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Mistral Nemo

mistral/mistral-nemo

Mistral Nemo is a 12B model with a context window of 131.1K tokens and the Tekken tokenizer trained on 100+ languages, offering ~30% better source code compression and improved multilingual efficiency as a drop-in replacement for Mistral 7B.

Tool Use
index.ts
import { streamText } from 'ai'


const result = streamText({
  model: 'mistral/mistral-nemo',
  prompt: 'Why is the sky blue?'
})

What To Consider When Choosing a Provider

  • Zero Data Retention

    AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.

    Authentication

    AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

The Tekken tokenizer's compression efficiency means that for code-heavy or non-Latin-script workloads, you use fewer tokens per request than with models using conventional tokenizers.

When to Use Mistral Nemo

Best For

  • Multilingual applications:

    Spanning European, East Asian, and Arabic-script languages

  • Code-heavy workloads:

    Tekken's 30% compression advantage reduces token costs

  • Korean or Arabic applications:

    2-3x tokenizer compression is significant

  • Mistral 7B migrations:

    Deployments that need a larger context window

Consider Alternatives When

  • Larger general-purpose headroom:

    You need more capacity (consider Mistral AI Large 3)

  • Code generation or agentic coding:

    Coding is the primary task (consider Devstral or Codestral)

  • Higher reasoning depth:

    Tasks that require deeper reasoning traces (consider Magistral models)

Conclusion

Mistral Nemo's Tekken tokenizer is its distinguishing technical contribution, delivering efficiency gains for code and non-Latin-script languages that translate into lower costs per task. For multilingual applications and code-heavy pipelines, those gains compound at scale.

FAQ

Tekken is a tokenizer trained on 100+ languages, introduced with Mistral Nemo. Tekken achieves ~30% better source code compression, 2x better compression for Korean, and 3x better compression for Arabic compared to previous Mistral AI tokenizers.

131.1K tokens.

Yes. Mistral AI positions it as a drop-in upgrade with the same architecture family, improved quality, and a larger context window.

English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi, among others.

FP8 is a reduced-precision number format that speeds inference and reduces memory usage. Quantization-aware training means the model was trained to tolerate FP8 quantization, so accuracy doesn't degrade compared to full-precision inference.

Apache 2.0, permitting commercial use and modification.

Mistral AI in collaboration with NVIDIA, as indicated by the NeMo branding aligned with NVIDIA's NeMo framework ecosystem.