Math is where AI chatbots earn or lose trust fastest. A wrong integral looks exactly like a right one, and a student who copies it learns nothing. So which assistant deserves your homework, your spreadsheets, or your proof attempts? This guide compares six of them: ChatGPT, Claude, Gemini, Perplexity, Microsoft Copilot and Mistral. Google made Gemini 4 Argon globally available on October 13, 2026, OpenAI rolled GPT-6 into ChatGPT on October 7 and 8, and Anthropic’s Claude 5.5 family landed between September 22 and October 7.
Here is the finding that shapes everything below. None of the six vendors has published a dated, standardized math-homework benchmark that ranks it against the others. I checked official launch pages, release notes and pricing documents for all six on November 5, and each one stresses coding, agents, science or security instead. Plenty of sites will still hand you a confident ranking of the best AI for math. This article will not, because a ranking without a test is a guess.
What you get instead is everything that can be verified today. That means the newest model each company ships, who can reach it and at what price, what a thousand math problems cost through each API, which dated changes hit your budget, and a 20-minute test you can run yourself. If you already live inside one assistant and fear losing your history, the guide to porting chatbot memory and chat history covers the move.
Best AI for Math: The Quick Verdict by Job
Start here if you only have a minute. The picks below rest on access, price, model positioning and dated facts, not on a head-to-head accuracy score, because no such score exists in the sources I reviewed. Treat them as a starting shortlist and confirm with the test later in this article.
- Free math homework help: ChatGPT Free, which runs GPT-6 Luna in regular chat, is the easiest place to start. Mistral also lists a Free plan.
- Hard multi-step problems at the standard paid tier: Claude Pro (Sonnet 5.5 and Opus 5.5 tier) plus the mid-priced ChatGPT and Google AI plans are the three to test side by side.
- The hardest problems, money no object: GPT-6 Pro (powered by GPT-6 Astra) starts on the $100 Pro tier. Claude Opus 5.5 and Gemini 4 Argon are the direct rivals.
- Cheapest per problem through an API: GPT-6 Luna at $1.60 per 1,000 illustrative problems, then Mistral Large 4 at $7.00.
- Math with sources and citations: Perplexity, because it wraps several third-party models in a web-grounded workflow.
- Spreadsheet math inside a company: Microsoft Copilot, if your employer already pays for Microsoft 365.
- Self-hosted, private math tutoring: Mistral Large 4, the only flagship in this group with downloadable weights.
The honest one-line answer to the best AI for math is this. Start with the free ChatGPT tier, check every answer, and move up only when the free model fails on problems that matter to you. The sections below show what each step up buys.
How to Judge a Math AI When No Benchmark Exists
A good AI math solver does three jobs. It gets the final answer right, it shows steps a human can audit, and it admits when it is unsure. Most chatbots manage the first job on easy problems and stumble on the other two. Since the vendors do not publish math-homework scores, I judge each assistant on five criteria that I can verify from official pages: model currency, access, cost, dated changes, and whether the weights or tools let you check the work.
Accuracy you can check
Accuracy matters most, and it is the hardest thing to compare from the outside. A single wrong answer in a sample of ten tells you little. A model that gets 18 of 20 problems right today can slip on a different 20 tomorrow. For context on the failure mode, see how often the big assistants invent answers in the Grok, ChatGPT and Gemini hallucination comparison. The practical fix is a verification habit. Substitute the answer back into the equation, differentiate your integral, or paste the result into a symbolic tool.
Explanations and tone
Tone is a real factor for homework. On October 29, OpenAI updated GPT-6 for Free and Go users, and for the fallback model that Plus and Pro users hit after their GPT-6 limits, to give more concise answers with less flattering language. For a student, a tutor that stops praising a wrong attempt is a feature. I will not claim one chatbot explains better than another, because I have no dated source that measures it. The test later in this article lets you judge explanation quality on your own course material.
Common Ways Chatbots Get Math Wrong
Knowing the failure patterns saves more time than knowing the model names. Every assistant in this comparison can produce a fluent, confident, wrong answer, and the errors tend to repeat in a handful of shapes. These are analysis from how language models work in general, not measured rates for any product, so treat them as a checklist for your own review.
- Dropped signs and swapped terms. A negative that vanishes between step three and step four often survives into the final answer, because the later steps stay internally consistent.
- Arithmetic slips inside long chains. Multi-digit multiplication and fraction work are classic weak spots. Ask the bot to recompute each line or to show the calculation separately.
