Which model is better: Gemini 2.5 Pro or Mistral Large 2 (2411)?
Gemini 2.5 Pro is 37.5% cheaper for input tokens ($1.25 vs. $2.00 per 1M tokens) and $10.00 vs. $6.00 for output tokens (1.6x cost advantage). In terms of operational performance, Mistral Large 2 (2411) delivers lower response latency at 380ms TTFT (320ms faster than Gemini 2.5 Pro) with 75 vs. 80 tok/s throughput. Gemini 2.5 Pro leads coding evaluations at 63.8% SWE-bench Verified vs. 45.2% for Mistral Large 2 (2411) and 74.0% 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
Gemini 2.5 Pro
Mistral Large 2 (2411)
Net Delta / Advantage
Input Cost / 1M Tokens
$1.25
$2.00
Gemini 2.5 Pro is 1.6x cheaper
Output Cost / 1M Tokens
$10.00
$6.00
Mistral Large 2 (2411) (40.0% lower)
Prompt Cache Read / 1M
$0.313
$0.200
Mistral Large 2 (2411) is 1.6x cheaper
Prompt Cache Write / 1M
$1.25
$2.00
Gemini 2.5 Pro is 1.6x cheaper
Context Window (Max Output)
1,024k (66k out)
131k (8k out)
Gemini 2.5 Pro (7.8x larger context)
Avg TTFT Response Latency
700ms
380ms
Mistral Large 2 (2411) (320ms faster)
Generation Speed (Throughput)
80 tok/s
75 tok/s
Gemini 2.5 Pro (+5 tok/s faster)
SWE-bench Verified (Coding)
63.8%
45.2%
Gemini 2.5 Pro (+18.6% lead)
GPQA Diamond (PhD STEM)
74%
54.2%
Gemini 2.5 Pro (+19.8% lead)
MMLU-Pro (Multi-Discipline)
85.2%
74.8%
Gemini 2.5 Pro (+10.4% lead)
MATH-500 (Quantitative Logic)
95%
76.5%
Gemini 2.5 Pro (+18.5% lead)
Inference Value Score (IVR)
56.8 / 100
50.1 / 100
Gemini 2.5 Pro (+6.7 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M cached reads).
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: Gemini 2.5 Pro or Mistral Large 2 (2411)?
Gemini 2.5 Pro is cheaper at $1.25/1M input tokens ($10.00/1M 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: Gemini 2.5 Pro or Mistral Large 2 (2411)?
Mistral Large 2 (2411) delivers faster first-token latency at 380ms TTFT (75 tok/s throughput) compared to 700ms TTFT (80 tok/s) for Gemini 2.5 Pro.
How do Gemini 2.5 Pro and Mistral Large 2 (2411) compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 2.5 Pro scores 63.8% versus 45.2% for Mistral Large 2 (2411). On GPQA Diamond graduate-level STEM reasoning, Gemini 2.5 Pro scores 74% versus 54.2% for Mistral Large 2 (2411) (and 95% vs 76.5% on MATH-500).
When should you choose Gemini 2.5 Pro?
Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M 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).
