Anthropic · frontier · Released 2026-03-27

Claude 3.7 Sonnet Reasoning: pricing, context window and benchmarks

Claude 3.7 Sonnet Reasoning is a frontier model from Anthropic. It offers a 128,000-token context window, costs $16.30 per million input tokens and $77.62 per million output tokens, and averages 83.7 across the index's benchmark suite.

frontierreasoningcodingvisionmultimodal
Input price
$16.30
per 1M tokens
Output price
$77.62
per 1M tokens
Context
128,000
tokens
Avg score
83.7
rank #65 of 600
Arena Elo
1464.4
modelled
Max output
128,000
tokens

Pricing

Claude 3.7 Sonnet Reasoning costs $16.30 per million input tokens and $77.62 per million output tokens. A blended 3:1 input-to-output mix works out to about $31.63 per million tokens. Cached input is listed at $2.07 per million tokens.

ItemPriceNotes
Input$16.30per 1M tokens
Output$77.62per 1M tokens
Cached input$2.07per 1M tokens
Blended 3:1$31.63estimate per 1M tokens
1M in + 1M out$93.92estimate

Benchmarks

Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.

BenchmarkScoreRankPercentileRelative
MMLU-Pro 86.4 45 93th
GPQA Diamond 81.3 39 94th
SWE-bench Verified 71.0 95 84th
HumanEval 89.2 57 91th
MATH-500 90.3 45 93th
AIME 2025 79.8 92 85th
LiveCodeBench 79.6 48 92th
IFEval 90.1 69 89th
TruthfulQA 86.2 47 92th
HellaSwag 90.4 75 88th
MMMU 76.5 122 80th
Arena Elo 1464.4 44 93th

What Claude 3.7 Sonnet Reasoning is good for

Claude 3.7 Sonnet Reasoning sits in the frontier tier of the index. It is aimed at work where answer quality matters more than unit cost: hard reasoning, multi-step coding, document analysis and multimodal inputs. At $16.30 per million input tokens and a 128,000-token context window, it is a premium option, so reserve it for tasks where a cheaper model measurably fails.

Strengths

  • Top-tier average benchmark score (83.7), placing it #65 of 600.
  • Large 128,000-token context window for long documents and agent traces.
  • Accepts more than text, including image input where the provider supports it.
  • Reasoning-oriented profile, useful for multi-step problems and structured analysis.
  • Coding benchmarks are a relative strength within its tier.

Trade-offs

  • Proprietary licence: you cannot download the weights or self-host the model.
  • Premium input pricing at $16.30 per million tokens.
  • Cost can climb quickly on long outputs and high-volume traffic.

How it compares

ModelProviderContextInput / 1MAvg score
Claude Haiku 4.5 Max Anthropic 200000 $15.98 83.8
Grok 3 Pro xAI 400000 $16.61 83.8
Claude 3.7 Sonnet Max Anthropic 128000 $16.88 83.7
Claude 3.7 Sonnet Reasoning Anthropic 128000 $16.30 83.7
DeepSeek-V3.2 Instruct DeepSeek 128000 $5.91 83.7
Gemini 3 Flash Reasoning Google DeepMind 128000 $17.05 83.7

Browse all comparisons →

FAQ

How much does Claude 3.7 Sonnet Reasoning cost?

Claude 3.7 Sonnet Reasoning is listed at $16.30 per million input tokens and $77.62 per million output tokens. Cached input is $2.07 per million tokens. A blended 3:1 input-to-output mix is about $31.63 per million tokens.

What is the context window of Claude 3.7 Sonnet Reasoning?

Claude 3.7 Sonnet Reasoning has a context window of 128,000 tokens and can emit up to 128,000 output tokens per request in this index.

Is Claude 3.7 Sonnet Reasoning open weights?

No. Claude 3.7 Sonnet Reasoning is a proprietary model with a proprietary licence, so you cannot download the weights.

How does Claude 3.7 Sonnet Reasoning score on benchmarks?

Claude 3.7 Sonnet Reasoning averages 83.7 across the scored benchmarks in this index, ranking #65 of 600 models. Its strongest relative result is Arena Elo at 1464.4.

What is Claude 3.7 Sonnet Reasoning best for?

Claude 3.7 Sonnet Reasoning sits in the frontier tier of the index. It is aimed at work where answer quality matters more than unit cost: hard reasoning, multi-step coding, document analysis and multimodal inputs. At $16.30 per million input tokens and a 128,000-token context window, it is a premium option, so reserve it for tasks where a cheaper model measurably fails.

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