- Unit and rounding traps. Word problems that mix hours and minutes, or metric and imperial units, let a model answer the wrong question smoothly.
- Invented theorems and citations. When you ask for a source, some assistants will cite a result that does not exist. Open the reference before you rely on it.
- Agreeing with a wrong attempt. If you paste your own flawed solution and ask whether it is right, an eager-to-please model may say yes. This is why the October 29 reduction in flattery matters.
- Notation mismatches. The bot may use a convention your course does not, such as a different sign for angles or a different meaning of log.
Each of these errors has a cheap countermeasure. Substitute, differentiate, estimate, or ask a second model. When two independent assistants agree on a result, the odds of a shared mistake drop, though they never reach zero. When they disagree, you have found the exact step that deserves your attention.
The Six Contenders and Their Newest Models
Model names changed fast this autumn, so a quick orientation helps. Every product below is described in its state as of November 5, 2026, and anything older is marked as history.
ChatGPT. OpenAI’s current generation is GPT-6. The rollout began on October 7 for Plus, Pro, Business and Enterprise users and reached Free and Go users on October 8. Paid tiers get GPT-6 Sol in regular chat, while Free and Go get GPT-6 Luna. At the top sits GPT-6 Astra, which OpenAI released on September 9 and describes as its highest-intelligence GPT-6 model. Astra powers GPT-6 Pro on the Pro $100, Pro $200, Business and Enterprise plans. GPT-5.6 is the previous generation and ChatGPT has replaced it as the default. OpenAI also lists GPT-6.1 Sol and GPT-6.1 Terra, but those are Work and Codex models, not the regular conversation models you use for homework.
Claude. Anthropic’s newest family is Claude 5.5, made up of Opus 5.5, Sonnet 5.5 and Haiku 5.5. Anthropic announced Opus 5.5 on September 22 and calls it the strongest model for agentic coding and knowledge work, at a typical cost 40% below Opus 5. Sonnet 5.5 runs more than 30% faster than Sonnet 5 and costs up to 30% less for most work. Haiku 5.5 arrived on October 7 as the fast, cheap option. Anthropic also lists Claude Mythos 5.1, but that model sits behind trusted-access programs for cyberdefenders and life scientists, so it is not a normal math option. See the Claude pricing page for current plans.
Gemini. Gemini 4 Argon is Google’s newest generally available model. Google announced a still stronger model, Gemini 4 Carbon, on October 28, but it is restricted to selected cyber defenders and trusted partners and is not a public option. Treating Carbon as a homework tool would be a mistake.
Perplexity, Copilot and Mistral. These three work differently. Perplexity is a search-and-agent platform that offers several third-party models, not one math model of its own. Microsoft describes Copilot as a family of products, from the consumer assistant to Microsoft 365 Copilot and GitHub Copilot. Mistral’s flagship is Mistral Large 4, a natively multimodal model with 1 trillion total parameters that the company released as open weights. How these three behave on math depends on which underlying model you pick. For a wider view of the field, the usage share comparison of Claude, ChatGPT, Gemini and Grok shows where the audience sits.
Specs Compared: Models, API Prices and Access
The table below compares the four vendors that publish a clear flagship price list. Perplexity and Copilot are platforms that resell or route to other models, so I cover them in their own sections instead of forcing them into rows they cannot fill. Every cell comes from an official page or release note reviewed on November 5, and “none listed” means I found nothing in those sources.
