DeepSeek-V2.5 vs Pixtral Large
DeepSeek-V2.5 and Pixtral Large are closely matched at 8.1 and 11.8 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 Pixtral Large are closely matched on the overall LLM Stats Score at 8.1 and 11.8.
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.
Pixtral Large 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 Pixtral Large
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Nov 2024
At a glance
The differences that matter most.
Individual benchmarks
15 reported for DeepSeek-V2.5 · 7 for Pixtral Large
DeepSeek-V2.5 and Pixtral Largedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 Pixtral Large ($2.00/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 21.4x cheaper than Pixtral Large ($6.00/1M tokens).
In conclusion, Pixtral Large 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 112.0B more parameters than Pixtral Large, making it 90.3% larger.
Context Window
Maximum input and output token capacity
Pixtral Large accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Pixtral Large can generate longer responses up to 128,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Pixtral Large supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Pixtral Large can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Pixtral Large
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Pixtral Large uses Mistral Research License (MRL) for research; Mistral Commercial License for commercial use.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Mistral Research License (MRL) for research; Mistral Commercial License for commercial use
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Pixtral Large was released on 2024-11-18.
Pixtral Large is 6 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Nov 18, 2024
1.8 years ago
6mo 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. Pixtral Large is available from Mistral AI.
DeepSeek-V2.5
Pixtral Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Pixtral Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Pixtral Large.