education

Best AI for Math

The best AI for math in 2026: solvers, tutors and reasoning models compared on accuracy and price, how to pick the right one for your level, and where AI stands on unsolved problems like Navier–Stokes.

Updated September 2026

Quick answer: There is no single best AI for math — the right tool depends on whether you need a guaranteed-correct answer or help understanding the method. For exact answers you can trust, Wolfram Alpha is the most reliable because it computes symbolically rather than predicting text, and it is near-flawless on well-formed algebra, calculus and statistics (Pro from $5/month billed annually). For free step-by-step working, Microsoft Math Solver is the best no-cost option — it shows every step, from arithmetic to calculus, with no account or subscription. For photographing a school problem and watching it worked out, Photomath (owned by Google) leads, with a free tier and a $9.99/month Plus plan. For word problems, proofs and conceptual explanation, a frontier reasoning model such as GPT-6 Astra (every paid ChatGPT plan) or Claude Opus 5.5 (Claude Pro and above) is stronger than any dedicated solver. The one rule that still matters: with thinking and code execution off, a language model predicts numbers rather than computing them, so check anything long in a symbolic engine.

What changed since August: the research story moved again. On 8 September OpenAI announced a Lean-verified proof that the Navier–Stokes equations can blow up — the first AI claim on one of the seven Millennium Prize Problems — and mathematicians are still arguing over whether it answers the question they cared about. OpenAI now says the same internal model has resolved more than 100 open problems, and 25 Fields Medallists have published a letter objecting to how the labs are going about it. Jump to that section — but note it changes almost nothing about which tool you should use for a problem set.

This guide ranks the tools built for mathematics specifically — from primary-school arithmetic through algebra, geometry, calculus, statistics, competition maths and university-level proofs. If you want general study help across every subject, see best AI for students; for homework help across subjects, see best AI for homework; for a human-style tutor across subjects, see best AI tutors. Every price below is list and every figure is dated; verify against each tool’s live pricing page before you subscribe.

Math AI, AI math solvers and AI for math: the short answers

What is a math AI?

A math AI is any tool that uses artificial intelligence to solve, check or teach mathematics. There are four kinds: computational engines that calculate exact answers (Wolfram Alpha), solvers that show the worked steps for a specific problem, often from a photo (Photomath, Microsoft Math Solver), tutors that guide you to the answer instead of giving it (Khanmigo), and general reasoning models that handle word problems, proofs and explanation (GPT-6 Astra, Claude Opus 5.5). The table below sets out which does what.

What is the best AI math solver?

For free worked steps, Microsoft Math Solver: it shows every step from arithmetic to calculus from a photo, typed input or handwriting, with no account. For photographing school problems, Photomath has the smoothest scan-to-solution flow. For university maths where the answer has to be right, Wolfram Alpha Pro computes symbolically rather than predicting. For drilling a topic before an exam, Symbolab pairs the solver with practice sets.

How should I use AI for math?

Compute in an engine, reason in a model. Use a solver or Wolfram Alpha for the calculation, and a reasoning model — with its thinking and code-execution modes switched on — for setting up the word problem and explaining why each step works. Check any final number that carries marks in a second tool, and read the steps rather than copying the answer: the working is what you are being assessed on.

Which one should you pick?

Start with your level, then narrow by what you need the tool to do.

By level

By need

If you only install one thing: Microsoft Math Solver for the working, plus the free tier of any chatbot for the explanation. That covers most school and undergraduate maths at no cost.

The current state of AI for math: September 2026

AI can now solve maths problems that would have stumped every tool available two years ago — but the tool that computes the answer and the tool that explains it are usually not the same one.

Four shifts define the moment.

First, computational engines still own accuracy. A tool like Wolfram Alpha computes from a structured symbolic-maths system that has done algebra and calculus since 2009, so its answers are derived, not predicted. Independent 2026 testing puts its accuracy near the top of every category, with effectively zero errors on well-formed algebra and calculus queries (Miniloop). A general chatbot, by contrast, predicts the next token — which is why it can produce a confidently wrong number.

Second, reasoning models have run through the benchmarks. GSM8K and MATH are solved, AIME differences between frontier models sit inside run-to-run variance, and even the hardest tier of Epoch AI’s research-level FrontierMath is close to its ceiling: OpenAI’s launch table for GPT-6 Astra (3 September) reports 97.6% on FrontierMath Tier 4 v2, against 87.8% for Claude Fable 5.1 and 73.2% for Claude Opus 5 — vendor-run figures, so read them as a ceiling. The live signal has moved from benchmarks to open problems, covered in the next section. When these models run code mid-answer, their arithmetic reliability rises sharply too, because they reason about the method and offload the computation to an exact tool.

