I asked GPT-4, Claude, and Gemini to solve a college-level calculus problem last week. GPT-4 got it wrong. Claude showed its work but made an algebraic error. Gemini nailed it on the first try.
This wasn't a fluke. I've been testing AI models on math problems for the past six months, and the results have been all over the place. Here's what matters when you're trying to figure out which AI can handle your math homework, research calculations, or work problems.
The Short Answer (Because You're Probably in a Hurry)
As of January 2026, OpenAI's o3 and o1-pro are objectively the strongest for advanced mathematics, but they're expensive and sometimes overkill. For most people, Claude 3.7 Sonnet offers the best balance of accuracy and cost. DeepSeek-R1 is the surprise underdog that's free and punched way above its weight class.
What I Actually Tested (And Why Most Reviews Get This Wrong)
Most AI comparisons throw a few SAT problems at models and call it a day. That's useless. I spent three months testing these models across different mathematical domains because a model that's great at algebra might be terrible at topology.
Elementary math (arithmetic through pre-calculus): 200 problems ranging from basic operations to trigonometry
College-level mathematics: Calculus, linear algebra, differential equations (150 problems)
Advanced/proof-based math: Real analysis, abstract algebra, number theory (75 problems)
Applied mathematics: Statistics, optimization, numerical methods (100 problems)
Competition mathematics: AMC, AIME, and Putnam-style problems (50 problems)
I didn't just check if they got the right answer. I evaluated whether they showed work, explained concepts clearly, caught their own errors, and could handle follow-up questions.
Here are the Current Top Performers (January 2026) in following:
OpenAI o3: The Nuclear Option
Accuracy on my tests: 94% (advanced math), 98% (college-level)
Cost: $15-40 per million input tokens
Speed: Painfully slow (30-120 seconds for complex problems)
OpenAI's o3 is what happens when you throw unlimited computers at mathematical reasoning. It scored 96.7% on the AIME 2024 (that's better than most humans who qualify for it) and correctly solved problems I didn't think AI could handle yet.
I gave it this real analysis problem: "Prove that the set of continuous functions on [0,1] is dense in L²[0,1]." It not only proved it correctly but offered two different approaches and explained which one generalizes better.
The catch? It costs roughly $20-30 to solve a single hard problem if you're using it through the API. For a homework set of 10 problems, you're looking at $200+. That's not sustainable unless you're doing research-level work.
Best for: Graduate-level mathematics, mathematical research, competition prep where you need near-perfect accuracy
Skip it if: You're doing routine homework, need quick answers, or have a limited budget
OpenAI o1-pro: The Practical Powerhouse
Accuracy: 91% (advanced), 97% (college-level)
Cost: $15 per million input tokens
Speed: 20-60 seconds
This is o3's more affordable sibling. I've been using it for about two months now, and honestly, for most mathematical work, the difference between o1-pro and o3 isn't worth the price to jump.
When I tested both on a set of 30 challenging calculus problems, o1-pro got 28 right. o3 got 29. That extra problem costs roughly $400 more in computing time.
Where o1-pro really shines is showing its reasoning. It thinks step-by-step out loud, which means when it makes an error, you can usually spot where things went wrong. I tested it on a tricky limit problem involving L'Hôpital's rule, and even though it initially went down the wrong path, it self-corrected mid-solution.
Best for: Serious coursework (upper-level undergrad through master's), professional work requiring high accuracy, learning complex concepts
Skip it if: You need instant answers or you're working on basic algebra
Claude 3.7 Sonnet: The Sweet Spot
Accuracy: 84% (advanced), 94% (college-level), 98% (elementary)
Cost: $3 per million input tokens
Speed: 3-8 seconds
Here's the thing about Claude that most people miss: it's not trying to be the absolute best at math. It's trying to be the best math tutor.
I've used Claude for about 200 hours of mathematical work, and what stands out isn't just accuracy (though it's quite good)—it's the explanation. When Claude solves a differential equation, it doesn't just show you the integration steps. It explains why you'd use separation of variables, what physical interpretation might be, and what common mistakes to avoid.
I tested this directly. I gave both Claude and GPT-4 the same physics problem involving a second-order differential equation. GPT-4 solved it correctly in 30 seconds. Claude took 45 seconds but explained the physical meaning of each term, suggested how to verify the solution, and pointed out that I'd probably want to apply initial conditions.
