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mistral/mistral-large-4Mistral's flagship, for carefully written text in French and English
Mistral Large 4 is Mistral AI's flagship model, released on 6 October 2026 as a public preview. It follows Mistral Large 3 with a one-million-token context window, image input and answers of up to 262K tokens.
It writes with care in French and English: announcements, emails, summaries and customer replies in the right tone. It calls tools and returns JSON. On Tchavi it answers directly; add reasoning_effort: "high" to have it reason first, which helps with calculations and multi-step problems.
On Tchavi you pay with mobile money, with no international card and no KYC, and one API key covers 80+ models. It is OpenAI-compatible: change the base URL, keep your SDK.
Announcements, emails and summaries in the right tone, in French and English
About a million input tokens in a single request
Very long answers in a single pass
Screenshots, photos and diagrams alongside your text
Function calling and structured output
reasoning_effort: "high" makes it think before answering
Write a polite reply to a customer in Porto-Novo upset about a late delivery, offering a goodwill gesture.
Summarise this service contract in ten points, with each party's obligations.
Look at this screenshot of a sales table and give the three main trends.
Large 4 is the new generation: a one-million-token context against 262K, and reasoning on request. Large 3 (model: "mistral-large-latest") costs less for everyday tasks.
Add reasoning_effort: "high" to your request. message.content then becomes a list: a thinking block, then the answer text. Without it, the model answers directly with a plain string.
Images, yes: screenshots, photos and diagrams. For a PDF, extract the text first, or pick a model that accepts documents.
Up to 1,048,576 input tokens, and up to 262,144 tokens per answer.
Point the base URL at Tchavi, use your Tchavi key and set model: "mistral-large-4" on /v1/chat/completions. The rest of your code stays the same.
Yes. Top up your Tchavi account with Orange Money, MTN MoMo, M-Pesa or another mobile money operator. No bank card is needed. Credits land as soon as the payment is confirmed and are deducted as you use them.
Yes. Keep your current OpenAI SDK, just change the base URL to Tchavi's (shown in the API tab) and use your Tchavi key. The rest of your code stays the same.
import Tchavi from '@tchavi/sdk';
const client = new Tchavi({ apiKey: 'API_KEY' });
const response = await client.chat.completions.create({
model: 'mistral-large-4',
messages: [
{
"role": "user",
"content": "Hello!"
}
],
});
console.log(response.choices[0].message.content);
console.log('Credits used:', response.tchavi.credits_used);Every parameter this model accepts on the API. Check the SDK for full typings.
modelRequiredID of the chat model (e.g. `mistral-large-4`).
messagesRequiredConversation messages. Each item has a `role` (system/user/assistant) and a `content` that's either a plain string or an array of content blocks (`text`, `image_url`, `document_url`).
temperature1Sampling temperature between 0 and 2. Higher values = more random.
max_tokensMaximum number of tokens to generate in the response.
max_completion_tokensOpenAI alias for `max_tokens`. Either field works; if both are sent, `max_completion_tokens` wins.
streamfalseIf true, partial message deltas are streamed as Server-Sent Events.
toolsFunction-calling toolbox. Pass an array of `{ type: "function", function: { name, description, parameters } }`. Returned tool calls land in `choices[].message.tool_calls`. Only models with `supportsTools: true` accept this.
tool_choiceHow the model picks among tools: `"auto"` (default), `"none"` to force a plain text reply, or `{ type: "function", function: { name } }` to force a specific tool. Only with `supportsTools: true`.
response_formatConstrain the output. Pass `{ type: "json_object" }` to force valid JSON (the model still needs a JSON instruction in your prompt). Only models with `supportsJsonMode: true` honour this.
top_p1Nucleus sampling: only tokens in the top `top_p` probability mass are considered.
seedInteger seed for deterministic output. The same seed + identical request yields the same completion when supported by the provider. OpenAI honours it; Anthropic ignores.
n1Number of completions to generate in one call. Defaults to 1; each extra completion is billed.
stream_optionsStreaming options, e.g. `{ "include_usage": true }` to receive a final SSE chunk with usage counts. Effective only when `stream: true`.
stopUp to 4 sequences where the API will stop generating further tokens.
frequency_penalty0Between -2.0 and 2.0. Positive values penalize tokens that have appeared frequently.
presence_penalty0Between -2.0 and 2.0. Positive values encourage the model to talk about new topics.
userEnd-user identifier passed to the upstream provider for abuse / moderation tracking. Should be stable per end-user.
logit_biasPer-token logit bias `{ "<tokenId>": <-100..100>, ... }`. OpenAI-specific; Anthropic ignores silently.
messages[].content[].image_urlAttach an image on a user message. Pass a content block `{ "type": "image_url", "image_url": { "url": ... } }`. PNG / JPEG / WebP, up to 4 per request. Only models with `supportsImageInput: true` accept this block (e.g. `gpt-4o`, `claude-opus-4-7`).
Estimated over 30 days, for a typical message: ~500 input + ~150 output tokens, billed to the thousandth of a credit.