Claude Sonnet 4.6 Thinking
Claude Sonnet 4.6 with extended thinking enabled for tougher coding, planning, and multi‑tool tasks. Ideal for long‑horizon agent workflows and complex problem solving.
- Reasoning
- Vision
- Native PDF input
- Tool Calling
- Structured Output
Added Feb 17, 2026
Pricing
Auto routing · per 1M tokens- Input
- $3.00
- Output
- $15.00
- Cache read
- $0.30
Specifications
- Context window
- 1M
- Max output
- 128K
- Avg output (7d)
- 753 tokens
- Longer than 63% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
30.1
Coding Index
63.0
Agentic Index
31.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
20.1%
Better than 40% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
3.7%
Better than 42% of models compared
AA-Briefcase v1.1
Agentic knowledge work (Elo)
1063 Elo
Better than 52% of models compared
GDPval-AA v2.1
Economically valuable tasks (Elo)
1232 Elo
Better than 61% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
15.8%
Better than 59% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
80.0%
Better than 86% of models compared
MLCR-AA
Medical long-context reasoning
7.8%
Better than 24% of models compared
Reasoning
HLE
Humanity's Last Exam
33.6%
Better than 80% of models compared
IFBench
Instruction-following benchmark
56.6%
Better than 67% of models compared
CritPt
Research-level physics reasoning
3.1%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
3.0%
Better than 51% of models compared
SciCode
Python programming for scientific computing
50.1%
Better than 53% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
40.9%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
48.4%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
87.5%
Better than 84% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
53.0%
Better than 96% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
75.7%
Better than 67% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
80.0%
Better than 86% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
36.6%
Last updated Oct 22, 2026
Artificial AnalysisProviders
Auto routing is available for this model. Explicit provider selection is not available.
Loading provider options…
Reliability
Last 3 days