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You can access Mistral Large 4 through Mistral’s API by creating a Studio API key, installing an official SDK, and sending a chat-completion request from your application’s server. Mistral’s model page lists the model ID as mistral-large-4 and labels it Public Preview Open v26.10 as of October 6, 2026. Check your Studio account for current availability and the model ID it exposes before integrating it.
What you need before you start
- A Mistral Studio account and an API key with access to the model.
- A server-side application environment where you can install the Python or TypeScript SDK.
- A secure place to store the key, such as a server environment variable or secrets manager. Do not embed it in browser or mobile app code.
Mistral’s Large 4 model page lists a 1-million-token context window and support for chat completions, structured outputs, function calling, document Q&A, and batching. Those are vendor-listed capabilities; check the current page and your account for any changes or access limits.
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How to get a Mistral API key
- Open Mistral’s Studio key setup guide and follow its steps to activate Studio and create a key.
- Copy and securely store the full key when it is displayed. Mistral says the complete key appears only once in the key flow; the guide also supports setting an expiry and recommends secure storage and regular rotation.
- Set the key in the server environment as
MISTRAL_API_KEY. Avoid committing it to source control or sending it to client-side code.
Access and payment conditions are not universal: Mistral’s key guide describes a Free mode with usage and rate limits, while its API cookbook says payments must be activated in the setup it describes. Check your Studio account and current plan details rather than assuming Large 4 is available on a free account.
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Mistral’s API quickstart demonstrates a chat-completion call using the alias mistral-large-latest. The Large 4 model page lists the specific ID mistral-large-4. These are distinct strings in the documentation; do not assume the example alias resolves to Large 4. Use the specific ID if it is available to your account, or verify the current mapping first.
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- Install the official Python SDK with
pip install mistralai. - Set
MISTRAL_API_KEYin the environment used by your server. - Use the model ID confirmed in Studio or the models API. For example:
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’s example uses mistral-large-latest; the code above substitutes the Large 4 page’s listed ID. SDK methods and model availability can change, so compare your implementation with the current quickstart before deploying.
Verify the model ID available to your account
Mistral provides models endpoints to list models available to a user and retrieve a model record. Use that endpoint or the model list in Studio to confirm the identifier and entitlement before relying on it in an application. This is especially useful when a model is in preview or when a quickstart uses a moving alias rather than a specific version.
Use the API from a TypeScript app
Mistral’s first-request quickstart links a TypeScript SDK path. Follow its current installation and request syntax, set the same MISTRAL_API_KEY secret on your server, and pass the model ID you verified for your account. Keep the request server-side: a key placed in browser JavaScript or a distributed mobile app can be extracted and misused.
Build features beyond a basic chat request
Structured outputs and document Q&A
The Large 4 page lists structured outputs and document Q&A among its features. These can support applications that need machine-readable responses or question answering over documents. Design validation and error handling around your own use case; a listed feature is not a guarantee that every output will meet application-specific requirements.
Function calling and agents
For an agent-style workflow, Mistral’s agent quickstart describes a pattern: 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 control which functions run and validate arguments before taking consequential actions.
Batching and larger context
The model page lists batching and a 1-million-token context window. Confirm the applicable API limits and account access in current documentation before designing around large requests or batch throughput; the context-window figure alone does not establish request size, latency, or cost for a particular workload.
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How much does Mistral Large 4 API access cost?
Mistral’s model page displayed these API rates on October 7, 2026. They are vendor-listed figures and may change:
| Token type | Listed price |
|---|---|
| Input | $0.68 per million tokens |
| Cached input | $0.07 per million tokens |
| Output | $2.09 per million tokens |
Estimate costs from the input, cached-input, and output tokens your app actually uses, then check the current model page and Studio account before budgeting production use. Mistral also notes that Studio features can vary by plan on its Studio overview.
Quick Recap
What to check before shipping
- Model status: Large 4 is labeled Public Preview Open v26.10 on the page dated October 6, 2026. Preview status and availability can change.
- Identifier: Confirm that
mistral-large-4is available to your account; do not treat the quickstart aliasmistral-large-latestas equivalent without verification. - Access and plan: Check Studio for your account’s model entitlement, rate limits, and payment requirements.
- Key security: Keep the secret on the server, restrict its use, set an expiry where appropriate, and rotate it regularly.
- Cost: Recheck current rates and account limits before projecting spend.
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.