Third, the dedicated math-app market split into solvers and tutors. Solvers (Photomath, Symbolab, Microsoft Math Solver, Mathway) optimise for showing the worked steps of a specific problem. Tutors (Khanmigo, Thetawise, Mathos AI) optimise for guiding you to the answer yourself, often withholding the final result on purpose. Choosing well means deciding which job you want done before you compare apps.

Fourth, free got genuinely good. Microsoft Math Solver gives complete step-by-step solutions at no cost — the same steps that Mathway, Symbolab and Wolfram Alpha place behind a paywall — and Khanmigo tutoring costs $4/month while remaining free for teachers. The paid tier now buys convenience, tutoring depth and advanced coverage, not basic answers.

The practical takeaway runs through the whole guide: use a computational engine or dedicated solver for the arithmetic, and a reasoning model for the word problem, the concept and the explanation — because the two have different strengths and, more importantly, different failure modes.

AI and unsolved math problems: where things stand

This is the part of the story that has moved fastest, and it is worth separating from everything else on this page: frontier models are no longer only solving problems that have known answers. Through 2026 they have been used to settle questions that were open for decades — and since September, one of the Millennium Prize Problems. Every result below came with a formal proof in the Lean proof assistant, which checks that the argument is valid; it does not settle whether the theorem proved is the one mathematicians most wanted.

The results so far

The Erdős results (early 2026 to May). Earlier in the year, GPT-5.2 produced solutions to Erdős problems #397, #728 and #729 that were formalised in Lean and accepted by Fields Medallist Terence Tao, and GPT-5.4 resolved #1196. In May an OpenAI reasoning model disproved an 80-year-old conjecture posed by Paul Erdős in 1946 on the planar unit-distance problem, finding a new family of constructions that beat the square-grid arrangements assumed optimal since the 1940s (TechCrunch). OpenAI published it with commentary from Noga Alon, Melanie Wood and Thomas Bloom, who maintains the Erdős Problems database, and later published ten further results across geometry, coding theory, complexity, group theory, cryptography and combinatorics.

The Jacobian conjecture counterexample (July). Anthropic mathematician Levent Alpöge used Claude Fable 5 to find a strikingly simple counterexample to the Jacobian conjecture, open since Ott-Heinrich Keller posed it in 1939 — a function whose Jacobian determinant is −2 everywhere yet which maps three distinct inputs to the same output. It was verified in Lean before publication (Fortune).

The Riemann zeta result (10 August). An unreleased research version of Claude raised the proven lower bound on the fraction of the Riemann zeta function’s zeros that lie on the critical line — the fraction known to satisfy the Riemann hypothesis — from 41.6% to 67.2% (Anthropic). Anthropic says the work ran over two sessions of about a day and a half in total, coordinating roughly 60 subagents that used 31 million output tokens; its first 650 ideas all failed. Anthropic mathematicians Levent Alpöge and Ralph Furman validated the paper, external experts Brian Conrey and Dan Goldston reviewed it, and Anthropic released a Lean 4 formalisation. Anthropic itself says it does not expect the techniques to lead to a proof of the Riemann hypothesis (TechCrunch).

Navier–Stokes (8 September). OpenAI announced an analytical proof and a Lean formalisation that solutions to the three-dimensional Navier–Stokes equations, which describe fluid flow, can develop a singularity in finite time — one of the Clay Mathematics Institute’s seven $1 million Millennium Prize Problems (OpenAI). OpenAI says it used an internal model significantly more capable than GPT-6 Astra. Quanta Magazine reports that about 10,000 agents worked for 88 hours on the proof and 17 more on the formalisation, at an estimated cost of several million dollars (Quanta). Hours earlier, Tristan Buckmaster of NYU and Anthropic’s Levent Alpöge had announced a Lean-verified blow-up result for the related Euler equations, found with an LLM; Quanta reports their own Navier–Stokes argument is not yet verified.

The dispute is about which problem was solved. OpenAI’s construction relies on an external forcing term. The Clay problem statement, written by Charles Fefferman in 2000, allows a forced version as one of its options, but most researchers had been working on blow-up with no force at all. Scientific American quotes Luis Silvestre of the University of Chicago: “The Clay problem is settled, but the main problem for the Navier-Stokes equations is not” (Scientific American). Quanta reports that the Clay Institute has not recognised a solution, and there is a separate priority dispute over how much the result builds on human work by Diego Córdoba and Luis Martínez-Zoroa.

More than 100 problems, and the pushback (September). On 21 September OpenAI said the same internal model had resolved more than 100 open problems across most areas of mathematics, and set up an unpaid, independent Advisory Group on Mathematics and Artificial Intelligence hosted at Princeton’s Institute for Advanced Study, with nine mathematicians as initial members (TechCrunch). Ten days earlier, 25 Fields Medallists including Terence Tao had signed a declaration arguing that the labs’ race to announce famous results leaves too little time for write-ups and citation, raising attribution and plagiarism questions; it proposed no ban or rule (Scientific American, Terence Tao).