| Spec | ChatGPT (OpenAI) | Claude (Anthropic) | Gemini (Google) | Mistral |
|---|---|---|---|---|
| Newest model family | GPT-6 (Sol, Luna, Astra) | Claude 5.5 (Opus, Sonnet, Haiku) | Gemini 4 Argon | Mistral Large 4 |
| Rollout date | Oct 7 (paid), Oct 8 (Free, Go) | Opus 5.5 Sept 22, Haiku 5.5 Oct 7 | Oct 13, 2026 | Preview Oct 6, open weights Oct 29 |
| Vendor’s top tier for hard problems | GPT-6 Astra (GPT-6 Pro) | Opus 5.5 | Argon (Carbon is restricted) | Large 4 |
| Model in the free chat tier | GPT-6 Luna | Not confirmed on reviewed pages | Not confirmed on reviewed pages | Free plan listed, model not confirmed |
| Flagship API price per 1M tokens (in/out) | $10 / $50 (Astra) | $4 / $20 (Opus 5.5) | $2 / $10 until Nov 30, then $4 / $20 | $0.70 / $2.10 |
| Mid-tier API price per 1M tokens (in/out) | $2 / $10 (GPT-6 Sol) | $2 / $10 (Sonnet 5.5) | $0.75 / $3.75 until Dec 31 (3.8 Flash) | None listed |
| Cheapest listed model (in/out) | $0.10 / $0.50 (Luna) | Haiku 5.5 (see text) | $0.75 / $3.75 (3.8 Flash) | $0.70 / $2.10 |
| Cached input discount | $1 per 1M (Astra) | $0.10 per 1M (Sonnet 5.5 cache reads) | 95% off input price | $0.07 per 1M |
| Batch discount | Not confirmed | 50% off with batch processing | Not confirmed | Not confirmed |
| Open weights | None listed | None listed | None listed | Yes, modified MIT-style license |
| Self-hosting | None listed | None listed | None listed | Yes, from the released weights |
| Previous generation status | GPT-5.6 replaced in ChatGPT | Sonnet 4.5 API retires Nov 30 | 3.8 Flash still current | Large 4 preview retired Oct 29 |
| Dated math benchmark published | No | No | No | No |
Three patterns jump out. First, the flagship API prices run from $0.70 to $10 per million input tokens, a spread of more than 14 times. Second, only Mistral lets you download the weights, and the others keep the model behind a hosted service. Third, the bottom row is the same for all four vendors, which is the central problem for anyone trying to pick the best AI for math from marketing alone.
One naming wrinkle deserves a flag. Mistral’s own pages now use the name Vibe for parts of its consumer app, and several pages still say Le Chat. I use Le Chat for the chat assistant and note Vibe where the pages do. Mistral Medium 3.5, a 128-billion-parameter dense model released on May 22, became the default model in Le Chat that day. The pages I reviewed do not say whether Large 4 has replaced it as the default.
Benchmarks: What Has Actually Been Published
Benchmarks do exist for these models, just not for math homework. The table collects every numeric result from the official launch material I could verify, drawn from three sources: Mistral’s Large 4 announcement (which cites Cognition and Artificial Analysis), Mistral’s Medium 3.5 announcement, and Google’s Gemini model posts. I include them so you can see what vendors measure and why none of it answers a question about integrals.
| Source | Benchmark | Result | What it measures |
|---|---|---|---|
| Cognition (cited by Mistral) | Private FrontierCode 2.0 | Mistral Large 4 leads with 19.7% | End-to-end code quality and mergeability |
| Artificial Analysis (cited by Mistral) | Coding Agent Index | Large 4 scores 48%, tied with GLM-5.3, using 47% fewer tokens per task | Agentic coding efficiency |
| Mistral’s own testing | Dense 200 visual grounding | Large 4 at 42% versus GPT-6 Astra at 41% | Locating objects in images |
| Mistral | SWE-Bench Verified | Medium 3.5 scores 77.6% | Fixing real software issues |
| SEC-bench Pro | Gemini 4 Carbon scores 88.2% (restricted model) | Finding software vulnerabilities | |
| Chrome vulnerability patches | Gemini 3.8 Flash Cyber produced 2.6 times more correct patches than the best commercial models Google compared | Security patching |
Why coding and security scores do not transfer to math
Every row measures code, security or images. A model that fixes software bugs can still botch a probability question, and the reverse also holds. Several of these scores also come from the vendor’s own testing or from a private benchmark that outsiders cannot rerun. The visual grounding row is the closest to math, since geometry and graph problems depend on reading a figure correctly. Even there, a 42% versus 41% gap on one vendor-run test says almost nothing about your triangle problem.
The sources I reviewed say the same thing in plain terms. Google’s launch material for Argon stresses complex reasoning and scientific work but publishes no math-homework ranking. Anthropic’s announcements center on coding, knowledge work and science. OpenAI positions Astra as its most intelligent model, which is a product claim and not a measured result. Third-party claims that Mistral Large 4 matches or beats GPT-6 or Claude Opus 5.5 are company claims or secondary reports, not confirmed math results. For a broader look at how these families reason, the reasoning comparison of ChatGPT, Claude, Gemini and Grok is a useful companion.
