To use Mistral Large 4 in an app, create an API key in Mistral Studio, store it securely on your server, and call Mistral’s chat-completion API with the model ID mistral-large-4. Mistral labels the model Public Preview Open v26.10 on its model page dated October 6, 2026, so check that your account can access it and confirm the current model ID before deploying.
What you need before making an API call
- A Mistral Studio account and an API key with access to the model.
- A server-side application environment where you can store the key as a secret or environment variable.
- The official SDK for your language, or an API client that can send chat-completion requests.
The Studio key flow displays the full key only once. Save it in a secure location, set an expiry if appropriate, and rotate it regularly. Do not embed it in browser code or a mobile app: clients distributed to users can expose credentials. See Mistral’s API-key setup guide.
Which model ID should you use?
Mistral’s Large 4 model page lists mistral-large-4 as the model ID and labels the model “Public Preview Open v26.10.” The general API quickstart uses mistral-large-latest as its example alias; that example does not establish that the alias selects Large 4 in every account. For a Large 4 integration, use the specific ID shown on the Large 4 page, then verify it is available to your account.
You can check available models by listing the models exposed to your account or retrieving a model record through Mistral’s Models API endpoints. Model names, preview status, and account access can change.
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Make a chat-completion request from Python
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Create a Studio key: follow the Studio key instructions and copy the key when it is displayed.
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Set the key in the server environment: define
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Install Mistral’s Python SDK: use the official quickstart for the current installation command and SDK guidance.
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Send a request: the following follows the quickstart’s request pattern, with the Large 4 model ID substituted for its example alias:
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import os from mistralai.client import Mistral client = Mistral(api_key=os.environ["MISTRAL_API_KEY"]) response = client.chat.complete( model="mistral-large-4", messages=[{"role": "user", "content": "What is Mistral AI?"}], ) print(response.choices[0].message.content)
The quickstart example uses mistral-large-latest; confirm the model identifier and availability for your account if the request returns a model or access error. The SDK’s API details can evolve, so consult the linked quickstart for the current version-specific setup.
Use Mistral from a TypeScript app
Mistral’s developer quickstart also provides a TypeScript path. Follow its current SDK installation and chat-completion example, set the API key in the server environment, and pass mistral-large-4 as the model only after confirming availability in your account. The same server-side secret-handling rule applies: never place the key in frontend JavaScript bundled for users. See Send your first API request.
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Build beyond a basic chat response
Mistral lists chat completions, structured outputs, function calling, document Q&A, and batching among Large 4’s features. Its model page reports a 1M-token context window. These are vendor-listed capabilities, not a guarantee that every feature or limit is enabled for every account or plan; check current model and Studio documentation before designing around a specific limit.
Structured output and document workflows
Structured outputs can support responses that need a defined shape, while document Q&A can be used in workflows that answer questions about supplied documents. Mistral describes Studio as a platform for conversational AI, autonomous agents, document intelligence, and retrieval-augmented generation (RAG); feature availability can vary by plan. See the Studio overview.
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Function calling and agents
For an agent that uses application tools, define a tool schema, let the model request a function, execute that function in your application, and send the result back to the model. Your application—not the model—should validate tool arguments and control access to operations with real-world effects. Mistral’s agent quickstart demonstrates this interaction pattern.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does Mistral Large 4 require a paid plan?
Do not assume that Large 4 is universally available for free or that every account must follow the same payment path. Mistral’s key guide describes a Free mode with usage and rate limits, while its API cookbook says payments must be activated to enable API keys in the setup it describes. These are not enough to establish the access terms for every plan or for Large 4 specifically. Check your Studio account for model entitlement, payment requirements, and applicable limits before building around the API.
How much does Mistral Large 4 API access cost?
Mistral’s Large 4 model page displayed the following API rates on October 7, 2026. They are vendor-listed prices and may change; confirm the live rates before estimating production spend.
| Token category | Listed rate |
|---|---|
| Input tokens | $0.68 per million tokens (Mistral AI, 2026) |
| Cached input tokens | $0.07 per million tokens (Mistral AI, 2026) |
| Output tokens | $2.09 per million tokens (Mistral AI, 2026) |
For an estimate, account separately for ordinary input, cached input where applicable, and generated output. Actual usage depends on request volume and token counts; use the model page’s current rates and your account’s billing details for a production calculation.
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- Verify the current model ID, preview status, and account entitlement rather than assuming the quickstart alias means Large 4.
- Keep the API key in server-side secrets, restrict who can access it, and rotate it.
- Confirm plan-specific rate limits, feature availability, and current pricing in Studio.
- Test expected errors and usage in your own account before routing production traffic.
Mistral’s Large 4 model page also has an official variant with a different active-parameter figure: one page reports 52B active parameters and another reports 49B, while both report 1.05T total parameters and a 1.6B vision encoder. Those figures are not needed to make an API request, and the active-parameter discrepancy means it is best not to rely on that number without a canonical current specification.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

