Which model is better: Claude 3.7 Sonnet or 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 advantage). In terms of operational performance, Claude 3.7 Sonnet delivers lower response latency at 540ms TTFT (910ms faster than DeepSeek R1) with 72 vs. 58 tok/s throughput. Claude 3.7 Sonnet leads coding evaluations at 70.3% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 78.5% vs. 71.5% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
Claude 3.7 Sonnet
DeepSeek R1
Net Delta / Advantage
Input Cost / 1M Tokens
$3.00
$0.55
DeepSeek R1 is 5.5x cheaper
Output Cost / 1M Tokens
$15.00
$2.19
DeepSeek R1 (85.4% lower)
Prompt Cache Read / 1M
$0.300
$0.140
DeepSeek R1 is 2.1x cheaper
Prompt Cache Write / 1M
$3.75
$0.55
DeepSeek R1 is 6.8x cheaper
Context Window (Max Output)
205k (64k out)
131k (33k out)
Claude 3.7 Sonnet (1.6x larger context)
Avg TTFT Response Latency
540ms
1450ms
Claude 3.7 Sonnet (910ms faster)
Generation Speed (Throughput)
72 tok/s
58 tok/s
Claude 3.7 Sonnet (+14 tok/s faster)
SWE-bench Verified (Coding)
70.3%
49.2%
Claude 3.7 Sonnet (+21.1% lead)
GPQA Diamond (PhD STEM)
78.5%
71.5%
Claude 3.7 Sonnet (+7.0% lead)
MMLU-Pro (Multi-Discipline)
88%
84%
Claude 3.7 Sonnet (+4.0% lead)
MATH-500 (Quantitative Logic)
96.2%
97.3%
DeepSeek R1 (+1.1% lead)
Inference Value Score (IVR)
54.3 / 100
64.8 / 100
DeepSeek R1 (+10.5 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Which is cheaper: Claude 3.7 Sonnet or DeepSeek R1?
DeepSeek R1 is cheaper at $0.55/1M input tokens ($2.19/1M output) compared to $3.00/1M input ($15.00/1M output) for Claude 3.7 Sonnet.
Which model has lower TTFT latency and higher tok/s throughput: Claude 3.7 Sonnet or DeepSeek R1?
Claude 3.7 Sonnet delivers faster first-token latency at 540ms TTFT (72 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do Claude 3.7 Sonnet and DeepSeek R1 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude 3.7 Sonnet scores 70.3% versus 49.2% for DeepSeek R1. On GPQA Diamond graduate-level STEM reasoning, Claude 3.7 Sonnet scores 78.5% versus 71.5% for DeepSeek R1 (and 96.2% vs 97.3% on MATH-500).
When should you choose Claude 3.7 Sonnet?
Choose Claude 3.7 Sonnet (Anthropic) for full-stack repository development, complex code refactoring, and agentic tool use with predictable prompt cache economics — offering a 204k context window, 540ms TTFT, and $3.00/1M input pricing ($0.300/1M cached reads).
When should you choose DeepSeek R1?
Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M cached reads).
