Mistral SmallvsMistral Nemo
Mistral AI vs Mistral AI — Side-by-side model comparison
Head-to-Head Comparison
| Metric | Mistral Small | Mistral Nemo |
|---|---|---|
| Provider | ||
| Arena Rank | #19 | #27 |
| Context Window | 32K | 128K |
| Input Pricing | $0.20/1M tokens | $0.30/1M tokens |
| Output Pricing | $0.60/1M tokens | $0.30/1M tokens |
| Parameters | 22B | 12B |
| Open Source | Yes | Yes |
| Best For | Fast inference, cost-effective tasks, chat | Lightweight tasks, drop-in replacement |
| Release Date | Sep 18, 2024 | Jul 18, 2024 |
Mistral Small
Mistral Small, developed by Mistral AI, is a compact 22 billion parameter model with a 32K token context window optimized for fast inference and low deployment costs. The model handles coding, summarization, classification, and conversational tasks while maintaining the quality standards established by the Mistral model family. Its small footprint makes it suitable for edge deployment, cost-sensitive production applications, and use cases requiring low-latency responses. Priced at $0.20 per million input tokens and $0.60 per million output tokens, it offers affordable access to Mistral's technology. As an open-source model, it can also be self-hosted without API costs. Mistral Small ranks #19 on the Chatbot Arena leaderboard, demonstrating competitive performance for its compact size and establishing it as a strong option for budget-conscious deployments.
View Mistral AI profile →Mistral Nemo
Mistral Nemo, developed jointly by Mistral AI and NVIDIA, is a compact open-source model with 12 billion parameters designed as a high-performance replacement for smaller models. Despite its size, the model delivers performance significantly above its weight class on coding, reasoning, and multilingual tasks, benefiting from the combined expertise of Mistral's model architecture team and NVIDIA's training infrastructure. Mistral Nemo can run on a single consumer GPU, making it ideal for organizations with limited compute resources or strict data privacy requirements that preclude cloud-based API usage. Its small footprint enables fast inference and low-cost deployment while maintaining the quality standards of the Mistral model family. Free and open-source, the model supports commercial use and fine-tuning. It has become a popular choice for developers seeking capable, self-hosted AI without the hardware demands of larger models.
View Mistral AI profile →Key Differences: Mistral Small vs Mistral Nemo
Mistral Small ranks higher in arena benchmarks (#19) indicating stronger overall performance.
Mistral Small is 1.3x cheaper on average, making it the better choice for high-volume applications.
Mistral Nemo supports a larger context window (128K), allowing it to process longer documents in a single request.
Mistral Small has 22B parameters vs Mistral Nemo's 12B, which affects inference speed and capability.
When to use Mistral Small
- +You need the highest quality output based on arena rankings
- +Budget is a concern and you need cost efficiency
- +Your use case involves fast inference, cost-effective tasks, chat
When to use Mistral Nemo
- +Quality matters more than cost
- +You need to process long documents (128K context)
- +Your use case involves lightweight tasks, drop-in replacement
Cost Analysis
At current pricing, Mistral Small is 1.3x more affordable than Mistral Nemo. For a typical enterprise workload processing 100M tokens per month:
Mistral Small monthly cost
$40
100M tokens/mo (50/50 in/out)
Mistral Nemo monthly cost
$30
100M tokens/mo (50/50 in/out)
The Verdict
Mistral Small wins our head-to-head comparison with 3 out of 5 category wins. It's the stronger choice for fast inference, cost-effective tasks, chat, though Mistral Nemo holds an edge in lightweight tasks, drop-in replacement.
Last compared: April 2026 · Data sourced from public benchmarks and official pricing pages