Mistral Large 4 — Specifications
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Developer | Mistral AI |
|---|---|
| Type | LLM (MoE, reasoning, multimodal) |
| Modality | Text, Image → Text |
| Parameters | 1.05T total / 52B active (MoE) |
| Context window | 1M |
| License | Mistral Ecosystem Open Weight License |
| Open weights | Yes |
| Released | 2026-10 |
| Input price | $0.70 /1M |
| Output price | $2.10 /1M |
| API providers | Mistral API, OpenRouter |
Run it locally
| VRAM (4-bit) | ~610 GB |
|---|
Benchmarks
| Mistral-reported | Artificial Analysis Coding Agent Index 48%; Terminal-Bench 4.0 41%; Artificial Analysis Intelligence Index v5 26. |
|---|
What is Mistral Large 4?
Mistral Large 4 is Mistral AI’s largest and most capable model to date: a mixture-of-experts model with 1.05T total parameters, 52B active per token and a 1.6B vision encoder. It reads text and images, writes text, has a 1M-token context window and offers three reasoning efforts (low, medium and high, the default). Mistral opened a public preview on 6 October 2026 and released the weights of an improved version on 29 October 2026; its model page now marks it generally available.
On Mistral’s pricing page it costs $0.70 per million input tokens, $0.07 for cached input and $2.10 per million output tokens. The licence is the Mistral Ecosystem Open-Weight License, which Mistral describes as a modified MIT licence: you can use, modify, fine-tune and commercialise the model, but a business that runs a Model-as-a-Service offering needs a separate commercial licence from Mistral. Besides Mistral’s own API (mistral-large-4), Mistral says it is available on OpenRouter, through Microsoft Foundry and on NVIDIA-hosted endpoints.
Self-hosting is data-centre work. The FP8 checkpoint on Hugging Face totals about 1.06 TB of weights, and Mistral’s NVFP4 checkpoint, about 610 GB, targets Blackwell GPUs (B200, B300, GB200 and GB300). It succeeds Mistral Large 3, which Mistral deprecated on 31 October 2026 and plans to retire on 30 April 2027; Large 3 remains the option if you need Apache 2.0 terms. Weigh renting against owning with the self-hosting vs API calculator, or estimate a bill with the AI API cost calculator.
Mistral Large 4 pricing: API cost per 1M tokens
| Input (per 1M tokens) | $0.700 |
|---|---|
| Output (per 1M tokens) | $2.10 |
| Output/input ratio | 3× |
| Blended (4:1 in:out) | $0.980 per 1M tokens |
What Mistral Large 4 costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Workload | Tokens / month | Cost / month |
|---|---|---|
| Side project | 1M in / 0.25M out | $1.23 |
| Small team | 20M in / 5M out | $25 |
| Production | 200M in / 50M out | $245 |
Run your own numbers in the AI API cost calculator.
Cheaper alternatives to Mistral Large 4
| Model | Blended $/1M | You save |
|---|---|---|
| Llama 3.1 8B open | $0.0240 | 98% cheaper |
| Mistral NeMo 12B open | $0.0292 | 97% cheaper |
| Gemma 3 4B open | $0.0600 | 94% cheaper |
Self-host or pay the API?
Mistral Large 4 is open-weight, so you can run it yourself. It needs ~610 GB of VRAM at 4-bit. Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the self-hosting vs API calculator works out the break-even point for your token volume.
Frequently asked questions
How much does Mistral Large 4 cost per 1M tokens?
Mistral Large 4 costs $0.700 per 1M input tokens and $2.10 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.980 per 1M tokens.
How much does Mistral Large 4 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $25 on Mistral Large 4. A side project (1M in / 0.25M out) costs roughly $1.23.
What is a cheaper alternative to Mistral Large 4?
Llama 3.1 8B is the strongest cheaper option in our database at $0.0240 per 1M blended — about 98% less than Mistral Large 4. It is also open-weight, so self-hosting is an option.
Can I run Mistral Large 4 locally?
Yes. Mistral Large 4 is open-weight and needs about ~610 GB of VRAM at 4-bit quantisation.
Why does Mistral Large 4 charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Mistral Large 4 charges 3× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Prices are the published list rates for the model's primary API and are reviewed as providers change them. Volume, batch and cached-input discounts are not included. Compare every model side by side in the AI models database or the LLM leaderboard.