Mistral Large 4.0
At a glance
- Price per 1M tokens (input)
- $0.68
- Price per 1M tokens (output)
- $2.09
- Context
- 524,288 tokens
- Free access
- yes (see below)
Refreshed daily; data verified October 8, 2026. Published October 8, 2026.
Mistral Large 4.0 is designed for developers and enterprise teams building applications that require both visual comprehension and dynamic interaction with external systems. It supports image understanding to interpret diagrams, charts, or photos, and tool calling to invoke APIs or functions based on user input, enabling step-by-step reasoning for complex workflows like data analysis or automated reporting. Mistral AI emphasizes open-weight accessibility and efficient inference, allowing organizations to deploy the model in private environments while maintaining control over data and latency.
Specifications & pricing
| Input (per 1M tokens) | $0.68 |
|---|---|
| Output (per 1M tokens) | $2.09 |
| Cache read (per 1M tokens) | $0.068 |
| Context window | 524,288 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified October 8, 2026. Official Mistral AI pricing.
What Mistral Large 4.0 would cost on your workload — run it through the cost calculator →
Where to try Mistral Large 4.0 for free
- Mistral AI offers a free chat — Le Chat (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What types of tasks benefit most from Mistral Large 4.0's image understanding capability?+
Tasks such as interpreting technical schematics, extracting data from scanned forms, analyzing medical imagery for preliminary insights, or understanding UI layouts in software testing are well-suited, as the model can process visual input alongside textual context to generate informed responses.
How does tool calling work in practice with this model?+
Tool calling allows the model to request execution of predefined functions — such as querying a database, triggering a cloud service, or performing a calculation — by generating a structured call that your application can interpret and act upon, then incorporate the result into its reasoning process.
How does Mistral Large 4.0 differ from smaller models in the Mistral lineup?+
Compared to compact variants like Mistral Small or Mistral Medium, this model offers stronger performance on multimodal and reasoning-intensive tasks due to increased parameter capacity, while still being optimized for efficient deployment via quantization and quantization-aware training techniques.
What should users be aware of regarding limitations when deploying this model?+
While capable of handling complex prompts, the model may occasionally produce inconsistent outputs when faced with ambiguous visual inputs or overly complex tool chains, and its performance depends on the quality and relevance of the provided tools and training data alignment with the target use case.
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