Subscription Pricing: What Each Plan Costs
Consumer plans change often, and I only list prices that appear on official pages or release notes reviewed on November 5. Where a price did not appear, the table says so instead of guessing. That affects ChatGPT Plus and the Google AI plans, whose prices were not shown in the official excerpts I retrieved. Check each vendor’s page before you pay.
| Plan | Price | Math-relevant access |
|---|---|---|
| ChatGPT Free | $0 | GPT-6 Luna in regular chat, with limits on uploads and other features |
| ChatGPT Plus | Not confirmed on reviewed pages | GPT-6 Sol in regular chat, GPT-6.1 Sol in Work and Codex |
| ChatGPT Pro 100 | $100 a month | GPT-6 Pro (Astra), but not the Ultrafast tier |
| ChatGPT Pro 200 | $200 a month | GPT-6 Pro, restored for new subscribers on Nov 4 |
| ChatGPT Pro 500 | $500 a month | GPT-6 Astra Ultrafast and GPT-6.1 Sol Ultrafast, highest included usage |
| ChatGPT Business | $25 per user monthly, $20 billed annually | GPT-6 Pro on Business, credit-based usage |
| Claude Pro | $20 monthly or $200 billed annually | Claude 5.5 models |
| Google AI Pro and Ultra | Not confirmed on reviewed pages | Gemini 4 Argon in the Gemini app |
| Mistral Pro | $14.99 a month, $5.99 for verified students | Le Chat with Medium 3.5 as the default model |
A few notes on that table. The jump from Pro 100 to Pro 500 is a fivefold price increase, and the extra money buys speed tiers and usage, not a smarter base model. OpenAI also doubled the Work and Codex allowances for Pro 100 and Pro 500 on October 29 and canceled a planned cut to Pro 200. Business customers pay by credits, and OpenAI lists GPT-6 Pro at 50 credits per message against 10 credits per message for GPT-6 Sol. In plain terms, a hard-math habit on the top model eats credits five times faster.
Prices I could only find in secondary sources belong in the reported column. Perplexity’s pricing summary reportedly lists Pro at $20 a month, Max at $200 and a free Standard plan, with an Education Pro tier at $10 after student verification. Microsoft reportedly lists Copilot Business at $21 per user per month. Treat all of those as reported until you see them on the vendor’s page. If you are comparing the whole subscription field, the subscription pricing comparison and the student discount roundup go deeper on plan structure.
API Cost per 1,000 Math Problems
If you build a tutoring app, grade worksheets or generate practice sets, API pricing matters more than any subscription. I ran the arithmetic on every model with a verified price. The assumption is illustrative: each problem uses 1,000 input tokens and 3,000 output tokens, and the output count includes the reasoning a model spends before it answers. Your real numbers will differ, but the ratios hold.
| Model | Input / output per 1M tokens | Cost per 1,000 problems | Note |
|---|---|---|---|
| GPT-6 Luna | $0.10 / $0.50 | $1.60 | Cheapest verified option |
| Mistral Large 4 | $0.70 / $2.10 | $7.00 | Open weights available |
| Gemini 3.8 Flash | $0.75 / $3.75 | $12.00 | $24.00 from Jan 1, 2027 |
| GPT-6 Sol | $2 / $10 | $32.00 | OpenAI cut prices 50% on Nov 3 |
| Claude Sonnet 5.5 | $2 / $10 | $32.00 | Cache reads $0.10 per 1M |
| Gemini 4 Argon (introductory) | $2 / $10 | $32.00 | Through Nov 30, 2026 |
| Gemini 4 Argon (standard) | $4 / $20 | $64.00 | After Nov 30, 2026 |
| Claude Opus 5.5 | $4 / $20 | $64.00 | Fast mode costs $8 / $40 |
| GPT-6 Astra | $10 / $50 | $160.00 | Listed Nov 2 for GPT-6 Pro |
The spread is wide. GPT-6 Astra costs 100 times more than GPT-6 Luna for the same imagined workload, and Opus 5.5 costs twice as much as Sonnet 5.5. Gemini 4 Argon doubles in price after November 30, so any estimate you make this week expires within a month. Anthropic also lists Haiku 5.5 at $0.10 input and $0.50 output for prompts up to 100,000 tokens, which would match Luna’s price, according to its launch page.
A teacher who wants 40 practice problems with worked solutions would spend about six cents on Luna and about $6.40 on Astra under these assumptions. That gap matters at classroom scale and barely registers for a single student. The smart play is a two-model setup. Draft with the cheap model, then send only the problems that fail verification to the expensive one.
