Mistral Small 4 vs Particle-1
Particle-1 leads the LLM Stats Score 32.0 to 18.2. Mistral Small 4 is 1.7x cheaper per token.
Mistral AI · Particle AI · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 32.0 to 18.2, ranking #135 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, Mistral Small 4 is roughly 1.7x 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 4
- cost matters — it's about 1.7x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Particle-1
- overall performance matters — it scores 32.0 and ranks #135 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
- 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
9 reported for Mistral Small 4 · 3 for Particle-1
Mistral Small 4 outperforms in 0 benchmarks, while Particle-1 is better at 1 benchmark (GPQA).
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 4 ($0.15/1M tokens) is 1.7x cheaper than Particle-1 ($0.25/1M tokens).
For output processing, Mistral Small 4 ($0.60/1M tokens) is 1.7x cheaper than Particle-1 ($1.00/1M tokens).
In conclusion, Particle-1 is more expensive than Mistral Small 4.*
* 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 4's 256,000 tokens. Mistral Small 4 can generate longer responses up to 256,000 tokens, while Particle-1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both Mistral Small 4 and Particle-1 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Mistral Small 4
Particle-1
License
Usage and distribution terms
Mistral Small 4 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 4 was released on 2026-03-16, while Particle-1 was released on 2026-10-28.
Particle-1 is 8 months newer than Mistral Small 4.
Mar 16, 2026
7 months ago
Oct 28, 2026
1 weeks ago
7mo 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 4 is available from Mistral AI. Particle-1 is available from Particle AI.
Mistral Small 4
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Small 4 and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Small 4 vs Particle-1.