Four caveats that matter

None of the Millennium or Riemann results is something you can use. The Navier–Stokes and 100-problem results came from an internal OpenAI model, and the Riemann zeta result from an unreleased Claude. Nothing on the pricing tables below will reproduce them.

Formally verified is not the same as settled. Lean confirms that a proof is correct for the statement it proves. Whether that statement is the important one — the forcing question on Navier–Stokes — and who deserves credit are human judgements, and they are still open.

The field has already had one false alarm. In October 2025, an OpenAI executive claimed GPT-5 had solved ten Erdős problems. It had not — it had rediscovered existing solutions in the literature, and the post was deleted after public criticism. That episode is why the 2026 results were released with named mathematicians and Lean formalisation attached, and it is a good reason to check whether any future claim carries the same.

The problems were chosen by people. In each result above, a person or a lab picked the question and framed it, and mathematicians judged whether the output mattered. The Fields Medallists’ letter is, in part, an argument about who gets to make those choices.

What this means if you are doing homework

Very little — and the gap between these two realities is the most confusing thing about AI and maths right now. A model class that produced a Millennium Prize claim can still drop a digit in a long multiplication if you ask it casually with thinking mode off. Research-grade results come from thousands of agents, tool use and expert verification over days, at costs Quanta puts in the millions of dollars; your chat window at default settings is not that. The advice in the rest of this guide is unchanged.

The three types of AI math tool

Match the type to the job before comparing individual apps.

Most students end up using two: a solver or engine for the computation, and a reasoning model for the parts a solver cannot parse — the word problem, the “why”, and the conceptual leap.

Best AI math tools ranked (September 2026)

Ranked by overall suitability for a student or learner today, weighting accuracy, quality of the step-by-step working, teaching value, coverage and price. Prices are list; the free-tier column notes what you get without paying.

Also worth knowing: Desmos and GeoGebra are the two free standards for graphing, geometry and function visualisation, used in classrooms worldwide at no cost. For the general AI chatbots — Claude, Gemini, ChatGPT and the rest — see the dedicated model section below.

The best AI math tools compared

Ordered by current suitability for a learner, not by company size. Verify every price against the vendor’s live pricing page before you commit.

1. Wolfram|Alpha — most accurate, best for higher-level maths

Type: Computational engine Price: Pro from $5/month billed annually (or $9.99 monthly); Pro Premium from $8.25/month annually; a free Basic tier (Wolfram) Key features: Symbolic computation across 200+ topics, step-by-step solutions, graphing, unit and data support, student and educator pricing

Wolfram Alpha is the accuracy benchmark for maths. Because it computes answers from a symbolic-maths engine rather than predicting them with a language model, its results are essentially always correct for algebra, calculus and statistics when the query is well-formed. The free Basic tier returns answers; the Pro step-by-step solver expands each answer into a worked explanation across more than 200 topics in maths, chemistry and physics, from primary school to university. It is the one tool on this list you can point at a hard integral or a differential equation and trust the result.

Why it wins: Verifiable, derived answers rather than predicted ones — the highest accuracy available, and the deepest coverage of advanced maths.

Limitations: It is a calculator, not a teacher. It solves what you type but does not coach you, and it struggles with loosely-worded word problems that a chatbot parses easily. Full step-by-step working requires Pro.

Best for: University students, engineers and anyone who needs an answer that is correct, especially in calculus, linear algebra, differential equations and statistics.

2. Photomath — best camera solver for school maths

Type: Camera solver Price: Free tier; Photomath Plus $9.99/month or $69.99/year (about $5.83/month on the annual plan) (Google) Key features: Point-the-camera scanning of printed and handwritten problems, step-by-step solutions, animated tutorials and textbook-specific solutions on Plus

Photomath, now owned by Google, is the most established camera-first maths solver. Point your phone at a printed or handwritten problem and it recognises it and shows the worked steps. The free app already gives unlimited step-by-step solutions for many problem types; Photomath Plus ($9.99/month or $69.99/year) adds animated tutorials, textbook-specific deep-dive solutions and extra visual aids. It is tuned for the school and early-college curriculum — arithmetic, algebra, trigonometry and introductory calculus.

Why it wins: The smoothest scan-to-solution experience for school-level maths, a genuinely useful free tier, and the backing of Google.

Limitations: Coverage thins out at university level and on multi-step word problems, and the richest explanations sit behind Plus.

Best for: School and early-college students who want to photograph a problem set and see each step worked out.

