LANGUAGE MODEL Anthropic Last updated:

Claude Opus 4.6 (Adaptive Reasoning, Max Effort)

Claude Opus 4.6 (Adaptive Reasoning, Max Effort) is an API model from Anthropic. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.

Intelligence
Top 5%
Speed
Slow
52 tok/s output
Cost
High
$6.25 in / $25.00 out
Context
1M
Up to 1,000,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/M out).
Intelligence
  • Top 1%≤ 1%
  • Top 5%≤ 5%
  • Top 10%≤ 10%
  • Good≤ 25%
  • Medium≤ 50%
  • Below avg> 50%
Speed
  • Top 1%≥ 345 tok/s
  • Top 5%≥ 237 tok/s
  • Top 10%≥ 196 tok/s
  • Good≥ 146 tok/s
  • Medium≥ 90 tok/s
  • Slow< 90 tok/s
Cost
  • Freeopen weights · self-host
  • Low< $1 / M out
  • Moderate$1–5 / M out
  • High≥ $5 / M out

Core Capabilities

Long Documents
Handles entire codebases, books, and multi-doc RAG.

Context Window

1M tokens
≈ entire codebase
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M This model 本模型
10M

Availability

API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales

Pricing Model

Pay per token
Input and output billed separately.
Pay-per-token

Capability / Performance

Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).

Lower 20% Upper 80% This model
Reasoning
AA Intelligence Index · scaled to 10
1.7
5.6
7.6
Coding
SciCode · scaled to 10
1.8
4.3
5.2
Agentic tasks
Terminal-Bench Hard · scaled to 10
0.2
3.6
4.6
Context / memory
Context window size · log-scaled
6.0
9.0
9.0
Cost efficiency
Input price ($/M tokens) · cheaper scores higher
6.2
10.0
3.2
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.

What it feels like

  • Adds 1M-token context to the Opus 4.5 baseline.
  • Best-in-class on real-world coding — 80.9% on SWE-bench Verified, ahead of Gemini 3 Pro and GPT-5.1
  • Reasoning index of 70 on Artificial Analysis — biggest single-jump from any prior Anthropic model
  • Testers say it 'just gets it' on multi-system bugs without hand-holding

Best use cases

  • Software engineering on large, real codebases (SWE-bench leader)
  • Multi-step agentic workflows with tool use (τ2-bench, MCP Atlas)
  • Computer-use / OSWorld automation tasks where Opus 4.5 leads by a wide margin

Tools to try

Not ideal for

  • High-volume, latency-sensitive workloads — use Sonnet 4.5 or Haiku 4.5
  • Simple chat or casual Q&A — overkill

Model Evolution

claude-opus is Anthropic's language model family.

View full evolution tree →