Which model is better: Mistral Large 2 (2411) or OpenAI o4-mini?
OpenAI o4-mini is 45.0% cheaper for input tokens ($1.10 vs. $2.00 per 1M tokens) and $4.40 vs. $6.00 for output tokens (1.8x cost advantage). In terms of operational performance, Mistral Large 2 (2411) delivers lower response latency at 380ms TTFT (300ms faster than OpenAI o4-mini) with 75 vs. 125 tok/s throughput. OpenAI o4-mini leads coding evaluations at 68.1% SWE-bench Verified vs. 45.2% for Mistral Large 2 (2411) and 81.4% 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
Mistral Large 2 (2411)
OpenAI o4-mini
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$1.10
OpenAI o4-mini is 1.8x cheaper
Output Cost / 1M Tokens
$6.00
$4.40
OpenAI o4-mini (26.7% lower)
Prompt Cache Read / 1M
$0.200
$0.275
Mistral Large 2 (2411) is 1.4x cheaper
Prompt Cache Write / 1M
$2.00
$1.10
OpenAI o4-mini is 1.8x cheaper
Context Window (Max Output)
131k (8k out)
205k (100k out)
OpenAI o4-mini (1.6x larger context)
Avg TTFT Response Latency
380ms
680ms
Mistral Large 2 (2411) (300ms faster)
Generation Speed (Throughput)
75 tok/s
125 tok/s
OpenAI o4-mini (+50 tok/s faster)
SWE-bench Verified (Coding)
45.2%
68.1%
OpenAI o4-mini (+22.9% lead)
GPQA Diamond (PhD STEM)
54.2%
81.4%
OpenAI o4-mini (+27.2% lead)
MMLU-Pro (Multi-Discipline)
74.8%
85.6%
OpenAI o4-mini (+10.8% lead)
MATH-500 (Quantitative Logic)
76.5%
97.8%
OpenAI o4-mini (+21.3% lead)
Inference Value Score (IVR)
50.1 / 100
63.7 / 100
OpenAI o4-mini (+13.6 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: Mistral Large 2 (2411) or OpenAI o4-mini?
OpenAI o4-mini is cheaper at $1.10/1M input tokens ($4.40/1M 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: Mistral Large 2 (2411) or OpenAI o4-mini?
Mistral Large 2 (2411) delivers faster first-token latency at 380ms TTFT (75 tok/s throughput) compared to 680ms TTFT (125 tok/s) for OpenAI o4-mini.
How do Mistral Large 2 (2411) and OpenAI o4-mini compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Mistral Large 2 (2411) scores 45.2% versus 68.1% for OpenAI o4-mini. On GPQA Diamond graduate-level STEM reasoning, Mistral Large 2 (2411) scores 54.2% versus 81.4% for OpenAI o4-mini (and 76.5% vs 97.8% on MATH-500).
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).
When should you choose OpenAI o4-mini?
Choose OpenAI o4-mini (OpenAI) for automated PR review, high-volume agentic coding loops, SQL/data analysis, and visual document reasoning — offering a 204k context window, 680ms TTFT, and $1.10/1M input pricing ($0.275/1M cached reads).