3. Microsoft Math Solver — best free step-by-step solver

Type: Solver Price: Free (Microsoft) Key features: Typed, photo and handwriting input, full step-by-step solutions from arithmetic to calculus, graphs, practice problems and video links, no account required

Microsoft Math Solver is the strongest fully free option, and its advantage is blunt: it shows every step at no cost, where Mathway, Symbolab and Wolfram Alpha place their steps behind a subscription. It accepts a typed expression, a photo or handwriting, covers arithmetic through calculus, and adds graphs, similar practice problems and links to relevant video lessons — without asking for a card or even an account.

Why it wins: Complete step-by-step working for free, across a wide curriculum, with flexible input.

Limitations: It is a solver, not a tutor, and Microsoft has invested less in it recently than the dedicated app vendors; advanced-topic depth trails Wolfram Alpha.

Best for: Any student who wants worked solutions and cannot or does not want to pay — the default free pick.

4. Symbolab — best for practice and drilling a topic

Type: Solver plus AI tutor Price: Around $6.99/month, or about $59.99/year (roughly $5/month annually); a free tier with answers, limited steps and ads (Symbolab) Key features: Step-by-step solutions across algebra, calculus and trigonometry, conversational AI chat, randomised practice sets, quizzes, progress tracking and a graphing calculator

Symbolab pairs a step-by-step solver with a practice engine, which is what sets it apart. Beyond working a single problem, its paid tier generates randomised practice problems and quizzes, tracks your progress by topic, and lets you ask questions conversationally through an AI chat — turning a solver into a revision tool. The free tier shows final answers with limited step visibility and ads; the subscription unlocks full worked steps, verified solutions and an ad-free experience.

Why it wins: The best blend of a solver and a practice platform, strong for students revising a specific topic before an exam.

Limitations: Full value needs the subscription, and its raw-accuracy ceiling on the hardest problems still trails a pure computational engine like Wolfram Alpha.

Best for: Students who want to drill and self-test a topic — algebra, calculus, trigonometry — not just solve one question.

5. Khanmigo — best AI tutor for guided learning

Type: AI tutor Price: $4/month or $44/year for learners and parents; free for teachers (Khan Academy) Key features: Socratic tutoring that guides rather than answers, tight integration with Khan Academy’s maths curriculum, teacher tools

Khanmigo is Khan Academy’s AI tutor, and its defining choice is that it withholds the answer on purpose. Instead of solving the problem for you, it guides you to the solution through Socratic questioning, which makes it a learning tool rather than a homework-shortcut. Maths remains its strongest subject, sitting on top of Khan Academy’s long-standing curriculum, and it costs $4/month for learners and parents while staying free for teachers.

Why it wins: The best value in guided, learn-it-yourself maths tutoring, backed by a trusted education non-profit.

Limitations: If you want a fast answer rather than a lesson, its refusal to just tell you is a feature you will find frustrating. Depth outside maths is thinner.

Best for: Younger students, parents and teachers who want genuine learning and Socratic guidance rather than a solved answer sheet.

6. Mathos AI — best all-in-one solve, tutor and revise

Type: Solver plus AI tutor Price: Free tier with limited use of its standard model; Basic $5.98/week (unlimited standard solves, 100 a day on its most accurate model); Prime $9.98/week (unlimited on both, plus larger video and PDF quotas), with a three-day trial on paid plans (Mathos AI) Key features: Photo, voice or typed input, interrogatable step-by-step working, a real-time whiteboard tutor, and conversion of any solution into a quiz, flashcard deck or short video; a parallel PDF worksheet solver

Mathos AI (formerly MathGPTPro) is built on the idea that the answer is only half the job. You snap, speak or type a problem and get step-by-step working you can question, plus an AI Tutor whiteboard that coaches you in real time and can turn any solution into a quiz, flashcards or a short explainer video. Its PDF homework helper solves every page of a worksheet in parallel, which is genuinely useful around finals. The company reports more than 5 million students and over 100 million problems solved (Mathos AI).

Why it wins: The most complete study loop on this list — solve, understand, then revise — in one app, spanning symbolic algebra, derivatives, integrals and multi-step word problems.

Limitations: Paid plans are billed weekly, and Prime works out at roughly $43 a month — the priciest dedicated maths app here — and, like all LLM-driven solvers, its computation should be spot-checked on the hardest problems.

Best for: Students who want one paid app to solve, tutor and generate revision material together.

7. Thetawise — best for advanced and university-level tutoring

Type: AI tutor Price: Free plan with limited daily messages; a paid Pro plan for unlimited use, with a one-week free trial (Thetawise) Key features: Maths-tuned models plus calculation tools, a “Tutor Me” guided mode, image upload, a draw pad, voice-to-text and generated AI video lessons

Thetawise positions itself as an accuracy-first AI maths tutor for harder maths. Rather than a general-purpose chatbot, it combines large language models with specialised calculation tools to cut arithmetic errors, and it offers a guided “Tutor Me” mode alongside straight solving. The company reports more than 300,000 student users and strong performance on complex, upper-division college problems, though those accuracy and outcome figures are vendor-reported and should be read as such (Thetawise).

