Mistral Small 3.1 vs Mistral Small 4: Which is Better in 2026?
A comprehensive comparison of Mistral Small 3.1 and Mistral Small 4 covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Mistral Small 3.1 if:
- →You want more affordable paid plans (from $2/mo)
- →You need 24b parameter model (mistral-small-3.1-24b) or 128k token context window
Choose Mistral Small 4 if:
- →You need 119b total parameters, 6b active per token (moe: 128 experts, 4 active) or 256k token context window
Mistral Small 3.1 vs Mistral Small 4: At a Glance
Pricing Comparison: Mistral Small 3.1 vs Mistral Small 4
Understanding the pricing differences between Mistral Small 3.1 and Mistral Small 4 is crucial for making the right choice. Here's how their plans compare side by side.
Mistral Small 3.1 Pricing
Mistral Small 4 Pricing
💡 Pricing takeaway: Both Mistral Small 3.1 and Mistral Small 4 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 3.1 and Mistral Small 4 stacks up.
What Makes Each Tool Unique
🔵 Unique to Mistral Small 3.1
Features available in Mistral Small 3.1 but not in Mistral Small 4:
- ✓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
- ✓Apache 2.0 license — permissive commercial and self-hosted use
- ✓Multilingual support across 40+ languages
- ✓Runs on a single RTX 4090 or Mac with 32GB RAM
- ✓Available as base and instruct checkpoints for fine-tuning
- ✓Function calling and agentic workflow support
🟣 Unique to Mistral Small 4
Features available in Mistral Small 4 but not in Mistral Small 3.1:
- ✓119B total parameters, 6B active per token (MoE: 128 experts, 4 active)
- ✓256k token context window
- ✓Unified reasoning, vision, and coding in a single model
- ✓Configurable reasoning effort: reasoning_effort='none' (fast) or 'high' (deep)
- ✓Native image input support (text + vision in one model)
- ✓Apache 2.0 license — permissive commercial use, no additional restrictions
- ✓40% reduction in end-to-end latency vs Mistral Small 3
- ✓3× higher throughput vs Mistral Small 3 (throughput-optimized setup)
- ✓Beats GPT-OSS 120B on AA LCR and LiveCodeBench with shorter outputs
- ✓Runs on vLLM, llama.cpp, SGLang, and Transformers
Use Case Recommendations
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
Best for: Mistral Small 4
Mistral's first unified open-source model, released March 16, 2026. A 119B MoE model (6B active parameters per token) that merges reasoning (Magistral), multimodal vision (Pixtral), and agentic coding (Devstral) into a single Apache 2.0 model. 256k context window. 40% faster and 3× higher throughput than Mistral Small 3. Beats GPT-OSS 120B on coding and reasoning benchmarks while generating shorter outputs.
Ideal use cases:
- •Teams or individuals who need 119b total parameters, 6b active per token (moe: 128 experts, 4 active)
- •Teams or individuals who need 256k token context window
- •Teams or individuals who need unified reasoning, vision, and coding in a single model
- •Teams or individuals who need configurable reasoning effort: reasoning_effort='none' (fast) or 'high' (deep)
- •Anyone focused on mistral workflows
- •Anyone focused on llm workflows
🔧 Other llm-apis Tools to Consider
Mistral Small 3.1 and Mistral Small 4 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 3
Mistral's 24B latency-optimized open model — faster than Llama 3.3 70B, 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 3.1 better than Mistral Small 4?
It depends on your needs. Mistral Small 3.1 offers 10 key features including 24B parameter model (Mistral-Small-3.1-24B) and 128k token context window, while Mistral Small 4 provides 10 features including 119B total parameters, 6B active per token (MoE: 128 experts, 4 active) and 256k token context window. Mistral Small 3.1 uses a freemium model with a free tier, while Mistral Small 4 is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Mistral Small 3.1 cheaper than Mistral Small 4?
Mistral Small 4 doesn't have standard paid plans, while Mistral Small 3.1 starts at Open weights under Apache 2.0 license — free to download, self-host, and use commercially. Available via Mistral API (La Plateforme) and Le Chat.. 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 3.1 and Mistral Small 4 together?
Yes, many users combine Mistral Small 3.1 and Mistral Small 4 in their workflow. Mistral Small 3.1 excels at 24b parameter model (mistral-small-3.1-24b), while Mistral Small 4 shines with 119b total parameters, 6b active per token (moe: 128 experts, 4 active). 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 3.1 and Mistral Small 4?
While both are llm-apis tools, Mistral Small 3.1 emphasizes 24b parameter model (mistral-small-3.1-24b), whereas Mistral Small 4 is known for 119b total parameters, 6b active per token (moe: 128 experts, 4 active). The best choice depends on your specific workflow and feature priorities.