The Dated Changes That Affect Your Budget
Autumn 2026 moved faster than most buyers’ guides. The timeline below lists every dated event from the official sources that changes which model you get or what you pay. Three of them land in the next four weeks, so they shape what to buy this month.
| Date | Event | Why it matters for math |
|---|---|---|
| Sept 9 | OpenAI releases GPT-6 Astra | Sets the top of the GPT-6 ladder |
| Sept 22 | Anthropic announces Opus 5.5 | New Claude flagship, priced 40% below Opus 5 on typical work |
| Oct 6 | Mistral Large 4 enters public preview | Preview version retired on Oct 29 |
| Oct 7 | GPT-6 reaches paid ChatGPT, Haiku 5.5 ships | GPT-5.6 stops being the default |
| Oct 8 | GPT-6 Luna reaches Free and Go | Free users get the new generation |
| Oct 13 | Gemini 4 Argon goes global | Introductory price starts at $2 and $10 |
| Oct 28 | Google announces Gemini 4 Carbon | Restricted to trusted partners, not public |
| Oct 29 | Mistral releases Large 4 weights, OpenAI trims flattery | Self-hosting becomes possible, answers get terser |
| Nov 2 to Nov 9 | Copilot fair-use limits roll out by organization size | Heavy spreadsheet users may hit caps |
| Nov 3 | OpenAI lists GPT-6 Sol at $2 and $10, Luna at $0.10 and $0.50 | Described as 50% cuts from GPT-5.6 promotional pricing |
| Nov 4 | ChatGPT Pro 500 introduced at $500 a month | New top rung with Ultrafast models |
| Nov 30 | Argon price rises, Claude Sonnet 4.5 API retires | Budget and migration deadline |
| Dec 1 | Standalone Microsoft 365 Copilot purchasing begins | Same prices, no Microsoft 365 base subscription required |
| Dec 31 | Gemini 3.8 Flash introductory price ends | Rises to $1.50 and $7.50 from Jan 1, 2027 |
The practical lesson is to avoid long commitments. Annual plans lock in a price while the model underneath changes every few weeks. A monthly plan you can cancel costs a little more per month and saves you from paying twelve months for a model that a rival has already passed.
What Experts Say About AI and Math
Two lab chiefs have described the progress in their own words, in remarks that WBUR’s On Point covered on October 13, 2026. Their statements are company perspectives, not independent measurements, so read them as claims about direction rather than proof about any one product.
Sam Altman, CEO of OpenAI, said in remarks at a United Nations briefing covered by WBUR’s On Point: “Three summers ago, our models were okay at grade school math. Two summers ago, they were pretty good at high school math competitions. Last summer, we reached a gold level medal in the most prestigious international math competition.”
Dario Amodei, CEO of Anthropic, offered a parallel view in the same On Point segment: “Four years ago, it could barely write a line of code or solve a high school math problem. Today, it writes most of the code at Anthropic and solves world-famous unsolved math problems.”
Altman also addressed what AI-generated discoveries mean for mathematicians. In a podcast discussion covered by The Verge, he said: “I think, on net, the world deserves to understand how the universe works.”
What do these quotes tell a student or a developer? They show that both labs treat math as a flagship proof point and will keep pushing it. They do not tell you which model solves your problem set. Competition-level results and homework reliability are different skills, and the leaps described above concern the first.
ChatGPT, Claude and Gemini: Pros and Cons for Math
ChatGPT. The biggest strength is the ladder. You can start at $0 on GPT-6 Luna, move to GPT-6 Sol on a paid plan, and reach GPT-6 Astra through GPT-6 Pro for hard problems. That ladder is unusually clean. The cons are cost and clutter. The top rung runs $100 to $500 a month, and OpenAI now splits regular chat from Work and Codex, which have separate model lineups. A student who wants GPT-6.1 Sol will not find it in the normal chat window.
- Pros: free GPT-6 Luna in regular chat, a documented path up to Astra, a concision update on October 29 that cuts flattery, and API Luna pricing at $0.10 and $0.50.
- Cons: Plus pricing was not confirmed on the pages I reviewed, Astra costs $10 and $50 per million tokens, and GPT-6 rollout was still staged on November 4, so your account may lag.
Claude. Anthropic gives you three clear tiers: Opus 5.5 for the hardest reasoning, Sonnet 5.5 for balance, and Haiku 5.5 for fast and cheap work. Researchers get a sweetener, since verified principal investigators can receive a Claude Team subscription with free Standard seats for their teams and Premium seats at $15 a month for up to a year. The research lab seat comparison lays out how that stacks against rivals. On the downside, Anthropic’s materials emphasize coding and agents, and no official page I found offers a downloadable or offline Claude.
