Mistral Large 4
Mistral Large 4Alternatives?
CheaperStep 5 PreviewthinkingOpen+9%-9%$1.03No PDF input+3
BetterMuse Glimmer 30BhighOpen+2%-95%$0.058Context: 1M → 131K
OpenKimi K3max+9%+76%$2.00Sampling parameters are fixed+3
CheaperMiMo v2.6 ProthinkingOpen+6%-88%$0.13No PDF input+4
CheaperGPT-6.1 Sollow+3%-88%$0.13Remove unsupported parameters+5Step 5 PreviewthinkingOpen+9%-9%$1.03No PDF input+3MiMo v2.6 ProthinkingOpen+6%-88%$0.13No PDF input+4GPT-6.1 Sollow+3%-88%$0.13Remove unsupported parameters+5
Gemini 3.8 Flashmedium+3%-18%*$0.93Sampling parameters are ignored+1GPT-5.6 Lunamax+3%-84%$0.18Muse Glimmer 30BhighOpen+2%-95%$0.058Context: 1M → 131KGPT-6 Lunamax+2%-94%$0.068Function calling only with none · Chat Completions+3GPT-6.1 Sollow+3%-88%$0.13Remove unsupported parameters+5MiMo v2.6 ProthinkingOpen+6%-88%$0.13No PDF input+4GPT-5.6 Lunamax+3%-84%$0.18Kimi K3maxOpen+9%+76%$2.00Sampling parameters are fixed+3Step 5 PreviewthinkingOpen+9%-9%$1.03No PDF input+3MiMo v2.6 ProthinkingOpen+6%-88%$0.13No PDF input+4Muse Glimmer 30BhighOpen+2%-95%$0.058Context: 1M → 131K
Ling 3.1 FlashOpen+2%-12%$1.00No image input+1Mistral Medium 3.5highOpen-15%-56%$0.50Context: 1M → 262KMistral Small 4thinkingOpen-39%-99%$0.015Context: 1M → 262KMistral Large 3non-thinkingOpen-56%-97%$0.031Context: 1M → 262KDevstral Mediumnon-thinkingOpen-61%—$0.40 / $2Context: 1M → 128K+2Ministral 3 14B 2512non-thinkingOpen-68%-98%$0.019Context: 1M → 262KMinistral 3 8B 2512Open-68%-99%$0.011Context: 1M → 262KMinistral 3 3B 2512Open-79%-99%$0.008Context: 1M → 131KMagistral Medium 1thinkingn/a——Context: 1M → 40K+1All Mistral models →History
4 versionsBenchmarks
in Chat & KnowledgeWatch out 5
Infinite whitespace stream›
In JSON Object mode, include JSON in the system or user prompt to avoid a possible infinite whitespace stream. Source ↗
Content is a list of chunks›
With reasoning_effort high, message.content is a chunk list rather than a plain string; parse thinking and text chunks. Source ↗
Only function calling · Mistral regional endpoints›
Regional endpoints support function calling only and omit Agents, Batch and Files; check regional model availability before routing requests. Source ↗
10% upcharge · Mistral regional endpoints›
Regional inference charges 1.1× standard list prices for input, output, cached reads and cache writes. Source ↗
Reinforcement learning run still in flight›
The public preview reinforcement learning run is still in flight, and Mistral expects further rapid changes as training scales. Source ↗
FAQ
How much does Mistral Large 4 cost?›
$1.36 per 1M input tokens and $4.18 per 1M output tokens; cached input $0.14 per 1M; cheapest via mistralai at $0.68 / $2.09. A typical benchmark task costs about $1.13 at default effort.
Is Mistral Large 4 free to use?›
There is no free API; the cheapest paid price is $0.68 per 1M input tokens.
How good is Mistral Large 4, and what is it best at?›
#62 of 334 in Chat & Knowledge, #23 of 132 in Coding, #19 of 133 in Agents & Tool Use, #90 of 195 in Writing & Language, #39 of 124 in Vision & Documents. Strongest at: Coding, Agents & work tasks, Reasoning, Long context.
Is Mistral Large 4 better than Mistral Large 3?›
On AA-LCR (long context) 1.1, Mistral Large 4 scores 81.3 vs 36 — higher. It is 3,544% more expensive per task.
How can I use Mistral Large 4?›
Through the API of Mistral.
What is the context window of Mistral Large 4?›
1M tokens, with up to 262K output tokens.
When was Mistral Large 4 released? Is there a newer version?›
Released 2026-10-06. It is the current version in its line.
Is Mistral Large 4 open source?›
No, Mistral Large 4 is a closed model available through an API. The closest open-weights option here is Kimi K3.
Can Mistral Large 4 read images?›
It accepts text and images, PDF files as input.
What is Mistral Large 4?›
Mistral Large 4 is a closed language model from Mistral, released Oct 2026, the successor to Mistral Large 3.
What are the best alternatives to Mistral Large 4?›
Step 5 Preview (cheaper), Muse Glimmer 30B (better), Kimi K3 (open weights).