💰 Unit Cost Winner DeepSeek R1 $0.55/1M in · $2.19/1M out
⚡ Latency & Speed Winner Claude 4 Opus 920ms TTFT · 46 tok/s
💻 Coding (SWE) Winner Claude 4 Opus 77.2% SWE-bench · 96.8% HumanEval
🧠 STEM Reasoning Winner Claude 4 Opus 87.1% GPQA · 98.1% MATH-500

Which model is better: Claude 4 Opus or DeepSeek R1?

DeepSeek R1 is 96.3% cheaper for input tokens ($0.55 vs. $15.00 per 1M tokens) and $2.19 vs. $75.00 for output tokens (27.3x cost advantage). In terms of operational performance, Claude 4 Opus delivers lower response latency at 920ms TTFT (530ms faster than DeepSeek R1) with 46 vs. 58 tok/s throughput. Claude 4 Opus leads coding evaluations at 77.2% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 87.1% vs. 71.5% 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 4 Opus DeepSeek R1 Net Delta / Advantage
Input Cost / 1M Tokens $15.00 $0.55 DeepSeek R1 is 27.3x cheaper
Output Cost / 1M Tokens $75.00 $2.19 DeepSeek R1 (97.1% lower)
Prompt Cache Read / 1M $1.500 $0.140 DeepSeek R1 is 10.7x cheaper
Prompt Cache Write / 1M $18.75 $0.55 DeepSeek R1 is 34.1x cheaper
Context Window (Max Output) 205k (64k out) 131k (33k out) Claude 4 Opus (1.6x larger context)
Avg TTFT Response Latency 920ms 1450ms Claude 4 Opus (530ms faster)
Generation Speed (Throughput) 46 tok/s 58 tok/s DeepSeek R1 (+12 tok/s faster)
SWE-bench Verified (Coding) 77.2% 49.2% Claude 4 Opus (+28.0% lead)
GPQA Diamond (PhD STEM) 87.1% 71.5% Claude 4 Opus (+15.6% lead)
MMLU-Pro (Multi-Discipline) 91.8% 84% Claude 4 Opus (+7.8% lead)
MATH-500 (Quantitative Logic) 98.1% 97.3% Claude 4 Opus (+0.8% lead)
Inference Value Score (IVR) 43.6 / 100 64.8 / 100 DeepSeek R1 (+21.2 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 4 Opus $581.25

$15.00/1M in · $75.00/1M out

Prompt Cache: $1.500/1M read · $18.75/1M 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
$561.68 / mo
(96.6% lower cost with DeepSeek R1)
Open Full Matchup Analysis →
A

When to Choose Claude 4 Opus

Choose Claude 4 Opus (Anthropic) for mission-critical R&D, long-horizon autonomous coding tasks, complex theorem proving, and executive strategic synthesis — offering a 204k context window, 920ms TTFT, and $15.00/1M input pricing ($1.500/1M 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 4 Opus or DeepSeek R1?

DeepSeek R1 is cheaper at $0.55/1M input tokens ($2.19/1M output) compared to $15.00/1M input ($75.00/1M output) for Claude 4 Opus.

Which model has lower TTFT latency and higher tok/s throughput: Claude 4 Opus or DeepSeek R1?

Claude 4 Opus delivers faster first-token latency at 920ms TTFT (46 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.

How do Claude 4 Opus and DeepSeek R1 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Claude 4 Opus scores 77.2% versus 49.2% for DeepSeek R1. On GPQA Diamond graduate-level STEM reasoning, Claude 4 Opus scores 87.1% versus 71.5% for DeepSeek R1 (and 98.1% vs 97.3% on MATH-500).

When should you choose Claude 4 Opus?

Choose Claude 4 Opus (Anthropic) for mission-critical R&D, long-horizon autonomous coding tasks, complex theorem proving, and executive strategic synthesis — offering a 204k context window, 920ms TTFT, and $15.00/1M input pricing ($1.500/1M 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).

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