This model is deprecated. We only continue performance benchmarking for the default 10k input token workload. Results for other workloads are historical and no longer updated.

Anthropic has launched a newer model, Claude Opus 5 (max). We suggest considering it instead.

For more information, see comparison of Claude Opus 5 (max) to other models and API provider benchmarks for Claude Opus 5 (max).

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) logo
Anthropic
•

Proprietary model

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Released May 2026

Análise de inteligência, desempenho e preço do Claude Opus 4.8 (Adaptive Reasoning, Max Effort)

Experimente Benchmarks de provedores de API

Resumo do modelo

Inteligência

56
Artificial Analysis Intelligence Index
4 de 4 unidades para Inteligência.

Velocidade

57.5
Tokens de saída por segundo
2 de 4 unidades para Velocidade.

Preço

Entrada
US$ 5,00
por 1M de tokens
Saída
US$ 25,00
por 1M de tokens
4 de 4 unidades para Preço.

Preço do cache

Escrita
US$ 6,25
por 1M de tokens
Acerto
US$ 0,50
por 1M de tokens
3 de 4 unidades para Preço do cache.

Verbosidade

120M
Tokens de saída do Intelligence Index
4 de 4 unidades para Verbosidade.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) está entre os modelos líderes em inteligência, mas tem preço especialmente alto em comparação com outros modelos de preço semelhante. Também é mais lento que a média e muito verboso. O modelo aceita entrada em texto e imagem, gera saída em texto e tem uma janela de contexto de 1M tokens.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) alcança 56 pontos no Artificial Analysis Intelligence Index, ficando bem acima da média entre os modelos comparáveis (mediana: 32). Na avaliação do Intelligence Index, gerou 120M tokens, um resultado muito verboso em comparação com a mediana de 65M.

O preço do Claude Opus 4.8 (Adaptive Reasoning, Max Effort) é de $5.00 por 1M de tokens de entrada (preço alto, mediana: $1.75) e $25.00 por 1M de tokens de saída (preço alto, mediana: $10.00). No total, a avaliação do Claude Opus 4.8 (Adaptive Reasoning, Max Effort) no Intelligence Index custou $3752.55.

Com 58 tokens por segundo, Claude Opus 4.8 (Adaptive Reasoning, Max Effort) é mais lento que a média (71).

RaciocínioSim

Esta página mostra a versão com raciocínio deste modelo.

Também pode existir uma variante sem raciocínio.

Modalidade de entrada

Compatível com: texto e imagem

Modalidade de saída

Compatível com: texto

Janela de contexto1M
~1500 páginas A4 em fonte Arial tamanho 12

As métricas são comparadas com modelos da mesma classe:

  • Modelos sem raciocínio → comparados apenas com outros modelos sem raciocínio
  • Modelos de raciocínio → comparados com modelos com e sem raciocínio
  • Modelos de pesos abertos → comparados apenas com outros modelos de pesos abertos da mesma classe de tamanho:
    • Muito pequeno: ≤4B parâmetros
    • Pequeno: 4B–40B parâmetros
    • Médio: 40B–150B parâmetros
    • Grande: >150B parâmetros
  • Modelos proprietários → comparados com modelos proprietários e de pesos abertos da mesma faixa de preço, usando uma proporção combinada de 3:1 entre os preços de entrada e saída:
    • <$0.15 por 1M de tokens
    • $0.15–$1 por 1M de tokens
    • >$1 por 1M de tokens

Destaques

Inteligência

Artificial Analysis Intelligence Index · Higher is better

Velocidade

Output tokens per second · Higher is better

Custo por tarefa

Weighted average cost (USD) per Intelligence Index task · Lower is better

Inteligência

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Avaliações

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
GDPval-AA v2

Agentic real-world work tasks, (Elo-500)/2000

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AANew

Agentic business operations

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better
Reasoning models are indicated by a lightbulb icon

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
Reasoning models are indicated by a lightbulb icon

