Mistral Large 3
mistral/mistral-large-3Europe's flagship: efficient frontier-class quality with EU data-residency alignment.
Model overview
Context window
256K
tokens
Input price
—
quoted on request
Output price
—
quoted on request
Weekly volume
—
tokens / week
Open weights
No
API only
Capabilities
4
of 11 tags
Description
Mistral Large 3 is a language model from Mistral. Webparam does not stock this model yet and can source it on request — tell us what you need it for and we will come back with availability and a price. Mistral’s stated focus is europe's frontier lab; efficient and open. The weights are not published, so it is API-only.
Capabilities
- Tool calling
- Calls functions you define and returns their arguments as structured data, the basis of agents.
- JSON mode
- Constrains the response to valid JSON matching a schema you supply.
- Streaming
- Emits tokens as they are generated, so answers appear progressively rather than all at once.
- Caching
- Reuses already-processed prompt prefixes across requests, cutting input cost for repeated context.
Strengths & weaknesses
Strengths
- EU residency and compliance posture
- Efficient serving
- Strong function calling
Weaknesses
- Peak capability trails the top three
- Smaller ecosystem
Pricing
| Rate | Price | Unit |
|---|---|---|
| Input | — quoted on request | — |
| Output | — quoted on request | — |
Prompt caching is supported, so repeated prefixes bill below the listed input rate. This model is not provisioned yet; the rate is quoted on request.
Context window
256Ktokens
covers prompt and response together, so a long input leaves less room for the answer.
Supported features
| Feature | Support |
|---|---|
| Tool calling | Supported |
| JSON mode | Supported |
| Streaming | Supported |
| Vision input | Not supported |
| Audio input | Not supported |
| Long context | Not supported |
| Open weights | Not supported |
| Reasoning | Not supported |
| Fine-tunable | Not supported |
| Batch | Not supported |
| Caching | Supported |
Example use cases
European enterprise
Regulated industries
General production
Comparison
Compare against anything →| Attribute | Mistral Large 3 | GPT-5.1 | Command A |
|---|---|---|---|
| Context window | 256K | 400K | 256K |
| Input price | R0.00 | R0.00 | R0.00 |
| Output price | — not charged | — not charged | — not charged |
| Tool calling | Supported | Supported | Supported |
| JSON mode | Supported | Supported | Supported |
| Vision input | Not supported | Supported | Not supported |
| Open weights | Not supported | Not supported | Not supported |
| Prompt caching | Supported | Supported | Not supported |
Best value in each row is highlighted. Blank cells are facts the supplier does not publish, not zeroes.
Provider information
View Mistral →Mistral · Paris, France · est. 2023
Mistral proved a small European team could ship frontier-relevant models within months of founding. Its efficient architectures, open releases and EU regulatory alignment made it the default choice for European enterprises, with Codestral and Voxtral extending the family into code and speech.
Recent releases
Documentation
Frequently asked questions
Input is billed at R0.00 per 1M tokens, and there is no separate output charge. Prompt caching bills repeated prefixes below the listed input rate. Every figure on this page is an illustrative placeholder.
No. Mistral Large 3 is served through its provider's API only; the weights are not published, so self-hosting is not an option.
Prompt and response share one window: Mistral Large 3 accepts up to 256K tokens in total, so a long input leaves less room for the answer. The median across language models is 256K. Recall is strongest well inside the limit. Test at your real prompt depth before relying on the ceiling.
GPT-5.1 is the closest neighbour in the catalogue, with 400K of context at R0.00 per 1M tokens in. Put both in a comparison before wiring a fallback chain, so the trade-off is a decision rather than a default.