Intelligence
Index v5.0
Custom pricing for production workloads
Mistral AI's largest model to date: a 1 trillion-parameter natively multimodal Mixture of Experts with 49 billion active parameters and a 512K context window.
Mistral Large 4 is a 1 trillion-parameter natively multimodal model with 49 billion active parameters, built as a hybrid instruct-and-reasoning Mixture of Experts. It is served with a 512K context window and up to 256K output tokens.
mistralai/mistral-large-4-0https://api.aimlapi.com/v1Mistral Large 4 is a 1 trillion-parameter natively multimodal model with 49 billion active parameters, built as a hybrid instruct-and-reasoning Mixture of Experts. Mistral describes it as its largest and most capable model to date, and the weights are open.
| Developer | Mistral AI |
|---|---|
| Model ID | mistralai/mistral-large-4-0 |
| Modalities | Image, Text → Text |
| Context window | 524,288 tokens |
| Max output | 262,144 tokens |
| Parameters | 1 trillion total, 49 billion active |
| Architecture | Hybrid instruct-and-reasoning Mixture of Experts |
| Released | 6 October 2026 |
Sparse at scale
1 trillion parameters in total but 49 billion active per token, so capacity does not translate one-for-one into cost per request.
A 512K window
524,288 tokens in and up to 262,144 out, which fits an entire service, source, tests and a ticket thread, in one prompt.
Instruct and reasoning in one
A hybrid design rather than separate chat and reasoning checkpoints.
Native multimodality
Image input is built in rather than bolted on.
Published by Mistral AI with the Mistral Large 4 announcement, retrieved 6 October 2026. These are the vendor's own figures and have not been independently reproduced.
Long-running agent work
The DeepSWE and AutomationBench figures are both about finishing multi-step tasks rather than answering one question.
Whole-repository reasoning
A 512K window keeps the context in the prompt instead of in a retrieval layer.
Sources: https://mistral.ai/news/mistral-large-4 · https://docs.mistral.ai/models/mistral-large-4-0
See how Mistral Large 4 compares in intelligence and cost per task. Data retrieved Nov 3, 2026.
Index v5.0
USD / benchmark task
Compare 5 models available on AI/ML API across 1 independent benchmark.
Agentic tasks completed in a command-line environment.
| Usage | Price | Unit |
|---|---|---|
| Input | $1.36 | 1M tokens |
| Output | $4.18 | 1M tokens |
| Cached input | $0.14 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $1.36 | $4.18 | |
| $0.1984 | $0.7935 | |
| $0.4655 | $2.33 | |
| $1.01 | $4.23 | |
| $1.59 | $7.93 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "mistralai/mistral-large-4-0",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "mistralai/mistral-large-4-0",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "mistralai/mistral-large-4-0",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Mistral Large 4 is a 1 trillion-parameter natively multimodal model with 49 billion active parameters, built as a hybrid instruct-and-reasoning Mixture of Experts. Mistral describes it as its largest and most capable model to date.
Mistral Large 4 costs $1.36 input and $4.18 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Yes. Mistral describes it as natively multimodal, taking both text and image input and returning text.
With the announcement Mistral reported DeepSWE v1.1 61.7%, Cybench 93% on vulnerability reproduction and patching, AutomationBench 59.9%, Dense 200 visual grounding 42%, B3 Attack Resistance 93.3%, and a KORA score of 1.691. These are the vendor's own figures, not independently reproduced.
Mistral describes the model as open-weight and said at announcement that the weights would drop at the end of the month.
Use mistralai/mistral-large-4-0 as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.
Mistral Large 4 accepts up to 524,288 tokens of context and returns up to 262,144 output tokens.