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Comparações do Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Uso de tokens

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Custo

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)
Reasoning models are indicated by a lightbulb icon

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Janela de contexto

Context Window

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Velocidade

Medida pela velocidade de saída (tokens por segundo)

Output Speed

Output tokens per second · Higher is better
Reasoning models are indicated by a lightbulb icon

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better
Reasoning models are indicated by a lightbulb icon

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latência

Medida pelo tempo (segundos) até o primeiro token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time
Reasoning models are indicated by a lightbulb icon

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Tempo de resposta de ponta a ponta

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better
Reasoning models are indicated by a lightbulb icon

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Perguntas frequentes

Perguntas comuns sobre o Claude Opus 4.8 (Adaptive Reasoning, Max Effort)

O Claude Opus 4.8 (Adaptive Reasoning, Max Effort) foi lançado em 28 de maio de 2026.

Anthropic criou o Claude Opus 4.8 (Adaptive Reasoning, Max Effort).

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) alcança 56 pontos no Artificial Analysis Intelligence Index, ficando bem acima da média entre outros modelos de raciocínio em uma faixa de preço semelhante (mediana: 32).

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) gera a saída a 57.5 tokens por segundo (com base na API de Anthropic), um resultado abaixo da média em comparação com outros modelos de raciocínio em uma faixa de preço semelhante (mediana: 71.1 t/s).

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) tem um tempo até o primeiro token (TTFT) de 32.89s (com base na API de Anthropic), um resultado na faixa superior em comparação com outros modelos de raciocínio em uma faixa de preço semelhante (mediana: 2.82s).

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) custa $5.00 por 1M de tokens de entrada (na faixa superior, mediana: $1.75) e $25.00 por 1M de tokens de saída (na faixa superior, mediana: $10.00), com base na API de Anthropic.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) custa $5.00 por 1M de tokens de entrada e $25.00 por 1M de tokens de saída (com base na API de Anthropic). Considerando uma tarifa combinada (proporção de 7:2:1 entre acerto do cache, entrada e saída), o preço é de $3.85 por 1M de tokens. O preço pode variar conforme o provedor. Comparar preços dos provedores

Na avaliação do Intelligence Index, Claude Opus 4.8 (Adaptive Reasoning, Max Effort) gerou 120M tokens de saída, um resultado na faixa superior em comparação com outros modelos de raciocínio em uma faixa de preço semelhante (mediana: 65M).

Sim, o Claude Opus 4.8 (Adaptive Reasoning, Max Effort) é um modelo de raciocínio. Ele usa reflexão prolongada ou raciocínio em cadeia para resolver problemas complexos antes de fornecer uma resposta.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) aceita entrada em texto e imagem.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) permite saída em texto.

Sim, o Claude Opus 4.8 (Adaptive Reasoning, Max Effort) aceita imagens como entrada e pode analisá-las, descrevê-las e responder a perguntas sobre elas.

Sim, o Claude Opus 4.8 (Adaptive Reasoning, Max Effort) é multimodal. Ele consegue processar entrada em texto e imagem e gerar saída em texto.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) tem uma janela de contexto de 1.0M tokens. Isso determina quanto texto e histórico da conversa o modelo consegue processar em uma única solicitação.

Não, o Claude Opus 4.8 (Adaptive Reasoning, Max Effort) é proprietário. Os pesos não estão disponíveis publicamente.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) é um modelo proprietário, e Anthropic não divulgou o tamanho nem o número de parâmetros do modelo.

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) alcança 56 pontos no Artificial Analysis Intelligence Index. Esse benchmark composto avalia os modelos em raciocínio, conhecimento, matemática e programação.

Sim, o Claude Opus 4.8 (Adaptive Reasoning, Max Effort) está disponível por API em 4 provedores. Comparar provedores de API

Claude Opus 4.8 (Adaptive Reasoning, Max Effort) está disponível em 4 provedores de API. Comparar provedores