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Claude Opus 4.6 vs Claude Opus 4.7

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Claude Opus 4.6 and Claude Opus 4.7.

Anthropic

Claude Opus 4.6

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...

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Anthropic

Claude Opus 4.7

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...

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Technical Specifications

SpecificationClaude Opus 4.6Claude Opus 4.7
ProviderAnthropicAnthropic
Context Window1,000,000 tokens1,000,000 tokens
Agent Suitability95/10096/100
Time to First Token (TTFT)500 ms480 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-02-042026-04-16

API Pricing Comparison

Input Price per Million Tokens

Claude Opus 4.6

$5.00

Claude Opus 4.7

$5.00

Output Price per Million Tokens

Claude Opus 4.6

$25.00

Claude Opus 4.7

$25.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9250.0%vs9410.0%
Claude Opus 4.6
Claude Opus 4.7
HumanEvalPython coding & logic synthesis
9450.0%vs9610.0%
Claude Opus 4.6
Claude Opus 4.7
MATHComplex mathematical problem solving
8890.0%vs9150.0%
Claude Opus 4.6
Claude Opus 4.7
GPQAGraduate-level expert reasoning
7980.0%vs8420.0%
Claude Opus 4.6
Claude Opus 4.7
HellaSwagCommonsense reasoning and inference
9750.0%vs9880.0%
Claude Opus 4.6
Claude Opus 4.7
MT-BenchMulti-turn conversation flow quality
965.0%vs975.0%
Claude Opus 4.6
Claude Opus 4.7

Claude Opus 4.6 Quirks & Gotchas

  • Best for long-context document analysis and legal review
  • Tool calling requires structured prompt — prone to verbose refusal without explicit output schema

Claude Opus 4.7 Quirks & Gotchas

  • Top-tier agentic coding model — excels at autonomous software engineering
  • Requires explicit tool_choice parameter for parallel function calling to work reliably