Claude Sonnet 4.5 Thinking
Adds extended, step‑by‑step reasoning for tougher coding, planning, and multi‑tool tasks. Ideal for long‑horizon agent workflows, complex problem solving, and scenarios that benefit from explicit thinking traces.
- Reasoning
- Vision
- Native PDF input
- Tool Calling
- Structured Output
Added Sep 29, 2025
Pricing
Auto routing · per 1M tokens- Input
- $3.00
- Output
- $15.00
- Cache read
- $0.30
Specifications
- Context window
- 200K
- Max output
- 64K
- Avg output (7d)
- 1.6K tokens
- Longer than 86% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
20.7
Coding Index
52.1
Agentic Index
15.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
14.1%
Better than 38% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
713 Elo
Better than 30% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
893 Elo
Better than 41% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
5.2%
Better than 25% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
72.3%
Better than 68% of models compared
Reasoning
HLE
Humanity's Last Exam
17.8%
Better than 65% of models compared
IFBench
Instruction-following benchmark
57.3%
Better than 68% of models compared
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 16% of models compared
SciCode
Python programming for scientific computing
45.7%
Better than 41% of models compared
LiveCodeBench
Contamination-free coding benchmark
71.4%
Better than 82% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
88.0%
Better than 86% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
87.5%
Better than 98% of models compared
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
83.4%
Better than 73% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
35.6%
Better than 82% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
78.1%
Better than 68% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
72.3%
Better than 68% of models compared
Last updated Oct 1, 2026
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