Which model is better: Claude 3.7 Sonnet or 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 advantage). In terms of operational performance, DeepSeek V3 delivers lower response latency at 410ms TTFT (130ms faster than Claude 3.7 Sonnet) with 78 vs. 72 tok/s throughput. Claude 3.7 Sonnet leads coding evaluations at 70.3% SWE-bench Verified vs. 42.0% for DeepSeek V3 and 78.5% vs. 59.1% 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 V3
Net Delta / Advantage
Input Cost / 1M Tokens
$3.00
$0.14
DeepSeek V3 is 21.4x cheaper
Output Cost / 1M Tokens
$15.00
$0.28
DeepSeek V3 (98.1% lower)
Prompt Cache Read / 1M
$0.300
$0.014
DeepSeek V3 is 21.4x cheaper
Prompt Cache Write / 1M
$3.75
$0.14
DeepSeek V3 is 26.8x cheaper
Context Window (Max Output)
205k (64k out)
131k (8k out)
Claude 3.7 Sonnet (1.6x larger context)
Avg TTFT Response Latency
540ms
410ms
DeepSeek V3 (130ms faster)
Generation Speed (Throughput)
72 tok/s
78 tok/s
DeepSeek V3 (+6 tok/s faster)
SWE-bench Verified (Coding)
70.3%
42%
Claude 3.7 Sonnet (+28.3% lead)
GPQA Diamond (PhD STEM)
78.5%
59.1%
Claude 3.7 Sonnet (+19.4% lead)
MMLU-Pro (Multi-Discipline)
88%
75.9%
Claude 3.7 Sonnet (+12.1% lead)
MATH-500 (Quantitative Logic)
96.2%
90.2%
Claude 3.7 Sonnet (+6.0% lead)
Inference Value Score (IVR)
54.3 / 100
75 / 100
DeepSeek V3 (+20.7 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 V3?
DeepSeek V3 is cheaper at $0.14/1M input tokens ($0.28/1M 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 V3?
DeepSeek V3 delivers faster first-token latency at 410ms TTFT (78 tok/s throughput) compared to 540ms TTFT (72 tok/s) for Claude 3.7 Sonnet.
How do Claude 3.7 Sonnet and DeepSeek V3 compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude 3.7 Sonnet scores 70.3% versus 42% for DeepSeek V3. On GPQA Diamond graduate-level STEM reasoning, Claude 3.7 Sonnet scores 78.5% versus 59.1% for DeepSeek V3 (and 96.2% vs 90.2% 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 V3?
Choose DeepSeek V3 (DeepSeek) for cost-sensitive batch text processing, drafting, and open-weight self-hosted inference — offering a 131k context window, 410ms TTFT, and $0.14/1M input pricing ($0.014/1M cached reads).
