DeepSeek-V2.5 vs Mistral Large 2
DeepSeek-V2.5 and Mistral Large 2 are closely matched at 8.8 and 8.1 on the LLM Stats Score. DeepSeek-V2.5 is 17.1x cheaper per token.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V2.5 and Mistral Large 2 are closely matched on the overall LLM Stats Score at 8.8 and 8.1.
The models split the 4 individual benchmarks reported for both models evenly.
On price, DeepSeek-V2.5 is roughly 17.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral Large 2 also accepts a larger context window (128,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V2.5
- cost matters — it's about 17.1x cheaper per token
Choose Mistral Large 2
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Jul 2024
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 5 for Mistral Large 2
DeepSeek-V2.5 outperforms in 2 benchmarks (GSM8k, MT-Bench), while Mistral Large 2 is better at 2 benchmarks (HumanEval, MMLU).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 14.3x cheaper than Mistral Large 2 ($2.00/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 21.4x cheaper than Mistral Large 2 ($6.00/1M tokens).
In conclusion, Mistral Large 2 is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V2.5 has 113.0B more parameters than Mistral Large 2, making it 91.9% larger.
Context Window
Maximum input and output token capacity
Mistral Large 2 accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Mistral Large 2 can generate longer responses up to 128,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Mistral Large 2 uses Mistral Research License.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Mistral Research License
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Mistral Large 2 was released on 2024-07-24.
Mistral Large 2 is 3 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Jul 24, 2024
2.1 years ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Mistral Large 2 is available from Google, Mistral AI.
DeepSeek-V2.5
Mistral Large 2
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Mistral Large 2 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Mistral Large 2.