Why it wins: A tutoring experience aimed squarely at advanced and university maths, where general solvers thin out.

Limitations: Its standout accuracy and score-improvement claims come from the company, not an independent evaluator; treat them as directional. The best features need the Pro plan.

Best for: College and upper-secondary students working through advanced maths who want tutoring, not just a solved answer.

8. Gauth — fast K-12 and college STEM answers

Type: Camera solver Price: Free tier with limited daily solves and ads; premium from roughly $9.99–11.99/month, or $99.99/year (Gauth) Key features: Computer-vision scanning, step-by-step STEM solutions, a “DeepThinking” mode that explains the method, wide K-12 and college coverage

Gauth (formerly GauthMath), owned by ByteDance, pairs computer vision with language models to solve photographed STEM problems quickly. The company reports around a 95% solve rate across K-12 and college questions, and its DeepThinking mode aims to teach the method rather than only show the result (Gauth). In practice, accuracy is reliable on algebra, basic physics, chemistry and statistics and on straightforward textbook questions, but users report it is weaker on nuanced proof-based problems and advanced topics such as differential equations.

Why it wins: Fast, cheap answers for mainstream school and college STEM, with a teaching mode layered on top.

Limitations: The 95% figure is vendor-reported and problem-dependent; accuracy drops on proof-based and advanced work, so verify anything beyond standard textbook questions.

Best for: K-12 and college students who want quick, low-cost answers to standard STEM problems.

9. Mathway — broadest subject coverage with a camera

Type: Solver Price: Free tier (final answers only); Premium $9.99/month or $39.99/year (Mathway) Key features: Camera scanning, coverage from basic maths through calculus and statistics, graphing tools, offline problem-solving on Premium

Mathway, owned by Chegg, is a long-established solver covering basic maths, algebra, trigonometry, calculus and statistics. The free tier lets you enter a problem or scan it and returns the final answer; Premium ($9.99/month or $39.99/year) unlocks the step-by-step working, unlimited submissions, advanced graphing and offline solving. It is a competent, wide-coverage solver, though it sits behind Microsoft Math Solver on value because it charges for the steps that Microsoft gives away.

Why it wins: Broad subject coverage and a familiar, reliable solving workflow with a camera.

Limitations: The steps — the whole point of a solver — are paywalled, which is hard to justify when Microsoft Math Solver shows them for free.

Best for: Students who want one broad solver and are already in the Chegg ecosystem.

10. Julius AI — best for statistics and data maths

Type: Computational (data analysis) Price: Free tier (15 messages/month); Plus $35/month, Pro $45/month, Ultra $500/month; 50% off for students and educators (Julius AI) Key features: Upload a spreadsheet or connect a database, ask in plain English, and get computed statistics and charts back; notebooks for repeatable analysis

Julius AI is the pick when the maths is statistics and data rather than symbolic algebra. You upload a CSV or connect a database, ask a question in plain English, and it runs the computation and returns the statistics and a chart — executing real analysis code rather than estimating an answer, which keeps it accurate on quantitative work. Students and educators get 50% off, which softens the higher price relative to the school-focused solvers.

Why it wins: Genuine computational data analysis in plain language — the right tool for statistics, regressions and dataset maths.

Limitations: It is aimed at data work, not curriculum solving, and its individual plans start at $35/month, well above the dedicated maths apps.

Best for: Students and professionals doing statistics, data analysis and quantitative work on real datasets.

AI for geometry

Geometry is where camera solvers are weakest, because the problem usually lives in a diagram rather than an equation. Three tools cover it:

• GeoGebra Geometry (free) is the classroom standard for constructing and exploring figures: draw the shape, drag the points and watch angles, lengths and areas update. It does not solve a textbook question for you, which makes it the better tool for understanding one.
• A reasoning model with vision — GPT-6 Astra, Claude Opus 5.5 or a free chatbot tier — can read a photographed diagram and explain a proof or an angle chase step by step. Check that it has read the labels on the figure correctly before trusting the working.
• Wolfram Alpha handles the computational side — areas, volumes, coordinate geometry and trigonometry — exactly, once the problem is written down.

Photomath and Mathos AI also accept geometry questions, and both work best when the problem is stated in text or as clearly labelled numbers rather than a bare diagram.

AI for statistics

Statistics splits into two jobs: textbook problems (probability, distributions, hypothesis tests) and analysis of a real dataset.

