Mathstral 7B vs Mistral Small 4: Which is Better in 2026?
A comprehensive comparison of Mathstral 7B and Mistral Small 4 covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Mathstral 7B if:
- →You want more affordable paid plans (from $70.1/mo)
- →You need 56.6% on math benchmark — state-of-the-art in the 7b class at release (july 2024) or 63.47% on mmlu overall, with strong gains on stem subjects vs. mistral 7b baseline
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
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Mathstral 7B vs Mistral Small 4: At a Glance
Pricing Comparison: Mathstral 7B vs Mistral Small 4
Understanding the pricing differences between Mathstral 7B and Mistral Small 4 is crucial for making the right choice. Here's how their plans compare side by side.
Mathstral 7B Pricing
Mistral Small 4 Pricing
💡 Pricing takeaway: Both Mathstral 7B 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 Mathstral 7B and Mistral Small 4 stacks up.
What Makes Each Tool Unique
🔵 Unique to Mathstral 7B
Features available in Mathstral 7B but not in Mistral Small 4:
- ✓56.6% on MATH benchmark — state-of-the-art in the 7B class at release (July 2024)
- ✓63.47% on MMLU overall, with strong gains on STEM subjects vs. Mistral 7B baseline
- ✓74.59% on MATH with majority voting + strong reward model among 64 candidates
- ✓Built on Mistral 7B architecture — compatible with mistral-inference and mistral-finetune tooling
- ✓Instructed model fine-tuned for multi-step mathematical and logical reasoning
- ✓Produced in collaboration with Project Numina — research-grade academic use focus
- ✓GRE Math Subject Test evaluation curated by Professor Paul Bourdon (UVA)
- ✓Open weights under research-friendly license — use or fine-tune for STEM applications
🟣 Unique to Mistral Small 4
Features available in Mistral Small 4 but not in Mathstral 7B:
- ✓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: Mathstral 7B
Mistral AI's open-weight math-specialized LLM released July 2024. Built on Mistral 7B, Mathstral achieves 56.6% on the MATH benchmark and 63.47% on MMLU, rising to 74.59% on MATH with a strong reward model and 64 candidates. Developed in collaboration with Project Numina to advance academic mathematical reasoning. Weights available on Hugging Face under an Apache 2.0-style research license.
Ideal use cases:
- •Teams or individuals who need 56.6% on math benchmark — state-of-the-art in the 7b class at release (july 2024)
- •Teams or individuals who need 63.47% on mmlu overall, with strong gains on stem subjects vs. mistral 7b baseline
- •Teams or individuals who need 74.59% on math with majority voting + strong reward model among 64 candidates
- •Teams or individuals who need built on mistral 7b architecture — compatible with mistral-inference and mistral-finetune tooling
- •Anyone focused on mistral workflows
- •Anyone focused on open-source 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 & Models Tools to Consider
Mathstral 7B 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 3.1
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
Is one of these your tool?
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Frequently Asked Questions
Is Mathstral 7B better than Mistral Small 4?
It depends on your needs. Mathstral 7B offers 8 key features including 56.6% on MATH benchmark — state-of-the-art in the 7B class at release (July 2024) and 63.47% on MMLU overall, with strong gains on STEM subjects vs. Mistral 7B baseline, 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. Mathstral 7B uses a free 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 Mathstral 7B cheaper than Mistral Small 4?
Mistral Small 4 doesn't have standard paid plans, while Mathstral 7B starts at Open weights on Hugging Face (mistralai/mathstral-7B-v0.1) — free to download and self-host. Compatible with mistral-inference and mistral-finetune. No commercial API endpoint offered at release; self-hosting required.. 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 Mathstral 7B and Mistral Small 4 together?
Yes, many users combine Mathstral 7B and Mistral Small 4 in their workflow. Mathstral 7B excels at 56.6% on math benchmark — state-of-the-art in the 7b class at release (july 2024), 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 Mathstral 7B and Mistral Small 4?
While both are llm apis & models tools, Mathstral 7B emphasizes 56.6% on math benchmark — state-of-the-art in the 7b class at release (july 2024), 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.
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