Mistral Large 4 vs Mistral Small 3.1 24B Base
Mistral Large 4 leads the LLM Stats Score 46.2 to 0.3. Mistral Small 3.1 24B Base is 6.9x cheaper per token.
Mistral AI · Mistral AI · Updated for 2026
Which is better?
Mistral Large 4 leads the overall LLM Stats Score 46.2 to 0.3, ranking #34 overall.
On price, Mistral Small 3.1 24B Base is roughly 6.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral Large 4 also accepts a larger context window (1,000,000 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 Mistral Large 4
- overall performance matters — it scores 46.2 and ranks #34 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Mistral Small 3.1 24B Base
- cost matters — it's about 6.9x cheaper per token
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Individual benchmarks
18 reported for Mistral Large 4 · 5 for Mistral Small 3.1 24B Base
Mistral Large 4 and Mistral Small 3.1 24B Basedon'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, Mistral Large 4 ($0.68/1M tokens) is 6.8x more expensive than Mistral Small 3.1 24B Base ($0.10/1M tokens).
For output processing, Mistral Large 4 ($2.09/1M tokens) is 7.0x more expensive than Mistral Small 3.1 24B Base ($0.30/1M tokens).
In conclusion, Mistral Large 4 is more expensive than Mistral Small 3.1 24B Base.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 1026.0B more parameters than Mistral Small 3.1 24B Base, making it 4275.0% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to Mistral Small 3.1 24B Base's 128,000 tokens. Only Mistral Small 3.1 24B Base specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Both Mistral Large 4 and Mistral Small 3.1 24B Base support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Mistral Large 4
Mistral Small 3.1 24B Base
License
Usage and distribution terms
Mistral Large 4 is licensed under a proprietary license, while Mistral Small 3.1 24B Base uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
Mistral Large 4 was released on 2026-10-06, while Mistral Small 3.1 24B Base was released on 2025-03-17.
Mistral Large 4 is 19 months newer than Mistral Small 3.1 24B Base.
Oct 6, 2026
2 days ago
1.6yr newerMar 17, 2025
1.6 years ago
Knowledge 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 4 is available from Mistral AI. Mistral Small 3.1 24B Base is available from Mistral AI.
Mistral Large 4
Mistral Small 3.1 24B Base
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Large 4 and Mistral Small 3.1 24B Base side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Large 4 vs Mistral Small 3.1 24B Base.