- Pros: clear three-tier lineup, Opus 5.5 priced 40% below Opus 5 on typical work, a 50% batch discount, and low Sonnet 5.5 cache reads at $0.10 per million tokens.
- Cons: no confirmed free-tier model detail, no local option, and Mythos 5.1 and Fable 5.1 sit in specialized or premium lanes.
Gemini. Google’s pitch is breadth. Argon reaches the Gemini API, AI Studio, Google Cloud, the Gemini app and AI Mode in Search. On October 29, Google also said Gemini could generate, preview and edit documents, PDFs, presentations and spreadsheets directly in the product, which suits anyone who wants a worksheet formatted along with its solutions. The catch is the clock. The introductory $2 and $10 price ends on November 30, and in the Gemini app, Argon is limited to Google AI Pro and Ultra subscribers.
- Pros: a 95% discount on cached input, wide product reach, and Gemini 3.8 Flash as a cheap fallback.
- Cons: standard pricing doubles after November 30, Carbon is restricted, and Google’s launch material gives no math ranking.
Perplexity, Copilot and Mistral: Pros and Cons for Math
Perplexity. Think of it as a router with a search engine attached. According to its published model list, Pro and Max subscribers can choose among third-party models, and Max reportedly includes GPT-6 Astra, Gemini 4 Argon and Claude Opus 5.5. That means you could run one hard problem through three flagship models in a single interface, which makes disagreement easy to spot. When two models disagree, you know to check by hand. For research-style math questions, the citations are a real advantage. Perplexity’s strength is retrieval, not computation, so pair it with a verification step. See how it compares in the Perplexity, ChatGPT and Gemini matchup and the deep research comparison.
Microsoft Copilot. Copilot is the odd one out because it is a product family, not a single model. Microsoft introduced a redesigned Copilot with Home, Code and Autopilot on September 25 and announced the Copilot harness, a multi-model agentic system, in late October. Copilot Chat remains included with Microsoft 365 subscriptions. Starting November 2, Microsoft began applying fair-use limits to additional Copilot Chat features for organizations with 2,000 or fewer seats, and larger organizations follow from November 9. Microsoft pairs a per-user license with Copilot Credits for heavier usage. For spreadsheet formulas at work, that is convenient. For a student, there is no clear reason to choose Copilot over a free ChatGPT account.
Mistral. Mistral stands apart by shipping weights. It released Mistral Large 4, a 1-trillion-parameter, natively multimodal model, in preview on October 6 and as open weights on October 29 under a modified MIT license. Fortune reported that Mistral calls it the best open model for agentic coding and cyber defense, which is a company claim and says nothing about math. The API price of $0.70 and $2.10 per million tokens is the second cheapest on my list. A trillion parameters demands serious server hardware, so self-hosting suits a school district or a company, not a laptop. Verified students can get Mistral Pro for $5.99 a month, down from $14.99.
- Perplexity pros: web-grounded answers, several models in one place. Cons: it is not a single math model, and the prices I found are reported, not confirmed.
- Copilot pros: bundled with Microsoft 365, strong fit for spreadsheet work. Cons: new fair-use limits, no named math model, and a split product line.
- Mistral pros: open weights, low API price, a $5.99 student plan. Cons: flagship pitched at coding and agents, and the Le Chat versus Vibe naming is confusing.
Seven Real-World Math Jobs and the Best Fit
The scenarios below show how the verified facts translate into choices. They are planning examples, not test results. I did not run these problems, and I will not pretend a model scored well on them.
1. A ninth grader with an algebra worksheet. Cost matters and the problems are short. ChatGPT Free on GPT-6 Luna covers it at $0. Use the tutor prompt later in this article so the bot asks for your attempt first and explains each step.
2. A calculus student checking integrals. Here the risk is a confident wrong answer. A paid plan that unlocks GPT-6 Sol, Claude Pro with Sonnet 5.5 or a Google AI Pro plan with Argon is worth testing side by side. Differentiate every antiderivative before you trust it.
3. An engineering student formatting a problem set. Google’s October 29 update lets Gemini generate and edit documents, PDFs and spreadsheets in the product. A student who needs a clean worksheet with solutions gains from that, as long as the numbers get checked.
4. A teacher writing 40 practice problems. This is an API job. At the illustrative rate, GPT-6 Luna costs about six cents for the batch, and Mistral Large 4 about 28 cents. Generate the problems cheaply and grade the answers with the script in the testing section.
