Mathstral 7B vs Mistral Medium 3.5: Which is Better in 2026?
A comprehensive comparison of Mathstral 7B and Mistral Medium 3.5 covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Mathstral 7B if:
- →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 Medium 3.5 if:
- →You want more affordable paid plans (from $1.5/mo)
- →You need a broader feature set (9 features vs 8)
- →You need 128b dense model (merged: instruction-following + reasoning + coding) or 256k token context window
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Mathstral 7B vs Mistral Medium 3.5: At a Glance
Pricing Comparison: Mathstral 7B vs Mistral Medium 3.5
Understanding the pricing differences between Mathstral 7B and Mistral Medium 3.5 is crucial for making the right choice. Here's how their plans compare side by side.
Mathstral 7B Pricing
💡 Pricing takeaway: Both Mathstral 7B and Mistral Medium 3.5 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 Medium 3.5 stacks up.
What Makes Each Tool Unique
🔵 Unique to Mathstral 7B
Features available in Mathstral 7B but not in Mistral Medium 3.5:
- ✓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 Medium 3.5
Features available in Mistral Medium 3.5 but not in Mathstral 7B:
- ✓128B dense model (merged: instruction-following + reasoning + coding)
- ✓256k token context window
- ✓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
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 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
🧩 Other LLM APIs & Models Tools to Consider
Mathstral 7B and Mistral Medium 3.5 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 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 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?
This page ranks for "Mathstral 7B vs Mistral Medium 3.5" — buyers comparing the two land here, and ChatGPT and Perplexity cite it. Claim your listing for $19 one-time — no subscription, nothing to cancel — and get a Featured badge, top placement in your category, and a permanent dofollow backlink. Prefer it ongoing? Monthly is one click away on the next page.
Frequently Asked Questions
Is Mathstral 7B better than Mistral Medium 3.5?
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 Medium 3.5 provides 9 features including 128B dense model (merged: instruction-following + reasoning + coding) and 256k token context window. Mathstral 7B uses a free model with a free tier, while Mistral Medium 3.5 is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Mathstral 7B cheaper than Mistral Medium 3.5?
Mistral Medium 3.5 is cheaper, starting at $1.5/month compared to Mathstral 7B's 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 Medium 3.5 together?
Yes, many users combine Mathstral 7B and Mistral Medium 3.5 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 Medium 3.5 shines with 128b dense model (merged: instruction-following + reasoning + coding). 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 Medium 3.5?
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 Medium 3.5 is known for 128b dense model (merged: instruction-following + reasoning + coding). The best choice depends on your specific workflow and feature priorities.
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