• For textbook statistics, Wolfram Alpha computes probabilities, distributions and test statistics exactly, and Symbolab and Microsoft Math Solver show the working for standard problems.
• For real data, Julius AI runs the analysis as code on an uploaded spreadsheet and returns the statistics and charts; it is covered in the ranking above. A reasoning model with code execution does the same job for a one-off analysis.
• For regressions and plotting, Desmos (free) fits lines and curves to a table of points and graphs them.

The failure mode to watch is a chatbot estimating a p-value or a standard deviation instead of calculating it. If the number matters, make sure the tool ran the calculation — in code or in an engine — rather than stating it.

Best general AI models for math

The dedicated tools above solve and teach; the general reasoning models are what you reach for when the hard part is understanding the problem — the word problem, the proof, the concept — rather than the arithmetic. For the full cross-field ranking see best AI models; the maths-relevant picks are below.

Four points decide how well these do on maths.

The benchmarks that used to separate them are nearly gone. GSM8K and MATH are solved, and AIME gaps between frontier models fall inside run-to-run noise. Even FrontierMath Tier 4, Epoch AI’s hardest research-level set, is close to its ceiling: OpenAI reports 97.6% for GPT-6 Astra on its own launch table. Treat any model’s AIME score as table stakes, not a differentiator.

Read olympiad claims carefully. At IMO 2026 the only officially graded perfect scores went to Huawei’s Celia and Xiaohongshu’s dots-note-3.0, both 42/42 under the organisers’ own marking (France 24/AFP). Anthropic’s 42/42 for Claude Opus 5 was graded by a three-model judge panel with human experts marking one pre-specified solution per problem, and AFP reports that outside testing also gave OpenAI, Anthropic and Moonshot models full marks — neither is an official sitting. Gemini 3 Deep Think descends from the Gemini line that achieved officially graded IMO gold-medal standard in 2025. Note also that Gemini 3.5 Pro, with its built-in Deep Think mode, was still in limited preview at the end of August, months after its announcement.

Turn on code execution. A reasoning model that can run Python or call a calculator mid-answer is far more reliable on arithmetic than one predicting numbers directly. If your model offers a code, thinking or “analysis” mode, use it for anything numeric — this single setting closes most of the accuracy gap discussed below.

Vision lets you photograph the problem. All the models above accept images, so you can upload a photo of a problem the way you would with Photomath, then ask for the reasoning behind each step — a solver’s scan plus a tutor’s explanation in one.

Best free model for maths: DeepSeek V4, which is free in its app and free to self-host, with strong reasoning; ChatGPT’s free tier (on GPT-5.6 Luna), Claude’s free tier (on Sonnet 5) and Google’s free Gemini tier are all adequate for school and undergraduate work. The caveat below applies to all of them.

Accuracy: why chatbots get math wrong, and how to fix it

The single most important thing to understand about AI and maths is why the failure happens — and the answer now depends heavily on one setting. A computational engine like Wolfram Alpha computes an answer from a symbolic-maths system, so it is either right or it tells you it cannot parse the query. A language model answering directly predicts the most probable next token, which means it can generate a plausible-looking chain of steps with a wrong number buried inside and state it with full confidence. One 2026 comparison found a general chatbot produced arithmetic errors on roughly a third of complex, multi-step calculations, while Wolfram Alpha returned near-zero errors on well-formed algebra and calculus (Miniloop).

That figure is real but increasingly describes a configuration rather than a category. A model in thinking mode with code execution enabled does not predict the arithmetic at all — it writes and runs the calculation, which is why the same class of model can post vendor-reported scores above 90% on research-level FrontierMath problems and still fumble a long multiplication when answering off the cuff. The gap between those two behaviours is wider than the gap between any two vendors.

The fix is a workflow, not a single tool:

• Enable thinking and code execution first. This is the highest-leverage change available and it is free. A model that runs code for the arithmetic is dramatically more accurate than one answering directly.
• Compute in an engine, reason in a model. Use Wolfram Alpha, Microsoft Math Solver or a dedicated solver for the calculation, and a reasoning model like GPT-6 Astra or Claude Opus 5.5 for the word problem, the setup and the explanation.
• Cross-check the final number. For anything that matters — an exam answer, a grade, a real calculation — verify the result in a second tool before you rely on it.
• Read the steps, not just the answer. The value of a solver is the working; checking each step is how you both learn and catch the error.

This is the methodology behind our picks: we weight verifiable, derived accuracy over confident presentation, and we treat vendor accuracy claims as a ceiling to test, not a fact — which is why the olympiad and open-problem claims above are reported with their grading and verification conditions attached.

Use-case recommendations

The best tool depends on the job. Here are the decisive picks.

For guaranteed-correct answers and university maths

Winner: Wolfram Alpha Pro (from $5/month annually)

It computes rather than predicts, so its answers are derived and dependable — the pick for calculus, linear algebra, differential equations and statistics where being right is non-negotiable. Alternative: Julius AI when the maths is statistics on a real dataset.

