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Claude Opus 5 vs Claude Sonnet 5

vs

Qual usar, quando — veredito curado, não uma tabela de benchmark

A clássica questão de escolha de nível, agora com uma diferença de preço de 2.5× ($5/$25 vs $2/$10) e contextos idênticos de 1M. O Sonnet 5 foi posicionado como uma atualização direta de capacidade em relação ao 4.6 — para a maior parte do tráfego de produtos, ele atende plenamente; reserve o Opus 5 para a cauda longa de agentes de longo horizonte, onde sua profundidade é o diferencial.

Preços

Claude Opus 5 Claude Sonnet 5 Δ
Entrada / 1M tokens $5 $2 2.5×
Saída / 1M tokens $25 $10 2.5×
Leitura de cache / 1M tokens $0.5 $0.2 2.5×
Escrita de cache 1.25x (5m) / 2x (1h) 1.25x (5m) / 2x (1h)

Tarifas do catálogo em tempo real no momento do build; a página de cada modelo contém o cartão atual.

Onde eles se posicionam — preço de entrada por 1M tokens entre todos os 63 modelos de chat nesta unidade de cobrança (escala logarítmica)

Capacidades

Claude Opus 5 Claude Sonnet 5
Uso de ferramentas sim sim
Controle de raciocínio configurável configurável
Saída estruturada sim sim
Cache de prompt explícito (você marca o prefixo) explícito (você marca o prefixo)
Tempo de vida do cache 5m default, 1h option 5m default, 1h option
Prefixo mínimo em cache 1024 tokens 1024 tokens

Especificações

Claude Opus 5 Claude Sonnet 5
Modalidades de entrada texto imagem texto imagem
Modalidades de saída texto texto
Lançamento 2026-07-24 2026-06-30
Corte de conhecimento 2026-05 2026-01
Janela de contexto 1M 1M
Saída máxima 128K 128K
Parâmetro de raciocínio
  • thinking.type
  • output_config.effort
  • thinking.type "adaptive"
  • output_config.effort
Valores aceitos
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max

"enabled" returns 400

Padrão

thinking on

effort
  • high (Claude API and Claude Code)

thinking on (adaptive)

effort
  • high

As especificações são transcritas da documentação de cada fornecedor; uma linha que um fornecedor não publica é omitida em vez de ser inferida. Fontes completas: Claude Opus 5 · Claude Sonnet 5

Um prompt, ambos os modelos — medidos pelo gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. VERIFICAR exatamente 3 frases

Claude Opus 5 passou · 3 sentences

When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.

saída 529 tok latência 9.0 s

Claude Sonnet 5 passou · 3 sentences

Reasoning mode works by having the model generate extended internal "thinking" steps before producing its final answer, and since we're billed on total tokens, that hidden reasoning text adds significantly to what we pay for—even though you never see it in the output. This is different from simply getting longer visible responses; the model is essentially "showing its work" behind the scenes, and complex prompts can trigger thousands of extra reasoning tokens per request. The upside is improved accuracy on complex tasks, but if we want to control costs, we should selectively enable reasoning only for queries that truly need it, rather than applying it universally.

saída 194 tok latência 4.3 s

Cumprimento da instrução (exatamente três frases — contáveis), ajuste ao público (registro de CFO), e a lacuna de cobrança do pensamento oculto que os medidores de tokens abaixo expõem.

PROMPT This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. VERIFICAR a correção passa nos testes

Claude Opus 5 passou · 8/8 cases

## Fixed version ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]

saída 1094 tok latência 16.1 s

Claude Sonnet 5 passou · 8/8 cases

## The Bug The function has two related off-by-one issues rooted in the same cause: `cur` and `best` are initialized to `0` instead of `1`, treating run lengths as the number of *matches* between adjacent elements rather than the number of *elements* in the run. This means a single-element input never enters the loop (since `range(1, 1)` is empty) and returns `0` instead of the correct answer of `1`. It also means that for any input, the run length is undercounted by exactly one — a run of 3 equal elements only produces 2 "match" increments, so `best` ends up as 2 instead of 3. The claim that it "misses runs that end at the last index" is actually a symptom of this same undercount: the logic does update `best` correctly on every iteration (including the last), but because it's counting matches instead of elements, the final reported value is always one less than the true longest run. […]

saída 536 tok latência 7.1 s

Se a correção é de fato certa (executável), a densidade da explicação, e a eficiência em tokens numa tarefa delimitada.

PROMPT Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. VERIFICAR JSON válido, esquema exato

Claude Opus 5 passou · 5/5 fields, guidance "Withheld pending Q4 close"

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.

saída 539 tok latência 7.6 s

Claude Sonnet 5 passou · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11.5, "guidance": null } ```

saída 348 tok latência 5.3 s

Obediência ao esquema (sem campos inventados), pressão de alucinação (guidance é explicitamente retida), e diferenças no caminho de saída estruturada.

PROMPT Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. VERIFICAR 120 palavras, 0 palavras proibidas

Claude Opus 5 passou · 123 words, 0 banned, 1 question

**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.

saída 1593 tok latência 19.1 s

Claude Sonnet 5 passou · 123 words, 0 banned, 1 question

## Introducing Cross-Provider Prompt Caching We're rolling out a new caching layer for our API gateway that works across every major LLM provider you connect through it. Instead of resending identical prompts and paying full price each time, the gateway recognizes repeated requests and serves cached responses in milliseconds—regardless of whether you're routing to OpenAI, Anthropic, or Google. What does this mean for your bill and your latency? Both drop, often significantly, especially for teams running high-volume, repetitive workloads like customer support bots or batch content generation. The cache is configurable per route, with adjustable TTLs and invalidation rules, so you stay in control of freshness versus cost. Available now for all Pro and Enterprise plans. Check your dashboard to enable it today.

saída 259 tok latência 4.8 s

Obediência às restrições (orçamento de palavras, lista de palavras proibidas, a única pergunta), impressão digital de estilo, e controle de comprimento.

Alterne entre eles com uma linha

Ambos os IDs estão em todas as abas abaixo — o par de linhas destacado é a única edição. Mesmo endpoint, mesma chave, mesmo formato de requisição.

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="claude-opus-5",
    # model="claude-sonnet-5",  # descomente esta linha, comente a linha acima
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

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FAQ

Qual é mais barato, Claude Opus 5 ou Claude Sonnet 5?

Claude Sonnet 5 é mais barato em entrada / 1m tokens ($2 vs $5, com 2.5× de diferença). Outras linhas podem apontar para o outro lado — a tabela acima traz o quadro completo, e o custo real depende do seu mix.

Posso fazer um teste A/B de Claude Opus 5 contra Claude Sonnet 5 sem duas integrações?

Sim. Ambos são servidos pelo mesmo endpoint compatível com OpenAI com uma única chave de API — a troca é uma alteração de uma linha na string do modelo, de modo que você pode rotear uma fração do tráfego para cada um e comparar as faturas diretamente.

Claude Opus 5 e Claude Sonnet 5 suportam prompt caching?

Sim — ambos cobram leituras em cache abaixo da sua taxa de entrada, então cargas de trabalho com warm-prefix custam menos do que as taxas listadas sugerem. As linhas exatas de leitura em cache estão na tabela de preços acima.

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