LANGUAGE MODEL Anthropic Last updated:

Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)

Claude Sonnet 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
58 tok/s output
Cost
High
$3.75 in / $15.00 out
Context
1M
Up to 1,000,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/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
7.4
Coding
SciCode · scaled to 10
1.8
4.3
4.7
Agentic tasks
Terminal-Bench Hard · scaled to 10
0.2
3.6
5.3
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
4.5
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

  • Topped SWE-bench Verified at release — Cognition reported 18% planning gain and 12% end-to-end eval gain for Devin
  • Edit accuracy is the headline change — internal code-editing error rate dropped from 9% on Sonnet 4 to 0%
  • Sustains 30+ hour autonomous coding focus without losing the thread

Best use cases

  • Production agentic coding (Devin, Cursor, Cline) where reliability matters
  • AI SRE / on-call automation with long task horizons
  • Day-to-day software engineering at scale on real GitHub issues

Tools to try

Not ideal for

  • The very hardest reasoning tasks — Opus 4.5 still leads by ~7 points on AA Intelligence Index
  • Simple chat or casual Q&A — try Haiku 4.5 instead

Model Evolution

claude-sonnet is Anthropic's language model family.

View full evolution tree →