For free step-by-step working

Winner: Microsoft Math Solver (free)

Complete worked steps from arithmetic to calculus, from a photo, typed input or handwriting, with no account. Alternative: the free tier of Photomath for camera-first school maths.

For photographing a school problem

Winner: Photomath (free; Plus $9.99/month)

The smoothest scan-to-solution flow for the school and early-college curriculum, now backed by Google. Alternative: Gauth for fast, cheap STEM answers.

For learning the method, not just the answer

Winner: Khanmigo ($4/month; free for teachers)

Socratic tutoring that guides you to the solution instead of handing it over — the best value in genuine maths learning. Alternative: Thetawise for advanced and university-level tutoring.

For the hardest competition and proof-level maths

Winner: GPT-6 Astra ($20/month via ChatGPT Plus)

It posts the highest reported score on the hardest maths benchmark still in use — 97.6% on FrontierMath Tier 4, on OpenAI’s own table — and OpenAI’s models produced the Erdős results discussed above. Alternatives: Gemini 3 Deep Think, whose lineage holds an officially graded IMO gold-medal standard, and Claude Opus 5.5 for proof-style write-ups.

For word problems and concept explanation

Winner: Claude Opus 5.5 ($20/month via Claude Pro)

The clearest at parsing a word problem, setting up the maths and explaining each step in plain language. Alternative: GPT-6 Astra through ChatGPT, whose free tier runs GPT-5.6 Luna.

For statistics and data maths

Winner: Julius AI (50% student discount)

Runs real analysis code on your uploaded data, so its statistics and charts are computed, not estimated.

For graphing and geometry

Winner: Desmos or GeoGebra (both free)

The two classroom standards for plotting functions, exploring geometry and visualising maths, at no cost.

For the best value paid tool

Winner: Symbolab ($5/month annually) or Photomath Plus ($5.83/month annually)

Both deliver full steps plus extras — practice sets on Symbolab, animated tutorials on Photomath — for roughly the price of a coffee a month.

Pricing comparison: what you’ll actually pay

Cost strategy: most students need to pay for nothing. Microsoft Math Solver covers free step-by-step solving, DeepSeek V4 or a free chatbot tier covers word problems and explanation, and Desmos covers graphing — a complete free stack. Pay only for what that stack lacks: Wolfram Alpha Pro for guaranteed accuracy on hard maths, Khanmigo for structured tutoring, or Photomath Plus for the smoothest camera experience.

How to choose an AI for math

Four questions decide the fit.

Do you need the answer, or to learn the method? If you need a correct answer, use a computational engine or solver — Wolfram Alpha or Microsoft Math Solver. If you need to learn, use a tutor that guides you — Khanmigo or Thetawise — which deliberately withholds the answer.

Is the hard part the arithmetic or the wording? A solver reads a clean equation; a reasoning model like Claude Opus 5.5 parses a messy word problem. Match the tool to where the difficulty actually lies.

What level are you working at? School and early college is solver territory (Photomath, Symbolab, Gauth); university and competition maths need a computational engine (Wolfram Alpha) or a top reasoning mode (GPT-6 Astra or Gemini 3 Deep Think).

How much do you trust the output? Never trust a language model’s arithmetic on a long calculation without checking it in an engine. The safest workflow across every level is compute-then-explain: an exact tool for the number, a reasoning model for the meaning.

We rank by suitability for a learner, weighting accuracy, quality of the working, teaching value, coverage and price, and we anchor every recommendation in the tool’s documented features and independent testing rather than marketing claims.

Related guides

For general study help across every subject, see best AI for students. For homework help across subjects rather than maths specifically, see best AI for homework, and for tutoring across subjects, best AI tutors. For research and long-document work, see best AI for research. For where these models sit across the whole field, see best AI models, and for coding-specific picks, best AI for coding.

Frequently asked questions

What is the best AI for math in 2026?

There is no single winner, because computing an answer and explaining it are different jobs. For guaranteed-correct answers, Wolfram Alpha is the most reliable because it computes symbolically rather than predicting text. For free step-by-step working, Microsoft Math Solver is the best no-cost tool. For word problems, proofs and explanation, a frontier reasoning model such as GPT-6 Astra or Claude Opus 5.5 is strongest. The reliable approach is to compute in an engine and reason in a model.

What is the best free AI for math?

Microsoft Math Solver is the best free tool for worked solutions — it shows every step from arithmetic to calculus, from a photo or typed input, with no account or subscription, where rivals paywall their steps. Photomath has a strong free camera tier for school maths, Desmos and GeoGebra are free for graphing and geometry, and DeepSeek V4 is a free, capable reasoning model for word problems and explanation.

Which AI is most accurate at math?

