Mistral Small 3 vs Mistral Small 3.1: Which is Better in 2026?
A comprehensive comparison of Mistral Small 3 and Mistral Small 3.1 covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Mistral Small 3 if:
- →You need 24b parameter model — efficient size for local and cloud deployment or 150+ tokens/second inference speed
Choose Mistral Small 3.1 if:
- →You need a broader feature set (10 features vs 9)
- →You need 24b parameter model (mistral-small-3.1-24b) or 128k token context window
Mistral Small 3 vs Mistral Small 3.1: At a Glance
Pricing Comparison: Mistral Small 3 vs Mistral Small 3.1
Understanding the pricing differences between Mistral Small 3 and Mistral Small 3.1 is crucial for making the right choice. Here's how their plans compare side by side.
Mistral Small 3 Pricing
Mistral Small 3.1 Pricing
💡 Pricing takeaway: Both Mistral Small 3 and Mistral Small 3.1 offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Mistral Small 3 and Mistral Small 3.1 stacks up. They share 2 features in common.
What Makes Each Tool Unique
🔵 Unique to Mistral Small 3
Features available in Mistral Small 3 but not in Mistral Small 3.1:
- ✓24B parameter model — efficient size for local and cloud deployment
- ✓150+ tokens/second inference speed
- ✓Over 81% accuracy on MMLU benchmark
- ✓3× faster than Llama 3.3 70B on identical hardware
- ✓Both pretrained base and instruction-tuned checkpoints released
- ✓Low-latency function calling for agentic workflows
- ✓Not trained with RL or synthetic data — clean base for fine-tuning
🟣 Unique to Mistral Small 3.1
Features available in Mistral Small 3.1 but not in Mistral Small 3:
- ✓24B parameter model (Mistral-Small-3.1-24B)
- ✓128k token context window
- ✓Native multimodal vision — text and image inputs in one model
- ✓150 tokens/second inference speed
- ✓Outperforms Gemma 3 and GPT-4o Mini on GPQA Diamond and text benchmarks
- ✓Multilingual support across 40+ languages
- ✓Available as base and instruct checkpoints for fine-tuning
- ✓Function calling and agentic workflow support
Use Case Recommendations
Best for: Mistral Small 3
Mistral Small 3 is a latency-optimized 24B parameter open-source model released January 30, 2025 under Apache 2.0. At 150 tokens/second and over 81% MMLU accuracy, it outperforms Llama 3.3 70B and Qwen 32B while running more than 3× faster on the same hardware. Designed to handle 80% of generative AI tasks — conversational assistance, function calling, and fine-tuning — on a single RTX 4090 or MacBook with 32GB RAM. Superseded by Mistral Small 3.1 (vision + 128k context) in March 2025.
Ideal use cases:
- •Teams or individuals who need 24b parameter model — efficient size for local and cloud deployment
- •Teams or individuals who need 150+ tokens/second inference speed
- •Teams or individuals who need over 81% accuracy on mmlu benchmark
- •Teams or individuals who need 3× faster than llama 3.3 70b on identical hardware
- •Anyone focused on mistral workflows
- •Anyone focused on llm workflows
Best for: Mistral Small 3.1
Mistral Small 3.1, released March 17, 2025, is a 24B open-source model that adds multimodal vision and a 128k context window to Mistral Small 3. Apache 2.0 license. Outperforms Gemma 3 and GPT-4o Mini on text and multimodal benchmarks at 150 tokens/second. Runs on a single RTX 4090 or Mac with 32GB RAM — making it the first open-source small model to surpass leading proprietary competitors across text, vision, multilingual, and long-context tasks.
Ideal use cases:
- •Teams or individuals who need 24b parameter model (mistral-small-3.1-24b)
- •Teams or individuals who need 128k token context window
- •Teams or individuals who need native multimodal vision — text and image inputs in one model
- •Teams or individuals who need 150 tokens/second inference speed
- •Anyone focused on mistral workflows
- •Anyone focused on llm workflows
🔧 Other llm-apis Tools to Consider
Mistral Small 3 and Mistral Small 3.1 aren't the only options. Here are other popular tools in the same space:
Claude Opus 4.8
Anthropic's flagship model — stronger coding, agents, and honesty
Mistral Small 4
Mistral's unified open-source model — reasoning + vision + coding, Apache 2.0
Mistral Medium 3.5
Mistral's 128B merged flagship — open weights, coding+reasoning+instructions
Mistral 3
Mistral's MoE flagship + edge model family — Apache 2.0, multimodal, reasoning
North Mini Code
Cohere's open-source agentic coding model — 30B MoE, 3B active, Apache 2.0
Codestral 25.08
Mistral's low-latency code completion model — FIM, 80+ languages, 256k context
Frequently Asked Questions
Is Mistral Small 3 better than Mistral Small 3.1?
It depends on your needs. Mistral Small 3 offers 9 key features including 24B parameter model — efficient size for local and cloud deployment and 150+ tokens/second inference speed, while Mistral Small 3.1 provides 10 features including 24B parameter model (Mistral-Small-3.1-24B) and 128k token context window. Mistral Small 3 uses a freemium model with a free tier, while Mistral Small 3.1 is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Mistral Small 3 cheaper than Mistral Small 3.1?
Both tools are similarly priced, starting at Open weights under Apache 2.0 license — free to download, self-host, fine-tune, and use commercially. Available via Mistral API (La Plateforme) at Mistral Small tier pricing.. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Mistral Small 3 and Mistral Small 3.1 together?
Yes, many users combine Mistral Small 3 and Mistral Small 3.1 in their workflow. Mistral Small 3 excels at 24b parameter model — efficient size for local and cloud deployment, while Mistral Small 3.1 shines with 24b parameter model (mistral-small-3.1-24b). Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Mistral Small 3 and Mistral Small 3.1?
While both are llm-apis tools, Mistral Small 3 emphasizes 24b parameter model — efficient size for local and cloud deployment, whereas Mistral Small 3.1 is known for 24b parameter model (mistral-small-3.1-24b). The best choice depends on your specific workflow and feature priorities.