Claude 3.7 Sonnet
Proprietary CommercialDeveloped by Anthropic · Released 2025-01-01
What are the token costs and operational benchmarks for Claude 3.7 Sonnet?
Architectural Overview
Frontier foundation model developed by Anthropic featuring Hybrid Reasoning architecture.
Optimal Production Use Cases
Agentic coding, deep hybrid reasoning with controllable thinking budgets, and end-to-end software engineering workflows.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$3/1M in · $15/1M out
$0.18/1M in · $0.72/1M out
Head-to-Head Comparisons Involving Claude 3.7 Sonnet
Claude 3.7 Sonnet vs Claude 3.5 Haiku
Claude 3.5 Haiku is 73.3% cheaper for input tokens ($0.80 vs. $3.00 per 1M tokens) and $4.00 vs. $15.00 for output tokens (3.8x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (510ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.7 Sonnet vs Claude 3.5 Sonnet
Both models share identical input pricing at $3.00 per 1M tokens. In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (330ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.7 Sonnet vs Claude Opus 5
Claude 3.7 Sonnet is 40.0% cheaper for input tokens ($3.00 vs. $5.00 per 1M tokens) and $15.00 vs. $25.00 for output tokens (1.7x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (310ms faster than Claude 3.7 Sonnet). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs Codestral 25.01
Codestral 25.01 is 90.0% cheaper for input tokens ($0.30 vs. $3.00 per 1M tokens) and $0.90 vs. $15.00 for output tokens (10.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (500ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.2% for Codestral 25.01.
Claude 3.7 Sonnet vs Composer 2.5
Composer 2.5 is 40.0% cheaper for input tokens ($1.80 vs. $3.00 per 1M tokens) and $7.20 vs. $15.00 for output tokens (1.7x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (470ms faster than Claude 3.7 Sonnet). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs DeepSeek-R1
DeepSeek-R1 is 81.7% cheaper for input tokens ($0.55 vs. $3.00 per 1M tokens) and $2.19 vs. $15.00 for output tokens (5.5x cost difference). In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (1150ms faster than DeepSeek-R1). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 49.2% for DeepSeek-R1.
Claude 3.7 Sonnet vs DeepSeek-V3
DeepSeek-V3 is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.28 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (310ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 42.0% for DeepSeek-V3.
Claude 3.7 Sonnet vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.56 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (500ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
Claude 3.7 Sonnet vs Fable 5
Fable 5 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $8.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (410ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 58.0% for Fable 5.
Claude 3.7 Sonnet vs Gemini 2.0 Flash
Gemini 2.0 Flash is 96.7% cheaper for input tokens ($0.10 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (30.0x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (270ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Claude 3.7 Sonnet vs Gemini 3.7 Flash
Gemini 3.7 Flash is 97.3% cheaper for input tokens ($0.08 vs. $3.00 per 1M tokens) and $0.32 vs. $15.00 for output tokens (37.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (575ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 68.2% for Gemini 3.7 Flash.
Claude 3.7 Sonnet vs GLM 5.3 Flash
GLM 5.3 Flash is 95.0% cheaper for input tokens ($0.15 vs. $3.00 per 1M tokens) and $0.50 vs. $15.00 for output tokens (20.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (520ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 54.2% for GLM 5.3 Flash.
Claude 3.7 Sonnet vs OpenAI GPT-4o
OpenAI GPT-4o is 16.7% cheaper for input tokens ($2.50 vs. $3.00 per 1M tokens) and $10.00 vs. $15.00 for output tokens (1.2x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (370ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Claude 3.7 Sonnet vs GPT-5.6 Luna
GPT-5.6 Luna is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.72 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (560ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Claude 3.7 Sonnet vs GPT-5.6 Sol
Claude 3.7 Sonnet is 62.5% cheaper for input tokens ($3.00 vs. $8.00 per 1M tokens) and $15.00 vs. $32.00 for output tokens (2.7x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs GPT-5.6 Terra
GPT-5.6 Terra is 50.0% cheaper for input tokens ($1.50 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (2.0x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (440ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 65.4% for GPT-5.6 Terra.
Claude 3.7 Sonnet vs Grok 3
Both models share identical input pricing at $3.00 per 1M tokens. In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (200ms faster than Grok 3). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 58.5% for Grok 3.
Claude 3.7 Sonnet vs xAI Grok 4.6
xAI Grok 4.6 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (370ms faster than Claude 3.7 Sonnet). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Claude 3.7 Sonnet vs Mistral Large 2
Mistral Large 2 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Mistral Large 2 delivers faster response latency with 550ms TTFT (100ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 39.0% for Mistral Large 2.
Claude 3.7 Sonnet vs OpenAI o1
Claude 3.7 Sonnet is 80.0% cheaper for input tokens ($3.00 vs. $15.00 per 1M tokens) and $15.00 vs. $60.00 for output tokens (5.0x cost difference). In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (200ms faster than OpenAI o1). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 48.9% for OpenAI o1.
Claude 3.7 Sonnet vs o3-mini
o3-mini is 63.3% cheaper for input tokens ($1.10 vs. $3.00 per 1M tokens) and $4.40 vs. $15.00 for output tokens (2.7x cost difference). In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (550ms faster than o3-mini). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 49.3% for o3-mini.
Claude 3.7 Sonnet vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.36 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (540ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Claude 3.7 Sonnet vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (8.6x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Claude 3.7 Sonnet vs Qwen 2.5 Max
Qwen 2.5 Max is 90.7% cheaper for input tokens ($0.28 vs. $3.00 per 1M tokens) and $0.84 vs. $15.00 for output tokens (10.7x cost difference). In terms of operational performance, Qwen 2.5 Max delivers faster response latency with 480ms TTFT (170ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Claude 3.7 Sonnet vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.48 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (530ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Frequently Asked Questions & Query Fan-Out
How much does Claude 3.7 Sonnet cost per 1M tokens?
Claude 3.7 Sonnet costs $3.00 per million prompt (input) tokens and $15.00 per million completion (output) tokens.
What is the context window for Claude 3.7 Sonnet?
Claude 3.7 Sonnet supports a maximum context window of 204,800 tokens, with a maximum single-generation output of 64,000 tokens.
What are the primary use cases for Claude 3.7 Sonnet?
Agentic coding, deep hybrid reasoning with controllable thinking budgets, and end-to-end software engineering workflows.