Wolfram Alpha is the most accurate, because it computes answers from a symbolic-maths engine rather than predicting them with a language model — its results are essentially always correct for well-formed algebra, calculus and statistics. General chatbots are far less reliable on raw calculation: one 2026 comparison found a chatbot erred on roughly a third of complex, multi-step sums. Enabling a chatbot’s code or “thinking” mode, which offloads arithmetic to an exact tool, closes much of that gap.

Can ChatGPT or Claude do math reliably?

They are excellent at understanding and explaining maths, and with thinking and code execution enabled they are reliable on computation too, because the model writes and runs the calculation instead of predicting it. Answering directly, with those modes off, is where the errors come from. Use their reasoning to set up and explain a problem, enable code execution — GPT-6 Astra and Claude Opus 5.5 both run code for the calculation — then verify anything that matters in a computational engine like Wolfram Alpha.

Has AI actually solved any unsolved math problems?

Yes — several, in 2026. OpenAI models solved a string of Erdős problems and in May disproved an 80-year-old Erdős conjecture on the planar unit-distance problem. In July, Anthropic mathematician Levent Alpöge used Claude Fable 5 to find a counterexample to the Jacobian conjecture, open since 1939. On 10 August, an unreleased Claude raised the proven share of Riemann zeta zeros on the critical line from 41.6% to 67.2%. On 8 September, OpenAI announced a Lean-formalised proof that the Navier–Stokes equations can blow up in finite time, and on 21 September it said the same internal model had resolved more than 100 open problems.

Three caveats matter. The Riemann hypothesis is not solved — the zeta result is progress on a related bound. The Navier–Stokes result is disputed: it uses an external force that the Clay problem allows but most researchers had set aside, and the Clay Institute has not recognised a solution. And the models behind the biggest results are internal or unreleased, so you cannot use them. See the full section above.

Has AI solved a Millennium Prize Problem?

OpenAI says it has. On 8 September 2026 it published a proof, with a Lean formalisation, that solutions to the 3D Navier–Stokes equations can develop a singularity in finite time when an external force is applied. The Clay problem statement permits that forced version, but many mathematicians argue it is not the version that mattered, and the Clay Institute has not recognised a solution. The other six Millennium Prize Problems, including the Riemann hypothesis, remain open.

Does that mean AI can do my university math now?

For understanding and explanation, largely yes. But do not read the research headlines as a claim about your chat window. Those results came from internal models running many agents for hours or days, with tool use and expert verification, and with people choosing the problem and judging the output. The same model class, asked casually with thinking mode off, can still make an arithmetic slip in a long calculation. Enable thinking and code execution, and verify anything that carries marks.

What is the best AI to show step-by-step math solutions?

For free steps, Microsoft Math Solver shows complete working at no cost. For a camera-first experience, Photomath is the school-level leader. For practice and drilling, Symbolab pairs steps with quizzes and progress tracking. For university-level maths, Wolfram Alpha Pro expands its answers into worked explanations across more than 200 topics.

What is the best AI math tutor?

Khanmigo, Khan Academy’s tutor, is the best value at $4/month (free for teachers): it uses Socratic questioning to guide you to the answer rather than handing it over, and maths is its strongest subject. For advanced and university-level tutoring, Thetawise is aimed at harder maths, and Mathos AI combines a solver with a whiteboard tutor and revision tools. A tutor is the right choice when you want to learn the method, not just get the answer.

Which AI is best for hard, competition-level or university math?

For the hardest competition and proof-level problems, GPT-6 Astra has the highest reported score on the hardest maths benchmark still in use (97.6% on FrontierMath Tier 4, on OpenAI’s own table), with Gemini 3 Deep Think — whose lineage holds an officially graded IMO gold-medal standard — and Claude Opus 5.5 the main alternatives. For university computation where the answer must be exact, Wolfram Alpha Pro is the dependable engine. The best results come from using a reasoning model for the approach and a computational engine for the arithmetic.

Is it cheating to use AI for math homework?

Using AI to understand a method, check your working or learn a concept is study support; submitting AI-generated answers as your own is typically against school and university policy. Tutoring tools like Khanmigo are designed around this line — they guide rather than answer — and the most durable use of any tool here is to read the steps and learn to reproduce them yourself, not to copy the result. Check your institution’s specific policy on AI assistance.

This guide is updated as maths tools launch and pricing changes. Pricing and free-tier details are list figures verified against each vendor’s pages and independent reviews; confirm current terms before subscribing. Accuracy comparisons cite independent testing where available and label vendor claims as vendor-reported. The open-problem results are reported with their verification conditions — Lean formalisation and named mathematicians — and with the disputes attached: the Riemann zeta result is progress on a bound, not a proof, and the Navier–Stokes claim has not been recognised by the Clay Institute. Model capabilities reference our own model pages. Pricing and availability current as of 26 September 2026 and subject to change.