Mistral Large 2 vs Particle-1
Particle-1 leads the LLM Stats Score 31.9 to 6.9. Particle-1 is 6.9x cheaper per token.
Mistral AI · Particle AI · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 31.9 to 6.9, ranking #133 overall.
In the 1 individual benchmarks reported for both models, Particle-1 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Particle-1 is roughly 6.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Particle-1 also accepts a larger context window (1,048,576 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 Mistral Large 2
- you need open weights you can self-host or fine-tune
Choose Particle-1
- overall performance matters — it scores 31.9 and ranks #133 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 6.9x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
5 reported for Mistral Large 2 · 3 for Particle-1
Mistral Large 2 outperforms in 0 benchmarks, while Particle-1 is better at 1 benchmark (MMLU).
Particle-1 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, Mistral Large 2 ($2.00/1M tokens) is 8.0x more expensive than Particle-1 ($0.25/1M tokens).
For output processing, Mistral Large 2 ($6.00/1M tokens) is 6.0x more expensive than Particle-1 ($1.00/1M tokens).
In conclusion, Mistral Large 2 is more expensive than Particle-1.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Particle-1 accepts 1,048,576 input tokens compared to Mistral Large 2's 128,000 tokens. Particle-1 can generate longer responses up to 131,072 tokens, while Mistral Large 2 is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Particle-1 supports multimodal inputs, whereas Mistral Large 2 does not.
Particle-1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Mistral Large 2
Particle-1
License
Usage and distribution terms
Mistral Large 2 is licensed under Mistral Research License, while Particle-1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Mistral Research License
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Mistral Large 2 was released on 2024-07-24, while Particle-1 was released on 2026-10-28.
Particle-1 is 28 months newer than Mistral Large 2.
Jul 24, 2024
2.3 years ago
Oct 28, 2026
-2 days ago
2.3yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Mistral Large 2 is available from Google, Mistral AI. Particle-1 is available from Particle AI.
Mistral Large 2
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Large 2 and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Large 2 vs Particle-1.