Claude

Claude Opus 4.6

ClaudeFlagship
ThinkingTool UseVisionStructured Output

About this model

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 for large codebases, complex refactors, and multi-step debugging that unfolds over time. The model shows deeper contextual understanding, stronger problem decomposition, and greater reliability on hard engineering tasks than prior generations. Beyond coding, Opus 4.6 excels at sustained knowledge work. It produces near-production-ready documents, plans, and analyses in a single pass, and maintains coherence across very long outputs and extended sessions. This makes it a strong default for tasks that require persistence, judgment, and follow-through, such as technical design, migration planning, and end-to-end project execution. For users upgrading from earlier Opus versions, see our [official migration guide here](https://openrouter.ai/docs/guides/guides/model-migrations/claude-4-6-opus)

Performance Tier

Flagship

Claude Opus 4.6 is a flagship model from Claude : the most capable in their lineup.

Best-in-class model from this provider. Highest performance across benchmarks, ideal for demanding tasks.

Pricing

This model is included in Elosia plans
Typeper 1M tokens
Input (prompt)$5.00
Output (completion)$25.00
Cache read$0.500
Cache write$6.25

Capabilities

Context Length1.0M
Max Output Tokens128K
TokenizerClaude
Inputtext, image
Outputtext
Release DateFebruary 4, 2026

Benchmarks

General Intelligence
MMLU
91.1%
GPQA Diamond
91.3%
Mathematics
MATH-500
96.4%
AIME 2025
99.8%
Programming
HumanEval
95.2%
SWE-bench Verified
80.8%
Reasoning
IFEval
92%
ARC-AGI-2
68.8%
Humanity's Last Exam
53.1%
Agentic
Terminal-Bench 2.0
65.4%

Recommended Use Cases

CodingAnalysisResearchCreative Writing

Strengths

  • State-of-the-art reasoning and agentic capabilities
  • Top-tier software engineering performance (SWE-bench 80.8%)
  • Nuanced, high-quality writing with reliable instruction following
  • Strong long-context performance up to 1M tokens

Limitations

  • Higher cost per token than smaller models
  • Slower response time compared to Haiku/Sonnet

Resources

This model may use your data for training

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