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Claude Sonnet 5 vs Claude Sonnet 5.5

vs

Qual usar, quando

claude-sonnet-5 e claude-sonnet-5-5 têm preços idênticos de $2 por milhão de tokens de entrada, $10 por milhão de saída e $0.2 para leituras de cache, e ambos compartilham um contexto de 1000000 tokens, saída máxima de 128000, entrada de texto e imagem, e as mesmas flags de chat, code, thinking, tools e reasoning. As verdadeiras diferenças estão na geração e no controle: o claude-sonnet-5-5 é o lançamento mais recente (2026-09-28, corte de conhecimento em 2026-06), enquanto o claude-sonnet-5 (2026-06-30, corte em 2026-01) permite que você desative o thinking. Escolha o claude-sonnet-5 quando precisar de respostas sem thinking; caso contrário, opte pelo claude-sonnet-5-5 para obter o corte mais recente sem custo extra.

Benchmarks

Claude Sonnet 5.5: o fornecedor não publicou resultados de benchmark.

Acima da médiaNenhum melhorClaude Sonnet 54 / 221 / 22
Claude Sonnet 5 Claude Sonnet 5.5 outros modelos medidos média dos modelos comparados ★ nenhum outro modelo pontuou mais alto
DeepSWE 1.1
53.8%
N/A
BioMysteryBench hard
34.1%
N/A
OSWorld 2.0 Partial score, batch tool enabled
42.6%
N/A
Finance Agent v2
53.9%
N/A
Harvey Lab-AA
90.1%
N/A
HLE-Verified
31%
N/A
AutomationBench
10.7%
N/A
LVBench
68.5%
N/A

Publicado pelos fornecedores: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai

Preços

Claude Sonnet 5 Claude Sonnet 5.5 Δ
Entrada / 1M tokens $2 $2 =
Saída / 1M tokens $10 $10 =
Leitura de cache / 1M tokens $0.2 $0.2 =
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 76 modelos de chat nesta unidade de cobrança (escala logarítmica)

Capacidades

Claude Sonnet 5 Claude Sonnet 5.5
Uso de ferramentas sim sim
Controle de raciocínio configurável sempre ativo
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 Sonnet 5 Claude Sonnet 5.5
Modalidades de entrada texto imagem texto imagem
Modalidades de saída texto texto
Lançamento 2026-06-30 2026-09-28
Corte de conhecimento 2026-01 2026-06
Janela de contexto 1M 1M
Saída máxima 128K 128K
Parâmetro de raciocínio
  • thinking.type "adaptive"
  • output_config.effort
thinking.type
Valores aceitos
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max

"enabled" returns 400

thinking.type
  • adaptive (default)
  • between_tools
Padrão

thinking on (adaptive)

effort
  • high
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 Sonnet 5 · Claude Sonnet 5.5

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 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

Claude Sonnet 5.5 passou · 3 sentences

Enabling reasoning makes the model "think" before it answers, and that internal thinking is generated as extra text (reasoning tokens) that we are billed for at the same rate as output tokens, even though users never see it. Output tokens also cost several times more than input tokens, so a single request that used to produce a short answer can now generate many times more billable text behind the scenes. Because our request volume and prompts didn't change, the doubling comes almost entirely from this hidden thinking, and we can bring it down by limiting reasoning effort, turning it off for simple tasks, and reserving it for the workloads where the accuracy gain justifies the cost.

saída 205 tok latência 3.6 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 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

Claude Sonnet 5.5 passou · 8/8 cases

**The bug:** `cur` counts the number of *adjacent equal pairs* rather than the number of items in the run. A run of length k contains only k−1 such pairs, so every result is one too small. The counter also starts at 0 and the loop starts at index 1, so a single-element list never updates `best` and returns 0 instead of 1. (An empty list should return 0.) In addition, `cur` resets to 0 on a mismatch, when it should reset to 1, because the item at `xs[i]` begins a new run of length 1. The claim that it "misses runs ending at the last index" is really the same off-by-one: the final run is counted, but as one less than its true length. Because `best` is updated inside the loop on every extension, the last run isn't skipped. […]

saída 444 tok latência 6.7 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 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

Claude Sonnet 5.5 passou · 5/5 fields, guidance null

Here is the extracted JSON: ```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on the choices: - **quarter**: The text says "September quarter" without specifying a fiscal year or Q-number, so I kept the wording as stated. - **revenue_usd_m**: The text gives "412" for the topline, with no currency or unit stated explicitly. I've taken it as 412 million USD per the field definition, but the text does not confirm that, and the figure is constant-currency. - **growth_pct**: "Eleven and change" means slightly above 11, so 11 is an approximation. The exact figure isn't given. - **guidance**: Guidance is withheld pending the Q4 close, so no guidance is stated, hence null.

saída 300 tok latência 3.5 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 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

Claude Sonnet 5.5 passou · 122 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching** Your API gateway now caches prompts across every connected LLM provider, whether you route traffic to OpenAI, Anthropic, Google, or a self-hosted model. Here's how it works: the gateway fingerprints each prompt prefix, stores it centrally, and applies the right provider-specific caching mechanism automatically. When you switch providers or fail over during an outage, your cached context follows you. Early testing shows input token costs dropping by up to 70% on repeated system prompts and long documents, with latency improvements of 40% or more on cache hits. Tired of paying to send the same instructions thousands of times a day? Enable it with one configuration flag. Cache rules, TTLs, and per-team isolation are available in the dashboard today.

saída 266 tok latência 3.8 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-sonnet-5",
    # model="claude-sonnet-5-5",  # 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 Sonnet 5 ou Claude Sonnet 5.5?

Eles listam a mesma entrada / 1m tokens ($2), então o preço não decide este caso - veja as especificações e capacidades abaixo.

Posso fazer um teste A/B de Claude Sonnet 5 contra Claude Sonnet 5.5 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 Sonnet 5 e Claude Sonnet 5.5 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.

Comparações relacionadas

De nossos estudos medidos