Intelligence
54 scoreAnthropic · Flagships Analysis
Claude Opus 4.7 (max)
Intelligence, Performance & Price Analysis
Canonical slug: claude-opus-4-7 · Canonical model registry at build time
Speed
56.4 output tokens/secLatency
17.52s TTFTInput Price
$5.00 / 1M tokensOutput Price
$25.00 / 1M tokensVerbosity
100M Output tokens from Intelligence Index 3 out of 4 units for Verbosity . CompExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Frontier research / complex reasoning
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) scores 54 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 28). Claude Opus 4.7 (Adaptive Reasoning, Max Effort) generates output at 56.4 tokens per second (based on Anthropic's API), which is at the lower end compared to other reasoning models in a similar price tier (median: 81.2 t/s). Claude Opus 4.7 (Adaptive Reasoning, Max Effort) costs $5.00 per 1M input tokens (at the higher end, median: $1.50) and $25.00 per 1M output tokens (at the higher end, median: $8.40), based on Anthropic's API.
Reserve this model for the hardest requests; simpler or repetitive work should stay on cheaper routes.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | optimal | Intelligence score 54 places this model among top performers. Suitable for multi-step analysis and agentic loops. |
| High-volume chat & customer-facing | optimal | Output speed 56.4 tokens/sec is adequate for chat. |
| Latency-sensitive applications | viable | TTFT 17.52s — latency may be noticeable in interactive use. |
| Cost-sensitive pipelines | viable | Output pricing at $25.00 is premium. Route high-volume simple tasks to cheaper alternatives. |
Cost Pressure Analysis
High
Pricing is premium — input $5.00, output $25.00. This model is expensive for high-volume or output-heavy workloads.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | Yes | faq |
| Input modalities | Claude Opus 4.7 (Adaptive Reasoning, Max Effort) supports text and image input. | faq |
| Output modalities | Claude Opus 4.7 (Adaptive Reasoning, Max Effort) supports text output. | faq |
| Context window | Claude Opus 4.7 (Adaptive Reasoning, Max Effort) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request. | faq |
| Open weights / source | No, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is proprietary. The model weights are not publicly available. | faq |
| Parameters | Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is a proprietary model and Anthropic has not disclosed the model size or parameter count. | faq |
| Knowledge cutoff | Claude Opus 4.7 (Adaptive Reasoning, Max Effort) has a knowledge cutoff of January 2026. The model's training data includes information up to this date. | faq |
| API availability | Yes, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is available via API through 4 providers. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
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. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
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 · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
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 · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Tokens per Intelligence Index Task
Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Claude Opus 4.7 (max).
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) was released on April 16, 2026.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) was created by Anthropic.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) scores 54 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 28).
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) generates output at 56.4 tokens per second (based on Anthropic's API), which is at the lower end compared to other reasoning models in a similar price tier (median: 81.2 t/s).
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) has a time to first token (TTFT) of 17.52s (based on Anthropic's API), which is at the higher end compared to other reasoning models in a similar price tier (median: 2.77s).
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) costs $5.00 per 1M input tokens (at the higher end, median: $1.50) and $25.00 per 1M output tokens (at the higher end, median: $8.40), based on Anthropic's API.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) costs $5.00 per 1M input tokens and $25.00 per 1M output tokens (based on Anthropic's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $3.85 per 1M tokens. Pricing may vary by provider.
When evaluated on the Intelligence Index, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) generated 100M output tokens, which is somewhat higher than average compared to other reasoning models in a similar price tier (median: 72M).
Yes, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) supports text and image input.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) supports text output.
Yes, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) supports image input and can analyze, describe, and answer questions about images.
Yes, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is multimodal. It can process text and image input and generate text output.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.
No, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is proprietary. The model weights are not publicly available.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is a proprietary model and Anthropic has not disclosed the model size or parameter count.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) achieves a score of 54 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) has a knowledge cutoff of January 2026. The model's training data includes information up to this date.
Yes, Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is available via API through 4 providers.
Claude Opus 4.7 (Adaptive Reasoning, Max Effort) is available through 4 API providers.