Mistral Small 3.1 24B Base vs Particle-1
Particle-1 leads the LLM Stats Score 31.9 to -0.1. Mistral Small 3.1 24B Base is 2.9x cheaper per token.
Mistral AI · Particle AI · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 31.9 to -0.1, ranking #133 overall.
In the 2 individual benchmarks reported for both models, Particle-1 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Mistral Small 3.1 24B Base is roughly 2.9x 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 Small 3.1 24B Base
- cost matters — it's about 2.9x cheaper per token
- 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 2 of 2 exact shared results
- 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 Small 3.1 24B Base · 3 for Particle-1
Mistral Small 3.1 24B Base outperforms in 0 benchmarks, while Particle-1 is better at 2 benchmarks (GPQA, 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 Small 3.1 24B Base ($0.10/1M tokens) is 2.5x cheaper than Particle-1 ($0.25/1M tokens).
For output processing, Mistral Small 3.1 24B Base ($0.30/1M tokens) is 3.3x cheaper than Particle-1 ($1.00/1M tokens).
In conclusion, Particle-1 is more expensive than Mistral Small 3.1 24B Base.*
* 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 Small 3.1 24B Base's 128,000 tokens. Particle-1 can generate longer responses up to 131,072 tokens, while Mistral Small 3.1 24B Base is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both Mistral Small 3.1 24B Base and Particle-1 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Mistral Small 3.1 24B Base
Particle-1
License
Usage and distribution terms
Mistral Small 3.1 24B Base is licensed under Apache 2.0, while Particle-1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Mistral Small 3.1 24B Base was released on 2025-03-17, while Particle-1 was released on 2026-10-28.
Particle-1 is 20 months newer than Mistral Small 3.1 24B Base.
Mar 17, 2025
1.6 years ago
Oct 28, 2026
-3 days ago
1.6yr 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 Small 3.1 24B Base is available from Mistral AI. Particle-1 is available from Particle AI.
Mistral Small 3.1 24B Base
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Small 3.1 24B Base and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Small 3.1 24B Base vs Particle-1.