEBbCt6BAGeWwipY+Oqhve2ti51A7\/vWtY4pxAG18cRvVFE4S2nduImkwRl2IacmqK+pTeFYnk5VFfMc4VC6w84Mj7cLFOe2GmORjmQZgpRmhY597mZwf0\/XN8695iNbY2vjWO6Pg65aVuft7MUJl4iKCupTVtqTxn+0ntN9\/cygxeFc\/yMXLztZDAMY5eIKJJUEIb8yjh0dwhwlQBHJVb4aNKwHIQa5KmcNqSZbgyD\/S8NCQCpHmVWseFoGGMhyxWO+RBmkC0pTLZHEgsh1hqntazpI9YN5bDDlRlQpwvMKijLOgh2EoNRrtEEW1LYFwKBbYWhJgeYp9ZySD9hJpDQbLMIyrIOglolRcOozWcElJiULIRQg7gIf8m8bub9nIhApf+ZbhYpzzoIcOYeDKMm\/ZHVQahdQnfwA+Jxx\/4ulFc69BXTdNDWpZpFy3CRwVuE0TBqFRMINcqMc69PiOQvvJ\/PgBZRQOiIO\/DgIbVuk3LMxSY7zdEwajOCQFATCDX9ROLefD9XHfMCgPWwKQV\/aM2rdSq\/LAQ7iaFq+pZhjL\/fKKwOQg3PWG\/Pc9X9mYYgoggdH5znclit20PVlvyMSRLiqlsCRyBz9mVH4AhBGQbq+s9iIG8mqk2iQO8F7f+dbIEjUlErt1yGpZa92GFNlU6p3glfmeb+O44JbOGEsqEQM\/JcunOvG4Kg\/OzCBLUyVDVKnmifQDebuzCM6oWJoJAnZzeve7N4\/TcCtpbLia61ChEQh4wklRvCnF8KQm85RhGYbJdjzSJZnJDiYSqHGYWTqWYRw5gkRw+OiKAzm9d+TTJ\/bpr526fUOVty2A8EjuCYkOb+yjB69ILntaz5uyoKZShiLEmxlikcPHUbQH9z+7kXKADS4BCziGFM0hgT2b6NeNaF634nYfSSYiLfYyYJbKPKPiOOGLnXf+a5v3jWos6Pzjj33qRsJ5AAWC1HsWaZce71CaAPURlMI1T1ALNIOU53rAmqJIQwhDnn3fz4zAvWvCH3crGoPmFLDpM+G0ddzPC53E65vKh+8boflH+XIWWQZazUeM\/dUg5hRgLFNT\/LK8OzGMjD\/ENVRBBoxFyz2c2dV\/tcX5hlcmMhZltymIwHLZeWFIqJv2Zbkpwza+m6jRXz3VVtmaHGJxZby8EnCEmh1m1BRJYwbEzWQ2rUXLPZSzrvmvJE4dxi4r8Mgi\/HbPpKJQgIBC3m3r9zVnPn605tufPhCns+mECo8VlrVhZzRCULZpdn\/7Ak5mqwo9+1nz\/ukpuK9c2d\/6G5vhLAg4XYHgt7SyFm+Fzv9bk01zev+wpQeWOJCbaNobaR8oghk+XClCOO2aIaVWHIsY2v+iWdy9Mke1GW6eq6mMGmE8bvygiYUmCkmd6oWfDi+iXrrq\/Ue2GQrTHW9MyCOCyHMwQJFl4nKr+aJLkNkWp5aI3Zz8+5cMPd\/3gsXtKX+I8DKIa25DB2HVaqVilbt+X\/K8lT59e33PJAJd9PYNODmncdU8shgkSEzExRhqc5GlWByPgidAsvuakI4CMbO5pu8oJvxhE\/O0ntabErwoDgPZ7MPf3bnCXrrqmK6aNFkGqXyy8Hk8oh5RBBUEXRLGIYkwPv4W61WYvW\/CoRfXGSSUccsR36NKKuL21hTHPdoJmePWfJmmuqpt\/0359RgzSfNqdOgaNFymGNQbeYRQxjkgQ41O3pe09pXvuX4tF5S5L5jzlHqS057MAxIQoZxcz\/eHNG\/zJr6dq1VSR9lAHLUq5VpvjpUwh4ppRB5JAVT5kXt+OejUnqWnt5Uui8eRuy+kWdV+S5Nue53GtlmgcOwdJtfVn+3o1J52tPa1nzWLXdo6Uo1zB9fWmsoENlP68xEABV3WxOvPyOe\/ZspzkaO5jVvHZ1KFiYZnKtY4Kr0TLNUcjwXh9Oclkyp3ndF1paUJWnIjPbPueahSMcCtKymCSqUmIWKcOtnraNoSog6IQ9wGYs6fzbCS9f8+rcy1tV9cmohpYciEr1DbJMbkwyWXhyBW9hHNMzwrY51rDTyNxMJnLWEoZR5ZOBSVi8qm\/u\/Gaa4uws13V1NVCm2TkCMyFJ5X+zzX0vP6WCSibvuUCwJMXaFQik86KAsL93MWS5KsCpWaQMnaIVSjJ2wclL167ty\/KzktR\/nQhVezJkHDEAfSz38qZZi9a+4+SLurbWhLC0Ll67KOTpvJ\/3LTETcq+bWf0TNW+PMqyC4K3CpbEb5rVseHLmos63q+jFRPRYXEUnQxKVxEGaS08mcvbs5s7v1JJtTSDUKHfecNYBKnh27vdv+KCkT\/QRYnnMrFKGeBMIxtiY1dx5dTHVs7JcbqkrVP6Sg2NCGBD6Uv\/1PsoXzlm0bn2t2dQEQo0S9D15rAtozn4XCEwg4MGHDpn6cM0bpRxn686OajHGzklL1tyebw1fvq3Pf58AdRVaWSkMCKq6NU31PXOaO98+7\/wNj9SiPVmsDkJNIjlmBI4K+zv\/gAkA4amFC2+yfHmLIBiThGLfCb05r7n58dmLOy9R1dcD+nhp\/b5yKMQMEfzRK86rX7z2i7Xcb5hUxTxADU5WHS0qh\/zUfq+12SxSplilJGMPmdXceXWa00uSVG6qK7iyL9NMBEQhIUnlF0XRf5ndvPa3tW5DhloIsda4Z\/XxMaBzyuG7iChUZJ1ZBSjHsUi2Dbo6JgT7qcbGSUvW3H6wy89NUv85YkrKtUxzEBAck6a5XP7PxwsXnNK89i+13mcESkH5FXc1Jpu0eOj8KKRZWb7\/d7CpAmDcalYpU81ihdSMveQZ52\/YBuD9vSsabvKOvhpHfGw5nQwZR4w0k7+Kx\/tnL+n8sVlsIHpQWgI2B1Bz0wk62zmK9nf+ARGQe+mD6kNmFJRnyrBFGI0JYuaSzp+lqS7MUr8qjvb\/Lgei0imM3uutyPHi2UvWmjgY2kLKaoWSaoqe1plTAX2V9\/vf7weOIEp3+C3ZP8wyANQSAo3q5uSWzvsk27I0yeSTAHR\/FVZy\/dnRSapf7\/N9L5t14do\/mnVG8NHWBLWFuAPOihwdl5eJQEhzvbtWqpJVIs4iCMYEU9\/SmwL4UFdb010E\/WIh4mcU9+GSQxQSVHWzz\/HOWc1rv28WGR2rg1BrBid6nXPEWgZuP8sVCvSaVQxjctmX2xzHypzFa1qLORYmmb85jnif7HKoix2yXDalOc6vN3EwBoGgtsRQK\/SumFdPRGdn+f73FURAnmvGpD83yxjG5FKuB2qceuHaPzKCc5NEPuscfDBJuxyYCYWIkWb5avL+xbaFcawCwWIINYMnvjiOeIrI\/hcITAQQHso0+X9mmX7RBJLy6zOWF1EVjr6Md6uduOjWzfWL134gSen14vXvE11YKQgIIpoVU\/9xn2xpnrlkw4PWI8YqEIya4L7r5j8NoNekeXn4iSgkEPTnJy+660mzznYvXn5O3CopGvuIOUvWXOMFZ6WZ3lyYoF0OUciA4h8QXFjf3PmR\/vwHY8wCwQ5zrQmeKurbCzE\/rRyiBwAgCgjQS1aHwzD2xXirCKE3e\/HaTYzHz00y+RLtxS4HAlBXcMhyf3OWyTn1S9ausl6wJwLBHHTV84flpz87cO5teV4eapCZUEz85kjpBrNOmWOHNVWHGVE5kaATF\/1h86xFa\/9TFG9gxiPjXXJwTIgiRjH1VzGCc2cv6bzLesCeCwQLIVY5KacfjmM+zJdJtIgZYKY\/zbjrnD+Ydcr8wWLbHKuDCvTz9c1rv7+1z78wz\/SXhZjBY9AJYUjwolv70vy9M89fe\/GJi261c172SiBYcZaqZmNHY2MUckualc9aUhQQIFhOV1xhC1yDUbGHsTE5XUupIvPNTlm6buM\/Ho\/P7yv6K1WR7uoshzhiiNe\/i9LL5jSv+4ItX05IBMFmCFXrFG48MxDRjzumA6RcogcEpJn0CeEWs9Awe5XhXnXbxVAdSAVvZ194yU3F2Ys7PwpFsyr+OnzJgQilLYyp\/Cb3OGvO4jU3mcUnSiDYHK5q6X6i+IY4ci8pltHBKM4Rcq9\/SZ6Zm0DYaTCWIbaLYfx9XMmOuJoEZjWvXa3CZ6WpXD+l4EBU8ieOCVmqXwzjg8+dvXjtJmupCY0gWFeuRv5w3bxnB0QfkTKLWjtHUMXyefM2ZGalYREEhbNWqAaFYE0wWZy46NY\/TDmo0Jyk8mnHyMXLY0mql5zYvOY9M869PrEWmlgCO82x+li\/fm6YPcBfKRTo6GJSPtEDApBlkjP7n5mVRhAI0LILIlBoSczjxYta6vckctzCm4oALu3paLzNC\/550oVr11irTJJAsNMcq4\/4Af5AFPG55SQOACAMGUkq66c+OdW2HY30MCYuO4EgylZMrQqoRiPWL7LaBhZBMMZFV1vDUufoiiwvP7MSASBtP+6Sm4pmqREiCGW4cp1bDkJVYKlmxh4JS4sgVA+3r2g4KXD030zlcVrjkI7GQJbJ4yFjmVlq9Pl62c0grFBSVeBYvbWCMW6\/bYWSqoPua+c9c0pAV4cBPyP35efTo4DhVZefcEHnX8xaFfRgUW8CoQpQJQsiGOOfIJCCzQNUeOSg9fTDw0LeFgY0uy8pPz9ABPSlkkYBvgpb0tqFFy+\/2bqyRRCqQyAgt1Yw9iSCYElIFcydK+YcUYiz68KA5pWjOACAKGIAev2MvmN7zGK7ElLlN8tTsZlnVfQt2BKDsQcRBGuCyqVrZeORLPrTKOT55SoOiADxkgN0FbUsMye1mxCCtYExOT3LhJ6xJxEEoyLZ1N74rJCwqhC7+cWkfMd+EDDSDN31z8xtS9Lu5YEJBGNSEDHxaZhAqAl62poalGl1GHBDOYsDACBAwfolssqJhrHfcCY+jT0UCJaEVEF0dzRcRKw\/CxydWO7iIAwISep7EvbLzXJjmeZZExiTpdQtgmCMnwB2rEhFcN\/3ziz0Hdz3AWb+GACU0\/HNo6pPhhDzZ+ad37nNLDgGH16O56JYoaRqiSFYExh7IBCMsuf2FQ0npWHypYD5hVmu0AqQdFGprPKG6Rm1mQUNwzBMIBgTyK2tTXUHhvJvIPqAc3RYOR3bvJuZMLwXgdBHj2lZ02eWrOSJp9VBMIxahZXUYohlSHf7\/LMPDPWmMHKfY64ccQAAccjIvV7b65\/5S7Pk2FG1RDJjsjqXmNAz9iCCYN2mrLirfe7sgIJLmbDUOQ7StLIy1xwTiok8wSFd0bLY6h6Mh3LMQXBqEYRqwPJfjT0TCEZZ0Ns2f4YSv4MIF0cRT01Sgc8qa1gTAOcIuZdP1r+8849m1XFO8mwrmmEYJhAMAFAF3\/Oz0+akuX+bF31VIeJpWS4o9+2LoxGFjCST31G+5ctm3T1SCGVXl8QK+FeJo7dNDMYeCQSbs+xz7lyx4IiA5dzeDlzI7F9YF\/EBaSZI0soNBDoGvMjmROWDc1t6U7OyYZQPImye3tgDgWArjPuEu1ctOEFITtYMrwbLKVHIR6sCeQVHDIZEDyJG3zb53Nwl624za+8ZVIZJihZirA5U1armGtUx\/iv95LF7Vp98eNIXPd2FONJ7ejkUJ+VeTpxScIflpBDRio4WDKcQMfqKcmM+te+LNqQMoywVgkUQjD0QCGXWbXxpN85xvR3zm70yq4oDACIm7z2YSZlJVElVxZHyTspYWLhUe9yBRAkOUCX16tUpqSqpkogDqQyccqZelUkDAF6ZmZVFiCVXdg4AKZEyg5SEQEwk6sUBNA3AoQQ6CozpqjotKeK5IJoRMFPApboAea7YVqy+pH7nCGkujxHLO04+u2urDSnDMIwqEQikVFb7mLJMQUSNCmpjAjAob4sHZdpQ\/+9G0jdu+xlUur3CKEHBxBh4g\/b\/zfarkxt4B5gAKIEJcCFtj2uU\/pi2Fy0l50BU+ntmAhGgCniv8KIVUQ55b6BS06iIXlbfvG6jDae9bU8ROz\/NmJS+5SzbzNiTCEIZooox92bd6z8Yx9t1x38I2H6MhZ+ID6pAChFjW+Kvql\/U+X82lCag3zOXXYjJdjEYhgmE8nOW5e7Ma7zjFCJGXyIbkm3+vWV5yFAlzvJUyy7kpOLNtoZRo7Ba9ooxTqKQkebyB4VvnveaDY9Yi0yU6Cy\/oUjszD0YRq0KBFgpVWMcBI6Q5\/KIz+iNs5vX\/9VaZGKHY7nh2PyDYdSsv4eFh42xPr4IEFWfqbzrpAvX3WItUgM2V7GsySpA1fy8sWc+3zqOsVuIgDBk5Jl84KTmdT+yFpkML15+meZerMCOYdSsQADUDvoydiMOCFHIKKb+Q3MuXPcFa5HJaugyFOtWSrFKxKctFRl7JBAMYxcdhIAoJPQV5aOzmzs\/aS0ymZhWNwyjnOYHZId4GKOIAyaEAaEv8ZfNWdL5KWsR0+tGZWI5CMYeCQQRMcdk7IRzBCg0Tf375yzu\/Ly1yL7w4lbtzpg0sW8CwdijSaJ1HGMIYUAA8ESeyZtnNZs42IcKwQSCMUl4awJj\/BEEHXzYgVHzFGJGMfH3i9Cb51zY+UtrkX0ZQFBXdl\/Kai1Xi\/a0iaAxfoEAWFjTKG1jLMSMNNUNYH7lnOY191qr7GMbwLmy9BBGxWPpr8aewCDb51zznYCB0BGSRH+ydUt2Tv0FJg72yyRPy3AsWgShShy9YYxXVCpxuR33bOzjCWJAICDPcvnQQwfGr7WzFfZnCKEM48AWQaiSvmW71YzxRw8CJVWyVYba8xdUOpExTaVHFB+Y1dy52lplv9uk\/AaiRRCqYzYolgBr7IFIsAhC7RGWogbal8h3fUpnzmpea+KgTIZjuX0jZbLl66oIINhSsjF+LIBYY1GDOGJkmTyQC793zuLbllmrGLsUCJb9Xi1j3yIIxvinLGIdpyaIQgYRfJ7r912ozzdxUI4PYym72bqKN4FQBYjadnZjDyIIbCU4qxrHhCAgeK\/rvZcrZi5ed521SrkqBJutG5PkB6wIl7EnAgEWQahOh+AIoSNkuTyYpfrldErf\/558ftdWaxnDqMEIgu10NPZIIBjVZdCAEDhCmsnfk0x+GDj+yvMuWPN3a5kKgMtvmhcE5iIMo3YFgi0xVDxEQOAIriQM\/uxVvytJfnV9y4YHrHWMvepbtj3OMGpXIChBzANUrjAIA4IqkOV6T5bo\/0mIH848f+1D1jrGRCBOnLVCFfgKkJ3WZIxv7EPJIggViHOEMCCkmRTzXNYr0TeRbe6ob+ndYq1TwahyuQ1GyckEQlUoBLV6FsZ4J6AUEFkhlEogCAgBE9Jc4XO9L8+lTRGs4PyJDfUtvam1UDUMSC6\/RDJnOysMo0ZFJVkGUrlGCbiUU0AEiCryTHszlU5y\/JMgyNefcL6dmVB1AQRRX26Hq7IXm0AYRi0itouhPJwwAcwlMeCYAAKKiTyqwL2i+nsl6nAqd89evO5Ra61qVoWSUpntRmNiW7uuBvGpsKUiY9wECnWwUgiTBvU3LfX\/g\/p\/xkxwDlAF+oqSqOpDqvhnSvIrEroNTu+decHaXmvBGhKKSklZDUUFBGoCoRr6ljWBMe7xTxqAMDWOGc7pmH3TrhYlaTd\/X\/ZShHZzg6PdiO742cCvvAC5V+ReBaoZAX0g5Kr6CIj+QBk2CPCQkvxJw\/j2Oefd\/Lj1yloejxTUxTyk\/+kYx9iE9f3BAQ0ibO3LbfYwbl1FrhAx9mZxhsbob8dCHDL6Uh+aZcqaqBDzEIPrxA7ncfWnOGIUi+ICUvy82Cf3e9FcBMIMLwowQUVBA\/8lKCmVDhVXAqG\/8zMDoiAGIAolgpbmxbT9+2npOgSCan\/BeSYoBCAiUdXtZ0IMlH4mIhVg+xFkUrqQgoiG3a0SqaqCBn435D39f8P915cdinrgOqX0Xi19byUo7dBK2x\/5\/Z8xyJlDeXBpXNJStqdSxhBRkCqwjYgecYp\/KOHhXPS+iIMnD8gkf+bGNQldAVvfNXb0KZU7+oryjW2JT1EaUgFEHRGRUqn3calfCbTUbQfGCXb0YYCI0N+Hqb\/2khBoe1l13jEQFKSAChFkeKlnZrgU9FezzDhn64T7t\/b5nySZSn\/7MwQBiAhEChWFlvzhIHE4YEMQ1Csh7590KBSsgGMiUoBIlZVIB35PA1Ee3e6sSLXfDRIgXkOCdptlyppfbO3Lt6SpZgRlBbn+kDORKmnJBRBoxzCGADT8ANjSU0qVSaCqpCSqSsQDXat0zR2PLSUpORvd3gdLfS5kRz1mFsMwjEkXf6DLLwdraeYw6mssAaIx\/i2V\/D+otdXyDwzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAyjbKG9efP69XND\/kvwrJARUK6ECFAhFfbyh\/S4e1talnlr4pG5a0XD0QwcqsDRzHw0Q+oE7OF1M0gfkIAeYcqfqD9\/wwPWWsADrU11jwX+mYFzggTQgFWjVClVqr9rw310BaSW2qNrZeORPpXDwpCUSIlyJRVScSwqrABALES5UkZKMStlHFAEoJh7CWPZjGlTH6tfeNOWWutLra1L3UlTHjw2y9NCmu74eRiQbneMrJR74TAgRQpouON3aTr0b3e8nzXLhYhLtgAAB\/EPHTzlzwsX3pRXcpv1tM6MUkw9fuC+2QsjAvLEcVbIHpx3\/oZH9sX3uKO98VnOc4FYaKit3CMnL7ntnxXZtquef4wvJnVhQJqxUoQIAOCRu8m6rwdam+oeR35MwE6IhbJcaeDzA+fkn1ML9y1ceFMe7M2H1D3gXhfG\/D9J5mOEIChAUI3Y5TODv74VwA\/s0TaoI1w3\/2mUcwtAZ6pKY+D4GaABlcZwABAQVEs\/yXP30KaOxjW50q2UuR\/Xt9xSs2LhKYe31kXu41kqXgOASIVSp0FAtOmUhtcAnT+rKWUv+O9C5F7pRVWVGA4QggLqwSJEAFQZIShQZiVQyKX3Bo4VOf1Tn+i7v7ej8V7x+mewrsinJH88+eyurdXedifV\/fVpaaq3MNPhYTjoOa+qRKRQJShxwI5VAHVQUiigCgBhCJCW\/v\/Au4lIcy8AlKAggioR4JWeOuLR9FQA91dym2kQPydi6oJCoErq2EGAKAa5PLir67rTF8457+bHJ9V\/rmh4ERN+4p1Mp9KjhhRAwMxQ\/yMAr6vItvXZh+M4uCQTyZwS5eoJAJgpCMj33rliwUsmWiQ8GfhPRM69LRfNCeQ4IBKQFBwFWa63PaOv7mwAOe\/pB9z4vWMLAnozMQ5gpoCIHBE5YgpCxwWQvuXG751ZMFkA3L688bnd7Q1fpow2OkdfjkNa7JifkXtFniuyYa\/cl17s+Mgw5AuigD6DIOvpbmv8xl3tTbNrTljdOHOqkrwdwBRmmuaYpjHhQHY4KAz4wNzTO2uuUynqgoCICFzSAgAziJkCx4iYEA2MS2aQAhBReFEwg5zjI6OAG6KIXx1G\/GFm7oz66m7c2Nbwvt4VZxxVzU23LdeQgClMcEy048UUECGk0n8ZKIl3ZpTaud\/Hcb+fI6aA+19ECIkQbveFTAE7DgiI2QlVepuxC9gxue3twyACSFRRiPkkzrJ\/n8zPv7H1zKlK9Jkw4MOYEA18D2YKgoAYirBS2zYHfRLA\/4sCriOiAjNiZsQAXKHgZgesH5vQ6OOKhpcFjt8OUIGJphJRHTMVgoCmiKDoSN4749zrk9K0dQ859OCjTmdGQ5IJtF9bD7yKqSAMqOFpBxWbalkY3PPl4+PujsYPRw631cXuncR8SJ4riqnAi+72\/SKlv81yATuaVhfzWxzklu72xk+ub517YM08C5+cujgM6Dl5rhDFjpcASSYIQ5y2sWPeKTUVQYD67WNu+wR49NeQ9lTAS0mMFhNBlitUETpH8+PYfVY4uaO7reG\/1rbNP7Qa2048K6Cyq\/YaaDPdTbvu9kXQ0udVwTgcfm\/9P0tzBRPe3btiXv1kffZhrvjeQszzRnreqAJEVLFLjKc0r\/1LlstHqL\/DDb6vJBUw65u6Oua9ZCI+q7dt\/qHM+DwzxV50yGc5R8i9fu7ERevWbxeGe\/Ihra1LHau8OQ6ZMUrXZyaXq76pVsVBd9tpz\/HPOnRFXcRXOkeHFhOByJ77CZGS8GLmaVNivrQuDn51V3vDGdXejuu\/OTckxWudY1IdWURFAU9V5dfXVgCBJvSho4qSYEgFzvGRUwruI9PY\/aZnRcOLqq3t4ohLXYn2iaE0iDWv5r4ooohCPkjYfWr9N+dO+Ey+e8W8+YHD+7JMdtF\/taKjNLOXdF6d5PLDKOKdxmXgOAzIfXZTx2nT9nZe4Yk+VYjdCVmuw8cEiqn8pv6Y\/DNDnuN78inPC\/88k5nOT3ZhsDxXMNPLupYvOKHWxMHGjsZG5\/xvg4BfVkwE3o\/syx0TAkeIQkIcMeKIEYWlnzlHIBp5MPYlgsjxvLqQf9bd3vDGam7LA47kk4joReku+lppBkwtt7eefngNRRBGdIhEpZnAaK\/A7ehfjkfuY96X+lgY0Jwgouu62preWlVtlwoBNOLkhpl22X7jfRHIbamSCMKuSDJBGNC5hUN56URe957V58Tsgs+6gKfsan6lRFzhTaiU+Q8VU\/9g4IYOyjQTFCI+JfP5B\/bqubRs\/osCRxcl6VBf6hwhzeVRIn0HzduQ7bVAIHXvjAIu6C4M5kVRiN1BxHpxLYmDrvbGuY5pVeD46OGGGHDgUcgIQ4IXfSTL5Y9JJtcnif92kvqvJ5lcn+d6n3j9BwAUYh7RiSeZwAum1UXu290dTRdVa3vm4DdFIbtd9bXcK+KIjwrD9PyaiSDQzhEEIkAUqffy99Feude\/5V7\/5r383Ys87lxJnI7Wx1SpLgzw1Z4VjR+olrYr5QSMLLBE9NFdtd94XnkufwfhgQPrqjuCMDDTBYgpcJ\/oWtl45ERdN+177E2FmF64qwlCf6Sm0gUC6ls2PKCKDxNhp\/FYTAVhyO\/oam+cuyfXXr9q7hQE\/OnAcTzYlxKAgAk+w6fqF3X2Dn\/fuHcx9Fw3\/2mU0b\/kfveiOM0EIL2o65rTPzPnNZOb4VoObOg45ems+IFzdMRI4iAICN4rslx\/qSKrwpDbs77ND89a2psRleYzqqCNy2aGfsohB1CWnpdlcpaILi1EXEiyoW3OBKRe\/+K4sjOkR+1rq+YeEwDn5\/mYJ2BvXv\/NuVfNe8tQFVydHnnnH0UBoy\/Nb48Lj79wTOKrePCxaeZf5BydKYpFcciFdFgf817BTByEdGVP+\/yH6pvXfb8qIzIEqGhO5N4Uxg\/\/fGKuejzSLQ\/o8Wf3prUgWrNcUBe7Z21L8v8AcOneXm\/j8gXHKvvLsmz3459UuRracM7izu92tzecXYhcy+BniAhQiGi693Klti59OY2zhECUBe+JCzyvOOy5FEeMvlR+Nrt77X+P9L5xCwROaWFU4GOKiewUmutX4IPVOMKAnuanpIsBfKfKFTT1dESfjyOeNZI4KESMLJMeVnzMZ0\/9tL5lZKdREgq9KYAUwNUArt7Ucdr\/JJn\/VBzxS9L+JJ1CzCgWpdsRvebE5jXdVem0vWsJY3dUXzJ0LASO+pMUdZBzUjiiUw84Ing+gN9WuzMebYkBIJlx7r3JGC9zT\/\/rG10rG+ckqX4kCulCLzuPY2IKmfkrXW3z\/zBn8brbqrNRSUX9tnG03264F7VGkgkc89vuWtG07KQla27fm2sJ+8vrIvf0Yrr7\/MMqWGLYIcoZH0xSOSMI+Kg8lyFRhDhyL+3RB\/4dwJfHLLQ65p3imP5zeN6Bc4Qklcclyy8drY7MuBpVFSxE7xyebBeFBPHyWy96bRzykLATA6SEN\/S0zoyqeWBsbJ9\/URTQq0YKhU2pc0hy+ZmKvPDE5rUrRhMHo3HiolvXR\/HB56WpfCpwhAPqHLJMfqdwzdUqDtavmjtFgNcPj1TFESHz+g0R+WUU0qC+pogiDjPFa1VBqHZoYu9xzgVru5bftfYVWe7fyaRb3LB1UC+K0PFUIv7CfTdW9vblJFMCDV2j6d8qKmBUf\/RpEiklDdOBRPJJ3Ys00K72xnMDRxftdmlhhweoGoFw8gWd96ng3aW6HEN\/l3sFMy67s23+jDG1Ss\/SSMV9yTk6ePjOucARvNePntSyYdRnyLgatWdlw5mOcWo2zGkTEZT4iwp8Ic37i7QMzOy8gkBNiKeeXq2D4v7rTj9YQR8d2PYzmLqYkaTysz7KW2YuXvfonn7GjHOvT2Y1r70szeUDxURWE+VLZi++9U\/V2qYH+PDFccgzBytoIiDLtQ+K\/yHR64Yv1KWZgIClPTeccXS1O+KdXcfec8UVkPrmdV\/JvL5dFRnzzrPDuogXbH08eUVFN14M0AgikkgFxDWxHDCZFFNBHPLZvW0Nl+yROLjm9IMZ+AIzubFu\/CKtnggCACSP5CuyXFfEw3Y1eK+IQz6Cib+gevlu77nnj\/f\/RxzxC4ZHtQsRo5j6nz+6ue7bu3r\/+BpV6I1hyJEO+qwwYCSJvzsg\/u3d2TF3etHfhcHQKEIcMqunN1brzG5zmr4mDt2zh6+VhwGhmEq3L+Kieedv2DYRnzW7ufOzP749vuCEfVTadH+RQ\/8DoCHp32HAEK+3z168dhO7cHkxkScHlrZKsxcgjng6FZNLUO1MYlLW7ObOq73o\/w4ex4PHM0jfWtFF0JL+eoc7NSkAyat+x8FE43hnty4KKNPH17c+\/5jxXo\/r0nfEEZ8wUkh8pM\/qVwhVZbd5b9mQhU7fn6T+n8N3NRRTQSGk8zZ2XL\/LMgJdqxacwIQP5MNUlmNCkskTXtz7F15yU3FCBMIfVzYdT4yXDU8YcQ4A49oTF926uf\/shatVdUjpsDRTEOG8u1Y1PKvaBsffV82dooRXYOTtYrl6en99y5rHJnamd1NVZ0X3ts1fEDCaBkcPgFJSJoCvAsAJF9z6oGP8OA5ppweYCl51X\/uZB5nr3nOmHlS4LEn92igc6iLSXOAczX3awdvqq+2eSz0pNOOPtb0IUNUk9\/q34fEs7xWFiI+KXPbh8VxzU3vTbGJ693Bx0B8O\/7MX+ROPIBKYqerO\/Tnhgs77JMdlOkJKsheAGR++a0XDiNHSG288MyDvvxiH7pDh2+yZAVX56MlLbuvZrVgbs+jO5eK6iA8avI7BDKSZPEy5237mQsjBsizTv\/Mg1SOqiCOezp6qrnDSoxoc74ifP3ytLI4YXnDdrK6zf2GuZJyTY6I3h+HQfc\/90Zh7GPqLfuek5LEizSQd7C+yXBBH7oQtVDynur3z5B5OddzCm4qq9FlA08HOXxUIHYW5oCrbl3PvbASOVSAQAKQKfrcq7hs+001KFXXfsLGt8aVjuV5P68zIQ74Qh3zg4Dy3\/m1\/wqBPKfBHN8JTS6r0qDaSLVdnuVxXGHGpwT2TaOQyzIc8XrwkdHzOTrsWQkaa+d8UnYxp08CYBELXNacfrESvGx49iEoh35WzLrxt+za7ExfdulkJP4zDnW\/IMV61tnX+06rKoXicGwRD4wdEQJ5LDuBquuIKMVcyrujBDFFcmO60pZMAUOvgPI4TF5\/z6zxH9\/CEOhAA0bfdeOOZQbW2E0EnvV89cnBhVTHVe4c7flGAiF9WdcJUiUFKNgrH3GAgUIHEP6LQy5lJhotJ59gJ9BM9rTOn7vZ6bvrSMOQXD18vj0JGkskvgvigqwl00ChqpSr9bH1Lbyo53pVm+tDOSw0edVFw0cb2hkWDf37nyobjQtYrMSxJ1DEh9bLZafCusS55j8mB8gHZooDpmMGhCiIgycQT005JDky8LEnl3xzT9IGIQ+4VdTEf5z1eCuB7VTNGoGcwMQZHgZgJmddHQna\/NC8y3ugBX1wX87TB22iJgGLm+4j5B0N9whWysb3hy8x81eD2zzJBFHLToQ9vfT6Am6o0yjLpDnHhwpvyno7G1cw0c3D7SqmG+5HrV809bF4V5cIwgTNHx\/aumHtUHgQiOeuUOqdb+g\/EZueHOFwOhAYOYuJEqM8JxUEg4lkfvb3w6MIqXwrUkv8jdlS3MTvmRyfK\/f9aKLjTBz\/g00xQiN0pRZr+dgCfHe1ad6+ae5iIXgEdmnfkmJBl+pRz+t4Z516f9LQ1ulH8cNW29cktnfd1tzd8KHT8TS\/YXjSudB6GhgT6RM\/1Tb+rP2fNY6qg7g58Po7dkX3J8IqJQJrop08aw9LCmCMIt7Y21UFwCfPQhZAoZKjgt31H5zvtdZ216LY7cpFbR9oqxaxvbm1dWhVhvAdam+qI6HDRnZNAGNhwwgW3brFH\/ti5p\/XkwwW4eKQ64aq0fNnta\/48\/D2FzK8qFuWe4YmxzlFEIb+maiMIqvtkxqRKvxu+5FsK\/+rTC3lQkWXU45B0ePF+UQCEMAB9RTi4hbzeHLD8Pi2mv4\/C9HdRmP7Osfx+4BWw\/J683oyMb0bKt3gKbokkvAUp3xI7vvng+rTqT10lAARSqNeWlmXeBXhflktxeI5A7hUO+sHuttOeM9q1ksxdWoj5OTvlHgSEPJcvzrpg3cbW1qVOR0lG3FfjYb9FEhZ1fjdJZWUhHvroTDNFXHAztU8\/BQC97Q3nF0L38pEKIqWZrMse8V8al2je3R8cGPrTXEBnDA75EgEi6sH0vXnzRq5aF4C+PXybVJYrAsennhjd\/4JqMNpjkKep6jP8sPQY5wAIfk8Ey4geB4krXBAFfNSQSFVpFuJF5UdXjFDM4zktG55kh6uHh9\/yXEGg8+5obXpGVc7edN+EVMX7J9Nc\/ODQsShQVwhiAlfksdBFYHgZhO3CkgkHOkfHhQEfHziaEQT8vMDxCYHjE0JHMwZegaMZ\/X\/znCCgZwcBHRcE\/KwgoGdHAR0fBn5qLYxZJaj0R7NmXtC5xot+LY6GTQy9Ior4YIW\/fKSdbHevbHhRXcT\/Orz4XhQyktR3HSCDHmo6sk8VcFUvDRFBFfIfWSZ\/2WmpIfFgwkU9bQ2vUqIPQxEOllGOCbnXp3Kid8x7y\/h20\/EYHNHbhn8hx4Q01z8d8Hi8YrT3ZdvC3ySJ3xQOKWYDBAHFELyuGowWAHUApuhOypoAh3\/aI38c0YPV58TE+urhojIICCLoOfCpKb8d9SEmWJF5eWLwe\/uXtI4KQyyuxvZixj4RCM7hqTzXPhqWps4EgKRi956PtilOtPRAy8f58v2vgX+rUu1MDgbdqyv4z\/cV\/Z+CYFjCYiaIAnpFd1vDuYN\/vnr1OXEm+DgxTR2clEwEeJEiO37nc1o2PLn956PsVqAayB2Z3bz+r3mOj7HbuS8TUFDgKgDz0mG7v8KA4HP5wsmL1q4dt5\/Z1S97VjYdz0wvGh72iSICCFcdt4s9lHNec\/PjonSNG+ZYskwBovPvWd1Q8cVsMtIRjtXYrhIsejAO8r7HTgkcdirowaUTB7+7q742e\/HaTXkuvxi+b7\/kqOWiakxWVOybfd8qVFpqriIiz2rjc+I6opBuf2jPfOmGB5np\/YyhGXKqADMiInzyzqvmHDDw82cWH31vIXILkhFC4lmO75x4\/prfDfxs6cZlCsWIuQYkGtRCc89qXnNVmsqPh+9qEAWYaac2iCNGMZHfPfzkQ5\/do4nILn8r+qo4cgcP3nLimFBM5DHN8xW7u3iapz9JMnlq8MzOi6Iu4kOTPryy0o0VqksImtDIHda2S41HIBC93bmh2tg5Ql8i\/9BUWnf3fiL+1vCyzLlXMNHJRz5VXFh9fnnfQETTmGmKjqRHKnSWXARAOrLAYt5xJPbevKhGdkMQqbIfutzVkxyzMsl0WTTsIZZmirqCm8NTC+8EgLta5z2PiN83UoG5vqK\/F0VcSYO7+segOopAUEJQG+0NzTNcVkxlp2Ohh\/dox4Q0k0fA+raFl9xfnFCBcF\/7mQdBcUnmhyq7ICB4wa\/mXLjh7t1dfG7LHfeK6HU7zexEAcabe1rPrOh1uqLLthJh8\/AYgqhCgOPtsT82Nv288VmOcfbwB3wUEAj6g\/qWdf\/Y3TX6OLvVe10XBUOXtAqxC0T0omqr4qm6b55ARJheiIYet01UquYG0opMwi0AUBo58ue9PJZ5fSDz+rc8179muTyQebl\/1FeuD+S5\/nXH3+sDSSr3+4y21cLYVQBwQ4ViS8syT44uS1J5eHiiepYrmOjdmzpOe54L3GWFiA\/0w2oeiKoHybvnvHrtQzv1R0BGGsnVVmp5V5zc0nkfqb5LFaMWXCcaKIiED410jPNeC4Stuu2CKKLjBqs7IiD34hnyf2P9AFF8JUllSGXFPFeEjp+LYNuSirbUsXgEwD+GZ+16ryCll4ylVrYB5EVqKUR8xODkxIGTxgD6wViuMe\/8DdtUcY1i6KJPMfUAqLn3usaqEmw0UpbdZDwABLOH18NnBryXxzPhByqx7VInBN25dgkAkNN3x\/GjMw5x+fMOOLgwg\/Itz33koLrjR3tRvvm503N6XuLy5z2V0\/Mo3\/zcB6fFx89euvb2Wh7T9ResuZcUnyhFU4b6Rsd0mFe\/HNBXJNnOiYl5rj\/elK1fPc4nWU3Vr5jV3Lnce\/nJ8Eqn26MwISPL5Kez7jzn23vzOSOGZVpblzrFA28d7oD6q9l1PfrklFvG+gF1dY\/dkRYP+V0Y8QsH74RwjijzfNE9Xz7nxzPedX1SiUaaN29D1t3e8OjO28AAAM\/p6fjZMwD81STALqIHHadNyyV\/c+aHdvTAEfoyuXHOks5NY72WV742zfyHnaPDBu8VjiM+oJjoGwF8sJpCCNgnPlEvGB6NZyIQ8Lf8kfQP1TYhVuHixB33XNtovvnrCU1bPPywIC+KwFG9l6Fh8cARksw\/TIG\/rGUx\/CgRC7bsEQAELbb6DxBRY+DouMHRV8dAmvl\/epb37W2hvhHlxwz+20sCR3N9PryGM4GB7+7ugIch1zr33gTEbRi2bJqmAia8sPjMp06tcDv9bngdBC+KQswHMbmLrCfvmkzypYWYdzroyosKMX1lPNc6acltDyth2UhVPIn0lXeuWHBEVbmISaZ71bz5QUj1ud8pegEC3TPvLRsq8mjk0ilTO6\/QqAKsYo+fcakAUDDKald9S2+qwOWq6Bvp2GIddt42M4GgH60\/f8OIkamPfaxUdXmU71FzzGvZ8ICo\/nb4AVbOMaC08aRF6\/dawPMIg4Qcy5vCkEIZtpeymMpfxdGy8T8FcE2S6T8G34iidMojwf97ZU85eHWWD61X3z8ASBRv6Lm+6RDzIiOz\/ptzQyJ6s8jQfhiFjCyXOzdnNK5tOaWEJvpOmsnWwcs+uVcUYj6WSF5VRROIyU+C9fzOIOBpIjufqiekyyq17ZJMacRCCAQVYm8jc3w90dPoYnV289rfJplcXRfverW1dEaA3BhGh+5Rld3RkhdrwQA6glpS6IScOraT1e7uaKoH4fw03Tk5URWr5lywc+LI7qhvWfMYVK8aXkAjywUEnNuzsqli14cfObjwRxX96fBZa+4VcUjPlq36QXMiIzPlsOCFTJg3PDmRCCDVa05rWdM33mvOaV67Ic\/1ptDtnBfiSN\/yp9a5B1aHW6BJjSD0rmw8iwmvHL79LHCEJPV\/Bfytldp0hRigEZJWCaoKNYEwPn1AktMuxeqBdXRpMZU\/hMHIXZYJyDLNUvgPzzh3z5abiTQ3Ywwxy4RMIHYSCF71DYWIo+FFK5LU5xD91p5+UEDRsmIqWwZHEbwAhZgPUq+vqFRDLFx4U54xvppkkg\/PRchyRRjQe7tXzJ+wUyw3Ll9w7N2rGl9R6R1YFewZl8QRB8OrfqWpf4hVrt3ja7P7lhcdkuGb5YoodCdui4MXV8m8YdKCqneuWHCECP47cENtMyAQRLRtdvP6is2tKSalCoA7z7sAFrLD1cbdFXddMOuYc9Y85r2\/zIvKSLI2ihhe5SunNq\/fpej82Mf6jx8Y0Z9QTQo7muQzWYYY9p7Vpx+u0KXD14OjkCGKnyeP+D3eLnFi880bvNe1O5\/PAJDqa+5Z3TC9Uo30x\/TY36noyuEZpf3OlVzgvtDdvvcP9d62+TOU\/TVhwNd2tzW+q5I79t0r5z0XqouTESJVXmn5zCUbHtzTa4dEv85y+dPwfcKlDoc3VIVn0MlZde1a2XhkFMiyQsSzshFykJJMnmTVr1Vy08UhKbBzCIEAVSbLQRiXOAAFY5ir1jevb\/deVw4v8BMEhGIif\/LiPjPGmbEfcZsja00KO1Wd1J1yQy6eJPkFceSeMXxfapaLEOT7e5uU5EA7OZYsF0QFPiHZRi+tVCO1tCzzucrHklw2j3RAFRGmM+EH3e0N793Tqn5dyxvPBfMvo8g9P8mUopD+u7tj\/ocrdX+\/F\/emQuxiHSlSRXt32ueJi27dDGDFcFvkXuEcTr+rvWl25TuGiZ85dC9vWBgCP49DfsHww16A0i4m8fjSzMXr7qlSgUUKsQJn4x3LY9hiWDqXJnhfkso\/BsYlEaAKFdVLT15y25hK0yuJt\/O4h7RrMJpymFCB0NM6MyLIxTs91Jngvf5pcx78fG8\/7Kmt+qsk9X8YXjgJCiLGWyrZUCcvWd+jni5zRDsVryhl0VMchfy5w5\/oW31X+\/wXr\/\/m3DElkXQtX3DCxo6mr4YhtTtHx6SZQEThRSkO3JW97U2fu\/OGHaVLKyJ6sGruYQCW+OGFkUKCAr+dfVDhrr32\/55+lKa6bXAVTxFFHPGBpLK4ih3GuITD+ta5B25qb3zhxo7Ga1xIq52jU4YfEwsAhZhRTPyvD\/T0mUpvo9EOayrNUC1JcdwTPxlbNGv24lv\/JKKfiAJGHDEOKDh4r1fPWdy5bIx9W6GQkTYyqFJtCjvicGT9OzFLLtvVh8bT54esTVm+c1JS5vG1PUkYG07T6zqf6m6bf3Xg8PFsUEpJniuY8fyutqYz5yxec1Ol2mpW85qvbexoaKyL3WuHO1kRhQgQhXxWnutZ0dP4d11tjTcz8pW5cw\/HGiTqEyUO4kT81JDci4X0RaR+YRy5aWkqGNxmIkCuigMOcO95alt8P4CvVEo7pQlfOGUKHzd4lkoEeA8h8Pdo4U17nXA0+8K13T3tDb+ti925xSHn0ysY9PqbO0774umlSEOlToB3coj9220P7l624DQXeRHvRZglYCe5VyLn2eVRkGt+CDOdqsCJIJxIjk6OmJBlgsG1SraLg9JRsX\/2Dm8+ZvHe+4H9TeRZhUd6zhBDZHbvigX3uTgPJEeQegoiduydMAkz1Kswi3ovDOdVc+0\/rwIIWEFCkolziBwH3hEjk2TL7fUtvWk1Pp8UkMFnMeyOhw+u+8ahT\/QdGTDPKab+cSfxpeMTwJSN\/PPaLG2vI9ZDIRAmJmkzGORd\/s0F7PJBAiFwhGLq\/x5n0TUTdUMcBT9Mc32HYzpyYClDFCjEHGnRX6yK31XqMclEkHta03f3ITqmELsXFEeYiQ044CjgFzDrC7I8uDTw6j38EyDnVXFIwM45Rxw5RpYLRroOUDqIY\/NW\/2si7aiUNuppPXOqur63DK8dUYpUyd+2bMF1E2QL7V7u\/if3eu7waE4Y8rOm5\/5VAP6vUh0D0c4zpqx0xPUsBHKLCAEUKCng8\/51KO8g5Mn1nzfgHMF7RZYr8lGGXCFi5F