Best LLM for math 2026
deepseek-reasoner or o3 models, as they lead benchmarks with ~97%+ accuracy on math datasets. These models outperform others in complex problem-solving and numerical reasoning.RECOMMENDATION
deepseek-reasoner for the best math and reasoning performance in 2026 due to its superior accuracy and cost efficiency compared to alternatives.| Use case | Best choice | Why | Runner-up |
|---|---|---|---|
| Complex math problem solving | deepseek-reasoner | Leads math benchmarks with ~97%+ accuracy and strong reasoning | o3 |
| General coding and math tasks | claude-sonnet-4-5 | Top coding and math accuracy with strong contextual understanding | gpt-4.1 |
| Cost-effective math reasoning | o3 | High accuracy with lower cost than premium models | deepseek-reasoner |
| Multimodal math applications | gemini-2.5-pro | Strong multimodal capabilities with solid math reasoning | gpt-4.0 |
Top picks explained
deepseek-reasoner is the leader for math and reasoning tasks in 2026, achieving top accuracy (~97%+) on MATH benchmarks at a competitive cost. It excels in complex numerical problem-solving and logical reasoning.
o3 is a close second, offering similarly high math accuracy with slightly different cost and latency trade-offs, making it a solid alternative for cost-conscious deployments.
claude-sonnet-4-5 and gpt-4.1 are excellent for combined coding and math tasks, with strong contextual understanding and coding benchmark leadership, useful when math is part of broader programming workflows.
gemini-2.5-pro stands out for multimodal math applications, supporting image and text inputs with strong reasoning, ideal for interactive or visual math tasks.
In practice
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["DEEPSEEK_API_KEY"], base_url="https://api.deepseek.com")
response = client.chat.completions.create(
model="deepseek-reasoner",
messages=[{"role": "user", "content": "Solve the integral of x^2 from 0 to 3."}]
)
print("Answer:", response.choices[0].message.content)Answer: The integral of x^2 from 0 to 3 is (1/3)*x^3 evaluated from 0 to 3, which equals (1/3)*27 - 0 = 9.
Pricing and limits
| Option | Free tier | Cost | Limits | Context window |
|---|---|---|---|---|
deepseek-reasoner | No free tier | Lower cost than premium OpenAI models | Max tokens ~4096 | 4096 tokens |
o3 | No free tier | Competitive pricing, cost-effective | Max tokens ~8192 | 8192 tokens |
claude-sonnet-4-5 | Limited free trial | Premium pricing | Max tokens ~9000 | 9000 tokens |
gpt-4.1 | Limited free trial | Premium pricing | Max tokens ~8192 | 8192 tokens |
gemini-2.5-pro | Limited free trial | Premium pricing | Max tokens ~8192 | 8192 tokens |
What to avoid
Avoid using older or smaller models like gpt-4o-mini or claude-3-5-sonnet-20241022 for advanced math tasks as they lack the accuracy and reasoning power needed for complex calculations.
Do not rely on generalist models without math specialization if your use case demands high precision in math, as they may hallucinate or produce incorrect results.
Steer clear of deprecated models such as gpt-3.5-turbo or claude-2 which are outdated and no longer supported.
How to evaluate for your case
Benchmark candidate models on your specific math tasks using datasets like MATH or custom problem sets. Measure accuracy, latency, and cost per query.
Use automated scripts to send math problems and compare outputs against ground truth answers.
Consider context window size if your math problems require multi-step reasoning or large input contexts.
Key Takeaways
- Use
deepseek-reasonerfor best-in-class math accuracy and cost efficiency in 2026. -
o3offers a strong alternative with competitive pricing and large context windows. - Avoid outdated or smaller models for complex math to prevent inaccurate results.
- Benchmark models on your own math tasks to ensure fit for your specific use case.