5. A graduate student surveying a theorem’s literature. Retrieval beats raw reasoning here. Perplexity’s search-first design suits finding papers and prior results, and then you move the hard step into a stronger model. The earlier deep research comparison covers the trade-offs.
6. A researcher attacking competition-style or open problems. This is the only job that justifies the top rung. GPT-6 Pro on the $100 tier, Opus 5.5 and Argon are the three to rotate through. At about $160 per 1,000 illustrative problems for Astra and $64 for Opus 5.5 or standard-price Argon, run the expensive models only on problems the cheap ones fail.
7. A school district that needs private tutoring. Student data and local control push toward self-hosting. Mistral Large 4’s open weights are the only flagship option in this group, and Mistral’s release page reportedly points to serving tools such as vLLM and SGLang. Plan for real GPU capacity, or use the hosted API at $0.70 and $2.10 per million tokens as a middle path.
Use-Case Recommendations at a Glance
Pulling the examples together, these are the recommendations I would stand behind today, each tied to a verified fact. Voice tutoring is a separate need, and the ChatGPT, Grok and Gemini Live voice comparison covers it.
- Best free starting point: ChatGPT Free with GPT-6 Luna, since it costs $0 and runs the current GPT-6 generation.
- Best for hard problems on a budget: test Claude Pro against a mid-priced ChatGPT and Google AI plan for one week, then keep the one that passes your own problems.
- Best for the toughest work: GPT-6 Pro from the $100 tier, with Opus 5.5 as the main rival.
- Best for app builders watching cost: GPT-6 Luna at $1.60 per 1,000 illustrative problems, escalating to Mistral Large 4 at $7.00.
- Best for private deployment: Mistral Large 4 open weights.
- Best for office spreadsheets: Microsoft Copilot inside an existing Microsoft 365 plan.
- Best for sourced answers: Perplexity, with a manual verification step.
Broader roundups sit in the AI and machine learning section if you want to compare these assistants on tasks beyond math.
Migration Guide: Switching Math Chatbots Without Losing Work
Switching assistants costs you context. Your tutor instructions, your notation preferences and your list of problems that tripped the old bot all live inside it. Follow these steps in order and the move takes about an hour.
- Export what you can. Copy your saved instructions and any memory entries into a plain text file.
- Keep a problem log. List the ten problems your current bot got wrong, with the correct answers. These become your regression test.
- Paste the tutor prompt below into the new assistant’s custom instructions or first message.
- Run the ten problems on the new bot and compare against your log before you cancel anything.
- Keep the old plan for one billing cycle so you can fall back.
- If you use an API, update model names and prices before the dates below.
Three dated changes affect API users right now. Anthropic announced that the Claude Sonnet 4.5 model (claude-sonnet-4-5-20250929) retires from the Claude API on November 30, 2026, and recommends moving to Sonnet 5.5. Gemini 4 Argon leaves its introductory price on the same date, climbing from $2 and $10 to $4 and $20 per million tokens. And ChatGPT has moved from the GPT-5.6 generation to GPT-6, so any hard-coded model name from the summer deserves a review.
You are a patient math tutor.
1. Ask me to show my own attempt before you solve anything.
2. Work one step at a time and name the rule you use at each step.
3. After the final answer, check it by substituting it back or differentiating.
4. If you are not sure, say so plainly and show how I can verify it.
5. Use plain text notation such as x^2 and sqrt(x).That prompt works on every assistant covered here, and it enforces the habit that matters most: verification. Copilot and Perplexity let you pick or inherit models, so also note which underlying model answered when you log results.
Build Your Own Math Benchmark in 20 Minutes
Since no vendor publishes the numbers you need, make your own. Pick 20 problems from your actual coursework or work, five each from algebra, calculus, probability and word problems with unit traps. Write the correct answer for each. Ask every chatbot the same question three times, with the tutor prompt on, and save only its final answer.