7\/7D3OO3lx533V4FTZCckwxzqQL0RMn\/AqH\/cp9\/dLkIcAHqXJKwgkpZBn\/6bwHZUCFYAygoBLFYGZ4b1sCcJpswH8pTojVqqSjz3qsrAk\/j+y5x84SpIiqFYFgo4m3CZkrADAhuULjgX0xcO3mwUBQYXaZ7Tc\/PCEzbLPu+3+LJefDj8ONM0ECizpaT\/t2ZVssBktdz6ci1taTGVlHDHcKMeUp5mUHDqBiClgR4ex4yOZERKBc69IUhmoyrjTw3RKgVFMZJmDvKKiMsqj4oviyJ00fKYaBgyv\/N2m13U+NVEf5bdtvTn3umbwkeP94oFY9fWrV58TV+xDbhelYYi2v4gJxNz\/ov7NkVoSE8Wk1AdHvD5TaVkh9bclxfzc2YvXbkI1sYu9+9vbi0vtNVCdZ\/trtPYd\/p7+T\/BZULUJdKqABG6\/L8vQJCfrlfFEYfJ3McQsS+piN6QWfv+5C30CvWaiP1Q8fTtJZchavQhQiN00Jv\/GSjfayUtu++fDjxdemab6ISI8WYgZo+iE0r4dHViC0O3\/Hs1plwqO6GNJJu\/SbPNrZy5e92iFKd63DY+J9e+tf5jAP5xQO1zUtRWKZTvPtAVEtODpfY89v4JnDrIrp73L1y6u61xJGBCwJc3k81GMc05qWV9tJZVBoPG1Hwa9dOwvANpfurEKn06lSJ0TkX3Y73mUJ2VN7mIgndwz2\/jOG+YcoNB\/HR49CENGmmHNnMUv7ZzoD71HN9+hqr+Jgp2LC3nVpXe0n3lQpRtu4SU3FWc1r\/nk1kRfkJaOK04KMWP44SW7V4ilB2hdyWlvTTO5Kvc4\/cTz1\/5Ppa1r3rWi6VTHdHrmd67M51V\/MXvxrX+a8A\/N5Udpqk8MTlbsP5+BHNOb9HJU5MxDiSZkjZGo1P5hQJhS5wDgoSyVH+S5nD7zgrXvm3HuxEV0yoVtnhX7riASUSrVm3ivpEq8zx7ONMrxAKRIUYMojXaK5cTsYgiCrXVvKcT8PFEgDAY9lJjgQN8gumLCjd\/S0pv2tDd9V4EXxxEPuhXFtCnB8VuTYgsqeH14MHOXru0C8Iru9nmnZam+VsAvVcUzCjGH3it8f\/LiTk67f524mAq84C+Sy0997q+evWT9usqc8YJ62vVfp00JpvYlHti+BbFUvw6E70zG585auu6hno7GHx5QF\/x7ku54JigAR1i68eT5nwXW3VmJLVqq3jmsoP3ofz\/k77hUlxrF1HsI\/pELHgTJVZLq9bMuXPvHanaqkctE4TiMGOK1X7Dvuu1U+2fMu23n\/r9HKeLnPaKqmKmyUuQYA2W3FQomIPES8T5a\/1cFbVyJuigkDNYJUchIq1mE7bqrRWFA289uI1CpPTI\/IaWWAwBRLnptnmmupERKClJKc3kKOf9qsu6r6LIOTcIvBg5Tc1EFgSBKIIQqOqXa7Di7VCXs1jvaTzqIKX5Jlsvzcq9zSNEAwjOJyKkOnIeOJ8VLt1fcDN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[et_pb_line_break_holder] --> pts.forEach(p=>{const c=p.m==='a'?'var(--astra)':'var(--opus)';<!-- [et_pb_line_break_holder] -->  s+=`<circle cx=\"${X(p.x)}\" cy=\"${Y(p.y)}\" r=\"7\" fill=\"${c}\"><\/circle>`;<!-- [et_pb_line_break_holder] -->  const anc=X(p.x)>W-120?'end':'start',dx=anc==='end'?-11:11;<!-- [et_pb_line_break_holder] -->  s+=`<text class=\"lbl\" x=\"${X(p.x)+dx}\" y=\"${Y(p.y)-9}\" text-anchor=\"${anc}\">${p.l}<\/text><text x=\"${X(p.x)+dx}\" y=\"${Y(p.y)+6}\" text-anchor=\"${anc}\">$${p.x.toFixed(2)} \u00b7 ${p.y}${ylab}<\/text>`});<!-- [et_pb_line_break_holder] --> el.innerHTML=s;<!-- [et_pb_line_break_holder] -->}<!-- [et_pb_line_break_holder] -->scatter('c1',[{m:'o',l:'Opus medium',x:.86,y:1576},{m:'o',l:'Opus max',x:8.92,y:1846},{m:'a',l:'Astra max',x:4.53,y:1542}],10,1500,1900,[1500,1600,1700,1800,1900],[0,2,4,6,8,10],'');<!-- [et_pb_line_break_holder] -->scatter('c2',[{m:'o',l:'Opus medium',x:2.94,y:57.6},{m:'o',l:'Opus high',x:3.88,y:64.2},{m:'a',l:'Astra 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Custo mais baixo, qualidade menor em tarefas complexas.'},<!-- [et_pb_line_break_holder] --> std:{o:1,a:1,t:'Padr\u00e3o: os dois fazem o mesmo volume de trabalho. 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