Which model is better: DeepSeek R1 or Mistral Large 2 (2411)?
DeepSeek R1 is 72.5% cheaper for input tokens ($0.55 vs. $2.00 per 1M tokens) and $2.19 vs. $6.00 for output tokens (3.6x cost advantage). In terms of operational performance, Mistral Large 2 (2411) delivers lower response latency at 380ms TTFT (1070ms faster than DeepSeek R1) with 75 vs. 58 tok/s throughput. DeepSeek R1 leads coding evaluations at 49.2% SWE-bench Verified vs. 45.2% for Mistral Large 2 (2411) and 71.5% vs. 54.2% 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
DeepSeek R1
Mistral Large 2 (2411)
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$2.00
DeepSeek R1 is 3.6x cheaper
Output Cost / 1M Tokens
$2.19
$6.00
DeepSeek R1 (63.5% lower)
Prompt Cache Read / 1M
$0.140
$0.200
DeepSeek R1 is 1.4x cheaper
Prompt Cache Write / 1M
$0.55
$2.00
DeepSeek R1 is 3.6x cheaper
Context Window (Max Output)
131k (33k out)
131k (8k out)
Equal Context Window
Avg TTFT Response Latency
1450ms
380ms
Mistral Large 2 (2411) (1070ms faster)
Generation Speed (Throughput)
58 tok/s
75 tok/s
Mistral Large 2 (2411) (+17 tok/s faster)
SWE-bench Verified (Coding)
49.2%
45.2%
DeepSeek R1 (+4.0% lead)
GPQA Diamond (PhD STEM)
71.5%
54.2%
DeepSeek R1 (+17.3% lead)
MMLU-Pro (Multi-Discipline)
84%
74.8%
DeepSeek R1 (+9.2% lead)
MATH-500 (Quantitative Logic)
97.3%
76.5%
DeepSeek R1 (+20.8% lead)
Inference Value Score (IVR)
64.8 / 100
50.1 / 100
DeepSeek R1 (+14.7 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Mistral Large 2 (2411) (Mistral AI) for sovereign enterprise multilingual flagship tuned for reasoning, strict JSON schemas, and European regulatory compliance — offering a 131k context window, 380ms TTFT, and $2.00/1M input pricing ($0.200/1M cached reads).
Which is cheaper: DeepSeek R1 or Mistral Large 2 (2411)?
DeepSeek R1 is cheaper at $0.55/1M input tokens ($2.19/1M output) compared to $2.00/1M input ($6.00/1M output) for Mistral Large 2 (2411).
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or Mistral Large 2 (2411)?
Mistral Large 2 (2411) delivers faster first-token latency at 380ms TTFT (75 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and Mistral Large 2 (2411) compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 45.2% for Mistral Large 2 (2411). On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 54.2% for Mistral Large 2 (2411) (and 97.3% vs 76.5% on MATH-500).
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).
When should you choose Mistral Large 2 (2411)?
Choose Mistral Large 2 (2411) (Mistral AI) for sovereign enterprise multilingual flagship tuned for reasoning, strict JSON schemas, and European regulatory compliance — offering a 131k context window, 380ms TTFT, and $2.00/1M input pricing ($0.200/1M cached reads).
