💰 Unit Cost Winner DeepSeek R1 $0.55/1M in · $2.19/1M out
⚡ Latency & Speed Winner Claude 3.5 Sonnet 310ms TTFT · 80 tok/s
💻 Coding (SWE) Winner DeepSeek R1 49.2% SWE-bench · 92.8% HumanEval
🧠 STEM Reasoning Winner DeepSeek R1 71.5% GPQA · 97.3% MATH-500

Which model is better: Claude 3.5 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 3.5 Sonnet delivers lower response latency at 310ms TTFT (1140ms faster than DeepSeek R1) with 80 vs. 58 tok/s throughput. DeepSeek R1 leads coding evaluations at 49.2% SWE-bench Verified vs. 49.0% for Claude 3.5 Sonnet and 71.5% vs. 59.4% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

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 3.5 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 (8k out) 131k (33k out) Claude 3.5 Sonnet (1.6x larger context)
Avg TTFT Response Latency 310ms 1450ms Claude 3.5 Sonnet (1140ms faster)
Generation Speed (Throughput) 80 tok/s 58 tok/s Claude 3.5 Sonnet (+22 tok/s faster)
SWE-bench Verified (Coding) 49% 49.2% DeepSeek R1 (+0.2% lead)
GPQA Diamond (PhD STEM) 59.4% 71.5% DeepSeek R1 (+12.1% lead)
MMLU-Pro (Multi-Discipline) 78% 84% DeepSeek R1 (+6.0% lead)
MATH-500 (Quantitative Logic) 78.3% 97.3% DeepSeek R1 (+19.0% lead)
Inference Value Score (IVR) 46.2 / 100 64.8 / 100 DeepSeek R1 (+18.6 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

Claude 3.5 Sonnet $116.25

$3.00/1M in · $15.00/1M out

Prompt Cache: $0.300/1M read · $3.75/1M write

DeepSeek R1 $19.58

$0.55/1M in · $2.19/1M out

Prompt Cache: $0.140/1M read · $0.55/1M write

Projected Monthly Cost Reduction
$96.68 / mo
(83.2% lower cost with DeepSeek R1)
Open Full Matchup Analysis →
A

When to Choose Claude 3.5 Sonnet

Choose Claude 3.5 Sonnet (Anthropic) for legacy enterprise workflows, code review pipelines, and human-like content generation — offering a 204k context window, 310ms TTFT, and $3.00/1M input pricing ($0.300/1M cached reads).

B

When to 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).

Related Questions & Decision Factors

Which is cheaper: Claude 3.5 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 3.5 Sonnet.

Which model has lower TTFT latency and higher tok/s throughput: Claude 3.5 Sonnet or DeepSeek R1?

Claude 3.5 Sonnet delivers faster first-token latency at 310ms TTFT (80 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.

How do Claude 3.5 Sonnet and DeepSeek R1 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Claude 3.5 Sonnet scores 49% versus 49.2% for DeepSeek R1. On GPQA Diamond graduate-level STEM reasoning, Claude 3.5 Sonnet scores 59.4% versus 71.5% for DeepSeek R1 (and 78.3% vs 97.3% on MATH-500).

When should you choose Claude 3.5 Sonnet?

Choose Claude 3.5 Sonnet (Anthropic) for legacy enterprise workflows, code review pipelines, and human-like content generation — offering a 204k context window, 310ms 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).

Explore High-Priority Related Model Matchups