Mistral Large 3 — Spezifikationen
| Entwickler | Mistral AI |
|---|---|
| Typ | LLM (MoE) |
| Modality | Text → Text |
| Parameter | 675 Mrd. insgesamt / 41 Mrd. aktiv (MoE) |
| Kontextfenster | 256 K |
| Maximale Ausgabe | — |
| Lizenz | Apache 2.0 (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2025 |
| Eingabepreis | 2,00 $/1 Mio. |
| Ausgabepreis | 6,00 $ / 1 Mio. |
| API-Anbieter | Mistral, OpenRouter |
Lokal ausführen
| VRAM (4-Bit) | ~400 GB |
|---|---|
| Mindest-GPU | Multi-GPU-Server |
What is Mistral Large 3?
Mistral Large 3 marks the company’s return to fully open licensing — a 675B
mixture-of-experts activating 41B parameters per token, released under Apache 2.0 with a 256K
context. Pricing is $2 in / $6 out per million tokens.
Apache 2.0 at this scale is the headline. Most large open models carry either a custom
community licence with conditions attached (Llama 4’s EU restriction being the obvious
example) or a modified MIT. Apache 2.0 is unambiguous, permissive, patent-granting and
already approved inside most legal departments, which removes the review cycle that stalls
open-model adoption in enterprises. Combined with European provenance, that makes Large 3 a
straightforward choice for organisations with data-governance requirements that rule out
other options. The 3:1 output-to-input ratio is also gentler than most frontier models,
making generation-heavy workloads relatively less punishing. Self-hosting needs around 400 GB
at 4-bit, so the licence buys portability and auditability rather than a workstation
deployment.
Mistral Large 3 pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $2.00 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $6.00 |
| Output/input ratio | 3× |
| Blended (4:1 in:out) | $2.80 per 1M tokens |
What Mistral Large 3 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/Monat | Cost / month |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $3.50 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $70 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $700 |
Run your own numbers in the KI-API-Kostenrechner.
Cheaper alternatives to Mistral Large 3
| Modell | Gewichteter Preis pro Million US-Dollar | You save |
|---|---|---|
| GLM 5.2 offenere | $2.00 | 29% cheaper |
| DeepSeek V4-Pro offenere | $0.522 | 81% cheaper |
| Kimi K2.7 Code offenere | $0.980 | 65 % günstiger |
Self-host or pay the API?
Mistral Large 3 is open-weight, so you can run it yourself. It needs ~400 GB of VRAM at 4-bit (Multi-GPU server). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the Selbsthosting-vs.-API-Rechner works out the break-even point for your token volume.
Häufig gestellte Fragen
How much does Mistral Large 3 cost per 1M tokens?
Mistral Large 3 costs $2.00 per 1M input tokens and $6.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $2.80 per 1M tokens.
How much does Mistral Large 3 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $70 on Mistral Large 3. A side project (1M in / 0.25M out) costs roughly $3.50.
What is a cheaper alternative to Mistral Large 3?
GLM 5.2 is the strongest cheaper option in our database at $2.00 per 1M blended — about 29% less than Mistral Large 3. It is also open-weight, so self-hosting is an option.
Can I run Mistral Large 3 locally?
Yes. Mistral Large 3 is open-weight and needs about ~400 GB of VRAM at 4-bit quantisation (Multi-GPU server).
Why does Mistral Large 3 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 3 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 Datenbank für KI-Modelle oder das LLM-Leaderboard.