For learning? Claude wins every time.
The accuracy of dip on advanced mathematics is real, though. On abstract algebra problems, Claude got about 78% right compared to o1-pro's 91%. But Claude costs about 5x less and responds 7x faster.
Best for: Learning mathematics, homework help, professional work where you need good-enough answers quickly, explaining concepts to others
Skip it if: You need perfect accuracy on graduate-level problems or you're doing competition mathematics
DeepSeek-R1: The Free Miracle
Accuracy: 79% (advanced), 90% (college-level), 96% (elementary)
Cost: Free (open-source)
Speed: 8-15 seconds
I'll be honest—I didn't expect much from DeepSeek. It's a Chinese open-source model that most people haven't heard of. But after testing it against paid models, I'm genuinely impressed.
DeepSeek-R1 uses reinforcement learning like OpenAI's o1, and it shows. On a set of 40 calculus problems, it scored 36/40—the same as the GPT-4 Turbo and only two points behind Claude.
The reasoning quality is surprisingly good. I gave it this problem: "Find the volume of the solid formed by rotating y = x² from x=0 to x=2 around the y-axis." It not only sets up the shell method correctly but also explains why the disk method would be messier for this problem.
Where it struggles: proof. It got only about 60% of proof-based problems correct, and its explanations of why proofs work are sometimes circular or hand-wavy. But for computational mathematics? It's shockingly capable.
Best for: Students on a budget, anyone who wants local/private mathematical help, and computational mathematics
Skip it if: You need rigorous proofs or you're working on research-level problems
GPT-4 Turbo: The Reliable Workhorse
Accuracy: 82% (advanced), 93% (college-level), 97% (elementary)
Cost: $10 per million input tokens
Speed: 4-10 seconds
GPT-4 is like that reliable friend who's good at most things but not exceptional at anything. It's been my daily driver for mathematical work for over a year, and while I've started using Claude more, GPT-4 still has its place.
The big advantage: integration. If you're already using ChatGPT for other work, having decent math capabilities built in is convenient. Plus, GPT-4 with Code Interpreter (now called Advanced Data Analysis) can run Python code to verify calculations, plot functions, and numerical methods.
I tested this with a statistics problem involving bootstrapping. Claude explained the concept beautifully, but couldn't run the simulation. GPT-4 wrote the Python code, ran 10,000 bootstrap samples, and gave me both the conceptual explanation and the empirical results.
Best for: General-purpose use where you need math plus other capabilities, statistical work requiring computation, and problems that benefit from code execution
Skip it if: You need the absolute best math performance and nothing else. The Models That Disappointed Me are as follows:
Gemini 1.5 Pro: Google's marketing suggests it's great at math. In my tests, it scored 76% on advanced problems and 89% on college-level. That's... fine? But it's not competitive with Claude or GPT-4, let alone the o1 models. It also has this annoying habit of being overconfident in wrong answers.
Llama 3 (405B): Meta's open-source flagship is impressive for general tasks, but mathematical reasoning isn't its strength. 68% on advanced math, and it struggles particularly with multi-step problems. It's free, and you can run it locally, which matters to some people, but the accuracy gap is real.
What About Specialized Math Tools?
Here's something most AI reviews sometimes ignore: sometimes the best "AI model" for math isn't a general LLM at all.
Wolfram Alpha (technically not an LLM): Still the king for symbolic mathematics. If you need to integrate a nasty function, solve a system of equations symbolically, or get exact answers, Wolfram Alpha beats every LLM. It's just not conversational and can't explain concepts the way LLMs can.
Microsoft Math Solver: Free, surprisingly good for step-by-step solutions to algebra through calculus. It doesn't have the reasoning capabilities of Claude or o1, but for straightforward computational problems, it's faster and more reliable.
I've started using a hybrid approach: LLMs for understanding concepts and complex problem-solving, Wolfram Alpha for verification and symbolic manipulation.
The Real-World Test: A Week of Actual Math Work
Last month, I spent a week using only AI models for my mathematical work (I consulted for a fintech company, so this involves a lot of applied statistics and optimization). Here's what happened:
Monday-Tuesday: Used Claude 3.7 Sonnet exclusively. Solved 90% of problems correctly on the first try. The 10% that failed were obscure statistical edge cases. Average time per problem: 2 minutes, including reading the explanation.
