Context Window
131,072
Tokens
Model Cost Profile
Mistral Large 2411
Developer: mistralai
Pricing updated Mar 11, 2026
Input rank: #279Output rank: #263
Mistral Large 2411, developed by mistralai, offers a substantial context window of 131072 tokens, making it suitable for applications requiring extensive text analysis or multi-turn conversations. With an input price of $2.00 per million tokens and an output price of $6.00 per million tokens, teams can effectively budget for high-volume processing tasks such as document summarization or customer support automation. This model's capabilities are particularly beneficial for enterprises needing to manage large datasets or complex interactions while controlling operational costs.
🔧 Tool Calling📋 Structured Output
Input Price / 1M
$2.00
Prompt tokens
Output Price / 1M
$6.00
Completion tokens
Intelligence (MMLU)
Benchmark Pending
Massive Multitask Language Understanding
Side-by-Side Pricing Table
| Usage Type | Price / 1M Tokens |
|---|---|
| Input (Prompt) | $2.00 |
| Output (Completion) | $6.00 |
Price History
Mistral Large 2411 Pricing Trend
Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 — Mar 11
Current Input / 1M
$2.00
Current Output / 1M
$6.00
Cost Calculator
Estimate monthly spend for Mistral Large 2411 based on your workload.
01.0B
0500M
Estimated Monthly Cost
$122
25M input + 12M output tokens
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FAQ
Common pricing and benchmark questions for Mistral Large 2411.
How much does Mistral Large 2411 cost per 1M input tokens?
Mistral Large 2411 input pricing is $2.00 per 1M tokens based on the latest synced provider data.
How much does Mistral Large 2411 cost per 1M output tokens?
Mistral Large 2411 output pricing is $6.00 per 1M tokens based on the latest synced provider data.
What context window does Mistral Large 2411 support?
Mistral Large 2411 supports a context window of 131,072 tokens.
How can I compare Mistral Large 2411 with cheaper alternatives?
Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.