Mistral Medium 3.5 vs Mistral Small 4: Which is Better in 2026?
A comprehensive comparison of Mistral Medium 3.5 and Mistral Small 4 covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Mistral Medium 3.5 if:
- →You want more affordable paid plans (from $1.5/mo)
- →You need 128b dense model (merged: instruction-following + reasoning + coding) or 77.6% on swe-bench verified (beats devstral 2 and qwen3.5 397b a17b)
Choose Mistral Small 4 if:
- →You need a broader feature set (10 features vs 9)
- →You need 119b total parameters, 6b active per token (moe: 128 experts, 4 active) or unified reasoning, vision, and coding in a single model
Mistral Medium 3.5 vs Mistral Small 4: At a Glance
Pricing Comparison: Mistral Medium 3.5 vs Mistral Small 4
Understanding the pricing differences between Mistral Medium 3.5 and Mistral Small 4 is crucial for making the right choice. Here's how their plans compare side by side.
Mistral Small 4 Pricing
💡 Pricing takeaway: Both Mistral Medium 3.5 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 Medium 3.5 and Mistral Small 4 stacks up. They share 1 features in common.
What Makes Each Tool Unique
🔵 Unique to Mistral Medium 3.5
Features available in Mistral Medium 3.5 but not in Mistral Small 4:
- ✓128B dense model (merged: instruction-following + reasoning + coding)
- ✓77.6% on SWE-Bench Verified (beats Devstral 2 and Qwen3.5 397B A17B)
- ✓91.4 on τ³-Telecom (strong agentic capabilities)
- ✓Configurable reasoning effort per request
- ✓Vision encoder trained from scratch — handles variable image sizes and aspect ratios
- ✓Open weights under modified MIT license (self-hostable on 4 GPUs)
- ✓Powers Mistral Vibe remote coding agents and Le Chat Work mode
- ✓Async cloud coding sessions with GitHub, Linear, Jira, Sentry integrations
🟣 Unique to Mistral Small 4
Features available in Mistral Small 4 but not in Mistral Medium 3.5:
- ✓119B total parameters, 6B active per token (MoE: 128 experts, 4 active)
- ✓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 Medium 3.5
Mistral's first flagship merged model, released May 22, 2026. A dense 128B model with a 256k context window that handles instruction-following, reasoning, and coding in a single set of weights. Available as open weights (modified MIT license) and powers Mistral Vibe remote coding agents and Le Chat's new Work mode. SWE-Bench Verified: 77.6%. API: $1.5/M input, $7.5/M output.
Ideal use cases:
- •Teams or individuals who need 128b dense model (merged: instruction-following + reasoning + coding)
- •Teams or individuals who need 256k token context window
- •Teams or individuals who need 77.6% on swe-bench verified (beats devstral 2 and qwen3.5 397b a17b)
- •Teams or individuals who need 91.4 on τ³-telecom (strong agentic capabilities)
- •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 Medium 3.5 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 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 Medium 3.5 better than Mistral Small 4?
It depends on your needs. Mistral Medium 3.5 offers 9 key features including 128B dense model (merged: instruction-following + reasoning + coding) and 256k 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 Medium 3.5 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 Medium 3.5 cheaper than Mistral Small 4?
Mistral Small 4 doesn't have standard paid plans, while Mistral Medium 3.5 starts at $1.5/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 Medium 3.5 and Mistral Small 4 together?
Yes, many users combine Mistral Medium 3.5 and Mistral Small 4 in their workflow. Mistral Medium 3.5 excels at 128b dense model (merged: instruction-following + reasoning + coding), 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 Medium 3.5 and Mistral Small 4?
While both are llm-apis tools, Mistral Medium 3.5 emphasizes 128b dense model (merged: instruction-following + reasoning + coding), 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.