Running maths problems locally with Ollama? From basic arithmetic to university-level calculus, these models handle numerical reasoning better than the rest. Here are the best Ollama models for maths in 2026.
What to Look for in a Maths Model
Maths is one of the harder tasks for language models. The best maths-capable models are trained on mathematical datasets, support chain-of-thought reasoning, and show their working — which helps you catch errors and build on results.
Top Ollama Models for Maths
1. Qwen2.5-Math 7B — Best Dedicated Maths Model
Qwen2.5-Math is purpose-built for mathematical reasoning. It’s trained specifically on maths data and consistently outperforms general-purpose models on everything from algebra to calculus. If maths is your primary use case, this is the model to run.
ollama run qwen2.5-math:7bBest for: All maths tasks, step-by-step workings
RAM required: 8GB minimum
2. DeepSeek-R1 8B — Best Reasoning Model
DeepSeek-R1 uses chain-of-thought reasoning to work through problems step by step before arriving at an answer. This approach dramatically improves accuracy on complex maths problems and means you can follow the model’s logic throughout.
ollama run deepseek-r1:8bBest for: Complex problems, university-level maths
RAM required: 8GB minimum
3. Phi-4 — Best for Speed and Efficiency
Microsoft’s Phi-4 has surprisingly strong mathematical ability for its size. It handles most GCSE and A-level style problems with ease and is significantly faster than larger models, making it great for quick calculations or tutoring applications.
ollama run phi4Best for: Everyday maths, fast responses
RAM required: 6GB minimum
4. Llama 3.1 70B — Best Quality If You Have the Hardware
If you have a powerful machine, Llama 3.1 70B is exceptional at maths. Its larger parameter count gives it deeper reasoning capability, and it handles multi-step problems, proofs, and statistics with impressive accuracy.
ollama run llama3.1:70bBest for: Advanced maths, statistics, proofs
RAM required: 48GB minimum
5. Mistral 7B — Best All-Rounder
Mistral 7B isn’t a dedicated maths model but handles basic to intermediate problems reliably. If you want one model for general tasks that also handles maths decently, Mistral is a solid choice.
ollama run mistralBest for: Mixed tasks including maths
RAM required: 8GB minimum
Quick Comparison
| Model | Maths Level | Speed | RAM |
|---|---|---|---|
| Qwen2.5-Math 7B | Advanced | Fast | 8GB |
| DeepSeek-R1 8B | Advanced | Medium | 8GB |
| Phi-4 | Intermediate | Very Fast | 6GB |
| Llama 3.1 70B | Expert | Slow | 48GB |
| Mistral 7B | Basic–Intermediate | Fast | 8GB |
Tips for Better Maths Results
Always ask the model to show its working. This reduces errors significantly:
Solve the following problem step by step, showing all workings: [problem]For complex problems, break them into smaller steps and solve each one separately rather than asking the model to solve everything in one go.
Our Recommendation
Start with Qwen2.5-Math 7B if maths is your main use case — it’s built for this and nothing else comes close at the same size. For more general use with solid maths capability, DeepSeek-R1 8B is an excellent alternative.
See our full guide to the best Ollama models in 2026 for a broader comparison.
Quantisation and Model Variants
When you run ollama run qwen2.5-math:7b, you’re actually running a quantised version of the full model. Quantisation compresses the model by storing numbers with lower precision—instead of 32-bit floating-point, quantised versions use 4-bit or 8-bit integers. This dramatically cuts RAM requirements and inference time, with minimal accuracy loss for most use cases. Understanding quantisation helps you make smarter choices about which models fit your hardware.
Ollama models typically come in multiple quantisation formats:
- 4-bit (Q4): The most common default. Reduces model file size by roughly 75% compared to full precision, with negligible accuracy loss. Ideal for most users and the best starting point.
- 8-bit (Q8): Larger file size and higher RAM requirements, but slightly better accuracy than 4-bit. Worth considering if you have 16GB+ available and need maximum mathematical precision.
- Full precision: The largest, slowest, and only marginally more accurate option—rarely worthwhile on consumer hardware.
For maths problems specifically, 4-bit quantisation rarely impacts correctness on typical algebra, calculus, or geometry tasks. The model still understands mathematical concepts correctly—it’s just using fewer bits internally to represent weights. However, if you’re doing highly precise numerical calculations (financial models, engineering tolerances, statistical analysis), test both 4-bit and 8-bit variants on real sample problems first. You might find 8-bit is necessary for your use case.
The real advantage is flexibility and experimentation. Start with 4-bit to see if it solves your problems within your hardware constraints, then upgrade to 8-bit only if accuracy isn’t good enough. Run your own benchmarks: take five representative maths problems you actually care about, solve them with both quantisation levels, and compare results side by side. This empirical approach beats guessing. You’ll quickly discover whether your chosen model at 4-bit is reliable for your specific workload, or whether you need 8-bit precision.