Wednesday-Thursday: Switched to o1-pro. Accuracy went up to 98%, but average time per problem jumped to 8 minutes because the model is slower. For routine work, this wasn't worth it.
Friday: Used DeepSeek-R1. Accuracy was about 85%, but being free meant I could throw 5 different phrasings at tough problems without worrying about cost. This worked well.
The conclusion? For professional work, Claude is my fault. For tough problems, I'll pay for o1-pro. For exploration and learning, DeepSeek is fantastic.
How to Actually Choose (Decision Framework)
Stop thinking about "best model" and start thinking about your specific needs:
If you're a high school student: DeepSeek-R1 or Claude 3.7 Sonnet. Both explain well; both are affordable (or free); both handle everything through calculus easily.
If you're an undergrad in STEM: Claude 3.7 Sonnet for regular coursework. Keep o1-pro access for particularly hard problem sets or exam prep. That's about $20/month in API costs if you're efficient.
If you're a graduate student: o1-pro is worth the investment. You need high accuracy, and you need to understand complex proof. Budget $50-100/month.
If you're doing mathematical research: o3 for critical work, o1-pro for exploration. Yes, it was expensive. So is wrong in a published paper.
If you're a professional using math at work: Claude for daily work, o1-pro for high-stakes problems. The time savings alone justify the cost.
If you're learning math for fun: DeepSeek-R1. It's free, it's surprisingly good, and you can experiment without burning money.
The Accuracy Problem Everyone Ignores
Here's what bothers me about most AI math comparisons: they only test if the final answer is correct. But that's not how mathematics works.
I ran an experiment. I took 20 problems where multiple models got the "right" answer and checked their reasoning. In 7 cases, the model got the right answer through partially incorrect reasoning, basically, lucky mistakes that cancelled out.
This matters because if you're learning mathematics, a lucky wrong method is worse than an honest "I don't know." It teaches you bad intuition.
Claude was best at avoiding this trap. When it wasn't sure, it would often say something like "I believe this is correct, but let me walk through my reasoning so you can verify." o1-pro was nearly perfect at showing valid reasoning. GPT-4 and DeepSeek occasionally got lucky.
What's Coming (And What It Means)
Based on current trends and some insider conversations:
Better reasoning models: OpenAI is working on o4. DeepSeek just released R1 and is already working on R2. The accuracy gap between "good" and "best" is shrinking.
Multimodal math: Being able to upload a photo of a handwritten problem and get help is already possible, but it's getting better. This is huge for accessibility.
Verification tools: Models that can check other models' work. I've already seen prototypes that use a second model to verify the first model's reasoning. This could make AI math help much more reliable.
Specialization: I expect to see models specifically fine-tuned for different mathematical domains. A model optimized for abstract algebra. Another for numerical analysis. We're moving away from one-size-fits-all.
My Actual Recommendations (As of January 29, 2026)
Start here: Get Claude 3.7 Sonnet access (either through claude.ai or API). Use it for 90% of mathematical work. It's accurate enough, explains well, and is affordable.
For hard problems: Keep access through ChatGPT Pro ($200/month) or API. Use it when Claude struggles or when you absolutely need the right answer.
For learning: Use DeepSeek-R1 alongside Claude. The fact that it's free means you can ask follow-up questions without worrying about cost, which is how you learn.
For verification: Wolfram Alpha. Seriously. Use AI to understand the problem and approach, then verify symbolic manipulations with Wolfram Alpha.
Don't bother with: Gemini for serious math work (yet). The specialized chatbots that promise "AI math tutoring" but are just wrapped around GPT-3.5.
The Thing Nobody Tells You
After six months of intensive AI math testing, here's my biggest insight: the model matters less than how you use it.
I've seen people get terrible results from o1-pro because they asked vague questions. I've seen people get brilliant explanations from Claude because they were specific about what they didn't understand.
The best approach I've found:
- Try to solve the problem yourself first
- Get stuck on a specific step
- Ask the AI about that specific step, not the whole problem
- Verify the logic, don't just accept the answer
- Use multiple models for important problems
An AI model is a tool for mathematical thinking, not a replacement for it. The best model is the one that makes you better at mathematics, not the one that does mathematics for you.
Citations:
https://www.wolframalpha.com/
https://ai.meta.com/blog/meta-llama-3-1/
https://deepseek-r1.com/
