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AI can solve many math problems—including some advanced competition problems—but no single benchmark score tells you whether a particular answer is correct. Reliability varies with the model, problem type, prompt, available tools, and grading method. Before trusting a solution, check that the AI understood the question, verify its assumptions and calculations, and inspect each important step of its reasoning.
Can AI solve math problems reliably?
Sometimes, and often impressively—but not reliably enough to accept an answer just because it sounds confident or includes a polished derivation. A model may solve one kind of problem well and make mistakes on another. A benchmark measures results on its particular questions under its particular test conditions; it is not a universal accuracy rate for math.
NIST’s Center for AI Standards and Innovation (CAISI) reported results for six models across three competition-style benchmarks in its 2025 evaluation. The figures below are benchmark accuracy percentages with the report’s standard error of the mean. CAISI used an LLM judge, o4-mini, to determine whether submitted mathematical expressions were equivalent to the ground truth. They describe those tests and scoring procedures—not the probability that a model will get your next question right. Read the NIST CAISI report.
| Benchmark (publisher and year) | GPT-5 | Anthropic Opus 4 | OpenAI gpt-oss | DeepSeek V3.1 | DeepSeek R1-0528 | DeepSeek R1 |
|---|---|---|---|---|---|---|
| SMT 2025 | 91.8% ± 1.5 | 82.2% ± 4.4 | 82.3% ± 4.3 | 86.2% ± 3.3 | 87.6% ± 2.8 | 75.0% ± 5.2 |
| OTIS-AIME 2025 | 91.9% ± 2.0 | 66.7% ± 8.0 | 72.9% ± 6.2 | 77.6% ± 6.0 | 73.3% ± 6.2 | 58.3% ± 7.7 |
| PUMaC 2024 | 85.9% ± 3.5 | 69.1% ± 5.8 | 67.3% ± 4.9 | 77.7% ± 4.0 | 72.7% ± 5.5 | 60.9% ± 5.3 |
Source for every result in this table: NIST CAISI, 2025. Values are accuracy (% of tasks solved) ± standard error of the mean, using the report’s expression-equivalence grading method.
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The table also shows why a single “AI math accuracy” number is misleading: model rankings and gaps vary from test to test. And even these named tests have a narrow scope:
- SMT 2025: 58 text-only advanced high-school problems spanning algebra, calculus, discrete mathematics, and geometry.
- OTIS-AIME 2025: 30 advanced high-school problems whose answers are integers from 0 to 999.
- PUMaC 2024: 55 text-only problems presented without visual diagrams.
These evaluations provide evidence about those competition-style questions. They do not establish how well a model reads a diagram, handles every classroom exercise, constructs a rigorous proof, or performs in a particular real-world use.
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What benchmark scores can—and cannot—tell you
Check whether the tests are comparable
A percentage is meaningful only with its context. When comparing published claims, look for the exact benchmark, model and version, test date, tools or compute conditions, number of attempts or sampling method, and grading method. Exact-answer scoring, expression-equivalence checks, and human review of proofs measure different things. If a company has not stated a condition, treat it as unspecified rather than assuming it matches another test.
Scores can also be less informative if test questions were included in a model’s training or otherwise seen in advance. Google DeepMind’s August 27, 2026 discussion of double-blind evaluations warns: “If a model has already seen the test questions – a problem known as benchmark contamination – the results can only be trusted to an extent.” That is a warning about how to interpret benchmark results, not evidence that any particular result is contaminated. Google DeepMind’s evaluation discussion.
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Treat separately published scores as separate claims
Google DeepMind’s model evaluation page lists Gemini 3.1 Deep Think at 81.5% on International Math Olympiad 2025 mathematics. That vendor-published result concerns a different benchmark from the NIST tests above; the percentages should not be ranked against one another as if the questions and conditions were identical. See Google DeepMind’s model evaluation page.
In a January 2026 post, Google DeepMind reported that Gemini Deep Think scored up to 90% on IMO-ProofBench Advanced as inference-time compute scaled, with human experts grading the stated results. The same post showed approximately 38% at the plotted highest point on the company’s internal FutureMath Basic PhD-level exercises, compared with an approximately 46% Aletheia marker. These are vendor-reported figures on named tests; the FutureMath figure is an internal-benchmark result, and neither should be treated as a general accuracy rate. Read Google DeepMind’s January 2026 post.
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What to check before trusting an AI solution
Use the checks that fit the answer’s claim. A calculator can catch arithmetic slips; it cannot show that the problem was interpreted correctly or that a proof is valid.
- Confirm the problem was understood. Compare the response with the original question. Check the requested quantity or form, constraints, domain, units, and all information in the prompt.
- Inspect the assumptions. Look for conditions the model added without permission or necessary conditions it overlooked. For example, dividing by an expression requires checking whether that expression can be zero.
- Recompute key arithmetic independently. Check important sums, products, substitutions, and decimal approximations with a separate method or calculator. Recalculate the decisive values, not just a convenient example.
- Check algebra against the original. Substitute a proposed solution back into the starting equation where possible. Look for sign errors, invalid cancellations, lost solutions, extraneous roots, and division by zero.
- Audit every consequential proof step. Ask which definition, given fact, or valid theorem justifies each inference. A convincing explanation is not itself a proof; if one important step does not follow, the conclusion is not established.
- Verify what the model read in a diagram or word problem. Check that it identified the correct labels, quantities, and relationships. The NIST benchmarks described above were text-only, so their results do not establish diagram-reading reliability.
- Escalate consequential decisions. If a mathematical error could cause meaningful harm, have a qualified person verify the result before relying on it. The benchmark results cited here do not determine whether a model is suitable for any particular high-stakes use.
Why fluent reasoning still needs checking
A step-by-step explanation can make a wrong answer easier to inspect, but fluency does not guarantee that its steps are sound. OpenAI’s September 5, 2025 explainer defines the risk directly: “Hallucinations are plausible but false statements generated by language models.” OpenAI says such errors can occur even on apparently straightforward questions. The useful response is to verify the derivation rather than treating confident wording as evidence. Read OpenAI’s explainer.
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