Mistral Small 4 vs Mixtral 8x22B: Which is Better in 2026?
A comprehensive comparison of Mistral Small 4 and Mixtral 8x22B covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Mistral Small 4 if:
- →You need a broader feature set (10 features vs 8)
- →You need 119b total parameters, 6b active per token (moe: 128 experts, 4 active) or 256k token context window
Choose Mixtral 8x22B if:
- →You want more affordable paid plans (from $2/mo)
- →You need 141b total parameters, ~39b active per token (8 expert groups of 22b, 2 routed per token) or 64,536 token context window
Mistral Small 4 vs Mixtral 8x22B: At a Glance
Pricing Comparison: Mistral Small 4 vs Mixtral 8x22B
Understanding the pricing differences between Mistral Small 4 and Mixtral 8x22B is crucial for making the right choice. Here's how their plans compare side by side.
Mistral Small 4 Pricing
Mixtral 8x22B Pricing
💡 Pricing takeaway: Both Mistral Small 4 and Mixtral 8x22B 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 4 and Mixtral 8x22B stacks up.
What Makes Each Tool Unique
🔵 Unique to Mistral Small 4
Features available in Mistral Small 4 but not in Mixtral 8x22B:
- ✓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
🟣 Unique to Mixtral 8x22B
Features available in Mixtral 8x22B but not in Mistral Small 4:
- ✓141B total parameters, ~39B active per token (8 expert groups of 22B, 2 routed per token)
- ✓64,536 token context window
- ✓Function calling and JSON mode support
- ✓Multilingual: English, French, German, Italian, Spanish
- ✓Apache 2.0 license — free for commercial use, modification, redistribution
- ✓State-of-the-art open-weight reasoning at launch — beats LLaMA 3 70B and GPT-3.5 on MATH, HumanEval, MMLU
- ✓Efficient inference: ~39B active params means faster throughput than a dense 141B model
- ✓Compatible with vLLM, llama.cpp, TGI, Ollama, and other inference frameworks
Use Case Recommendations
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
Best for: Mixtral 8x22B
Mistral AI's largest open-weights mixture-of-experts model, released April 17, 2024. Mixtral 8x22B uses a sparse MoE architecture with 141B total parameters and ~39B active per token (8 groups of 22B, routing 2 experts per token). At launch it was the strongest open-weight model on reasoning, math, and coding benchmarks — outperforming LLaMA 3 70B and GPT-3.5 Turbo on most tasks. Supports 64k token context, natively multilingual (English, French, German, Italian, Spanish), with function calling and JSON mode. Weights released under Apache 2.0 on Hugging Face.
Ideal use cases:
- •Teams or individuals who need 141b total parameters, ~39b active per token (8 expert groups of 22b, 2 routed per token)
- •Teams or individuals who need 64,536 token context window
- •Teams or individuals who need function calling and json mode support
- •Teams or individuals who need multilingual: english, french, german, italian, spanish
- •Anyone focused on mistral workflows
- •Anyone focused on mixtral workflows
🔧 Other llm-apis Tools to Consider
Mistral Small 4 and Mixtral 8x22B 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 3.1
Mistral's 24B multimodal open-source model — beats GPT-4o Mini, Apache 2.0
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
Frequently Asked Questions
Is Mistral Small 4 better than Mixtral 8x22B?
It depends on your needs. Mistral Small 4 offers 10 key features including 119B total parameters, 6B active per token (MoE: 128 experts, 4 active) and 256k token context window, while Mixtral 8x22B provides 8 features including 141B total parameters, ~39B active per token (8 expert groups of 22B, 2 routed per token) and 64,536 token context window. Mistral Small 4 uses a freemium model with a free tier, while Mixtral 8x22B is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Mistral Small 4 cheaper than Mixtral 8x22B?
Mistral Small 4 doesn't have standard paid plans, while Mixtral 8x22B starts at $2/month. 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 4 and Mixtral 8x22B together?
Yes, many users combine Mistral Small 4 and Mixtral 8x22B in their workflow. Mistral Small 4 excels at 119b total parameters, 6b active per token (moe: 128 experts, 4 active), while Mixtral 8x22B shines with 141b total parameters, ~39b active per token (8 expert groups of 22b, 2 routed per token). 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 4 and Mixtral 8x22B?
While both are llm-apis tools, Mistral Small 4 emphasizes 119b total parameters, 6b active per token (moe: 128 experts, 4 active), whereas Mixtral 8x22B is known for 141b total parameters, ~39b active per token (8 expert groups of 22b, 2 routed per token). The best choice depends on your specific workflow and feature priorities.