Put the results in a CSV file with four columns: id, problem, expected, answer. Use one file per chatbot. The short script below checks algebraic equivalence with SymPy, so 2*x and x + x both count as correct, and it falls back to a plain text match for non-symbolic answers.
import csv
import sys
from sympy import simplify
from sympy.parsing.sympy_parser import (
parse_expr,
standard_transformations,
convert_xor,
implicit_multiplication_application,
)
TRANSFORMS = standard_transformations + (
convert_xor,
implicit_multiplication_application,
)
def same(expected, answer):
try:
diff = parse_expr(expected, transformations=TRANSFORMS) - parse_expr(
answer, transformations=TRANSFORMS
)
return simplify(diff) == 0
except Exception:
return expected.strip().lower() == answer.strip().lower()
def score(path):
right = 0
total = 0
with open(path, newline="") as handle:
for row in csv.DictReader(handle):
total += 1
if same(row["expected"], row["answer"]):
right += 1
return right, total
if __name__ == "__main__":
for path in sys.argv[1:]:
right, total = score(path)
print(f"{path}: {right}/{total} correct")Run it with python score.py gpt6.csv claude55.csv gemini4.csv. Three runs per problem matter because chatbots vary between attempts. Count a problem as passed only if all three runs are correct, or report the pass rate across all 60 attempts. Record the plan and model name beside each file, since a free tier and a paid tier of the same product can behave differently.
Add a cost line while you are at it. For API runs, note input and output tokens and multiply by the price table above. For subscriptions, divide the monthly price by how many problems you realistically solve per month. The cheapest assistant that passes your 20 problems is the best AI for math for you, whatever any ranking says.
Final Verdict: Best AI for Math in November 2026
No chatbot earns a gold medal on math homework yet, because nobody has published the evidence. What the data does support is a clear decision rule built on price, access and recency.
- Most people: start with ChatGPT Free on GPT-6 Luna. It costs $0, runs the newest OpenAI generation, and gives you a baseline.
- Students and professionals who need more: run your 20-problem test on Claude Pro, a mid-priced ChatGPT plan and a Google AI plan, then keep the winner.
- Researchers and competition solvers: GPT-6 Pro from $100 a month, with Claude Opus 5.5 and Gemini 4 Argon as rivals. At $160, $64 and $64 per 1,000 illustrative problems, use them selectively.
- Developers and teachers: GPT-6 Luna at $1.60 per 1,000 problems, then Mistral Large 4 at $7.00. Escalate failures to a bigger model.
- Privacy-first deployments: Mistral Large 4 open weights.
My take: pay for the cheapest tier that passes your own test, and never skip verification. The leaders will keep swapping places, because GPT-6.1 Sol, Gemini 4 Carbon and the next Claude release are all in motion. Rerun the benchmark whenever a vendor announces a new model, and update your pick.
Frequently Asked Questions
What is the best AI for math homework right now?
No source I reviewed publishes a head-to-head math-homework benchmark, so I cannot name a proven winner. ChatGPT Free on GPT-6 Luna is the best low-risk starting point because it costs $0 and uses the current GPT-6 generation. Test paid options on your own problems before you upgrade.
Is ChatGPT better than Claude at math?
There is no dated, standardized result that says so either way. OpenAI positions GPT-6 Astra as its most intelligent GPT-6 model, and Anthropic positions Opus 5.5 as its strongest model for agentic coding and knowledge work. Both are product claims. Run the same 20 problems through each and compare.
Is there a free AI math solver I can trust?
Free tiers exist, including ChatGPT Free with GPT-6 Luna and a Free plan from Mistral. Trust comes from checking, not from the price tag. Substitute answers back into the equation or verify them with a symbolic tool such as SymPy.
Which AI is cheapest for building a math app?
Among models with verified prices, GPT-6 Luna is cheapest at $0.10 per million input tokens and $0.50 per million output tokens. Under an assumed 1,000 input and 3,000 output tokens per problem, that works out to $1.60 per 1,000 problems. Mistral Large 4 comes next at $7.00.
Can I use Gemini 4 Carbon for math?
No. Google announced Carbon on October 28, but access is limited to selected cyber defenders and trusted partners through the Fairwind Program. Gemini 4 Argon is the newest generally available Google model.
Does Perplexity solve math problems?
Perplexity is a multi-model search and agent platform, not one math model. Its value for math lies in retrieval and the choice of underlying models, so check its answers the same way you would any other chatbot’s.
Will AI chatbot prices change soon?
Yes, at least for Gemini. Gemini 4 Argon’s API price rises from $2 and $10 to $4 and $20 per million tokens after November 30, 2026, according to Google Cloud’s pricing.
Can I run a math AI on my own hardware?
Of the flagships here, only Mistral Large 4 offers downloadable weights, under a modified MIT license. It has 1 trillion parameters, so it needs serious server capacity. The sources I reviewed confirm no downloadable or offline version of the ChatGPT, Claude or Gemini flagships.