Mistral Nemo

Mistral Nemo by Mistral AI excels in the Coding category, where it ranks #305 with a composite score of 40/100. A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,... It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Mistral Nemo is priced at $0.02 per million input tokens and $0.03 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Mistral Nemo holds rank #305 out of 340 models tracked. Its quality rank is #305 and adoption rank is #305. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Mistral Nemo has been evaluated across 5 different signals. Its strongest areas include Capabilities (50/100), Pricing (100/100), Context Window (81/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 40/100 reflects a weighted combination of all tracked signals.

Mistral Nemo is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check Mistral AI's website for current free tier availability and promotional offers.

Mistral Nemo supports a 131K token context window (131,072 tokens total). That translates to roughly 98,304 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Mistral Nemo supports function/tool calling, structured JSON output, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Mistral Nemo is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Mistral Nemo was developed by Mistral AI. It was released on July 19, 2024. You can access it through Mistral AI's API or download the model weights directly. Check our provider page for all models from Mistral AI and how they compare against each other.

Pick Mistral Nemo when you need a budget-friendly option for high-volume, simpler tasks where you prioritize cost over peak performance. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Mistral Nemo through Mistral AI's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.