DeepSeek-V2.5 vs Pixtral-12B
DeepSeek-V2.5 leads the LLM Stats Score 8.4 to -1.4. Pixtral-12B is 1.2x cheaper per token.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V2.5 leads the overall LLM Stats Score 8.4 to -1.4, ranking #265 overall.
In the 4 individual benchmarks reported for both models, DeepSeek-V2.5 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Pixtral-12B is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Pixtral-12B 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
- overall performance matters — it scores 8.4 and ranks #265 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
Choose Pixtral-12B
- cost matters — it's about 1.2x cheaper per token
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Sep 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 · 12 for Pixtral-12B
DeepSeek-V2.5 outperforms in 4 benchmarks (HumanEval, MATH, MMLU, MT-Bench), while Pixtral-12B is better at 0 benchmarks.
DeepSeek-V2.5 significantly outperforms across most 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 1.1x cheaper than Pixtral-12B ($0.15/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.9x more expensive than Pixtral-12B ($0.15/1M tokens).
In conclusion, DeepSeek-V2.5 is more expensive than Pixtral-12B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V2.5 has 223.6B more parameters than Pixtral-12B, making it 1803.2% larger.
Context Window
Maximum input and output token capacity
Pixtral-12B accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Both models can generate responses up to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Pixtral-12B supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Pixtral-12B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Pixtral-12B
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Pixtral-12B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Pixtral-12B was released on 2024-09-17.
Pixtral-12B is 4 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Sep 17, 2024
2.0 years ago
4mo 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-12B is available from Mistral AI.
DeepSeek-V2.5
Pixtral-12B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Pixtral-12B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Pixtral-12B.