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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Yes—AI can solve many math problems, including some advanced competition problems, but it is not reliably correct on every task. Results depend on the model, problem type, prompt, tools, and how answers are graded. A strong benchmark score is evidence about that particular test, not a guarantee about your problem. Before trusting a solution, check that the system understood the question, that its assumptions and steps are valid, and that key calculations work independently.
How reliable is AI at math?
There is no single accuracy figure that describes AI’s ability to solve math problems. The National Institute of Standards and Technology’s Center for AI Standards and Innovation (NIST CAISI) evaluated six models on three competition-style benchmarks in its 2025 report. The scores varied by model and by test, so they should be read as results for those specific evaluations—not as universal accuracy rates.
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NIST reported accuracy as the percentage of tasks solved, with standard error of the mean shown below. It used an LLM judge, o4-mini, to assess whether a submitted mathematical expression was equivalent to the ground truth. The reported figures do not measure every aspect of a solution, such as whether a proof is rigorous or whether a model interpreted an image correctly.
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| Benchmark (publisher/year) | GPT-5 | Anthropic Opus 4 | OpenAI gpt-oss | DeepSeek V3.1 | DeepSeek R1-0528 | DeepSeek R1 |
|---|---|---|---|---|---|---|
| SMT 2025, NIST CAISI | 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, NIST CAISI | 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, NIST CAISI | 85.9% ± 3.5 | 69.1% ± 5.8 | 67.3% ± 4.9 | 77.7% ± 4.0 | 72.7% ± 5.5 | 60.9% ± 5.3 |
These numbers come from NIST CAISI’s 2025 evaluation report. The benchmark boundaries matter: SMT 2025 used 58 text-only advanced high-school problems across algebra, calculus, discrete mathematics, and geometry; OTIS-AIME 2025 used 30 advanced high-school problems with integer answers from 0 to 999; and PUMaC 2024 used 55 text-only problems without visual diagrams. These results do not establish performance on every kind of schoolwork, proof, applied problem, or diagram.
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Why benchmark scores do not guarantee a correct answer
The tests cover a defined slice of mathematics
A benchmark samples particular topics, formats, and difficulty levels. A model’s score on text-only contest problems cannot tell you whether it will correctly read a geometry diagram, follow a teacher’s required method, or interpret a word problem with ambiguous units. The grading method matters too: checking whether a final expression is equivalent to an answer is different from evaluating every line of a proof.
Test conditions and exposure can change results
For a model score to be meaningful, readers need context such as the test set, model and version, evaluation date, available tools or compute, number of attempts, and grading method. Google DeepMind warns that a model may have seen test questions during training: “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 the company’s statement in its August 27, 2026 discussion of evaluation integrity.
Different publishers’ results are not automatically comparable
Google DeepMind’s model page lists 81.5% for Gemini 3.1 Deep Think on International Math Olympiad 2025 mathematics. Its January 2026 post reports Gemini Deep Think scoring up to 90% on IMO-ProofBench Advanced as inference-time compute scales, with human experts grading the stated results. The post also reports approximately 38% on its internal FutureMath Basic PhD-level exercises at the plotted highest point, compared with an Aletheia marker of approximately 46%. These are vendor-reported results on distinct tests and setups; they should not be ranked directly against NIST’s results as if conditions and scoring were identical. See Google DeepMind’s model evaluation page and its January 2026 post.
What to check before trusting an AI math solution
- Confirm it answered the actual question. Check the requested quantity, domain, constraints, units, and answer format. A correct calculation for a misread problem is still the wrong answer.
- Inspect the assumptions. Look for conditions the prompt did not grant, or necessary conditions the solution omitted. For example, dividing by an expression requires checking whether it could equal zero.
- Recompute important arithmetic independently. Recalculate key sums, products, substitutions, and approximations. A scientific calculator can help verify numerical arithmetic, but it cannot establish that the model chose the right method or interpreted the problem correctly.
- Check algebraic transformations. Substitute a proposed answer into the original equation where possible. Watch for sign errors, lost solutions, extraneous roots, and division by zero.
- Demand justification for proof steps. Each consequential inference should follow from a definition, a stated assumption, or a valid theorem. A convincing-sounding explanation is not itself a proof.
- Verify visual and word-problem inputs. Confirm that labels, quantities, units, and diagram relationships were read correctly. The NIST benchmarks described above are text-only, so their results do not establish diagram-reading reliability.
- Get qualified review when errors have consequences. Benchmark scores alone do not determine whether an AI solution is suitable for a particular high-stakes use.
Why a polished explanation can still be wrong
Fluent wording is not evidence that the mathematics is valid. OpenAI describes hallucinations as plausible but false statements generated by language models, and notes that they can occur even in response to apparently straightforward questions. Its September 5, 2025 explainer provides that description. For math, the practical implication is to check the setup and reasoning line by line rather than judging by confidence, detail, or a neatly formatted final answer.
Rank #3
How to compare claims about math-capable models
When comparing published results, look for the conditions that make the scores interpretable. A model may perform differently depending on whether it can browse, run code, or use other tools, and whether the test checks exact answers, equivalent expressions, or proof quality. If a publisher does not state a condition, treat it as unspecified rather than assuming it matches another evaluation.
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- Benchmark name, problem type, and difficulty
- Whether problems are text-only or include images
- Model name and version, plus evaluation date
- Tools, compute conditions, number of attempts, and sampling method
- Scoring or grading method, including human review where applicable
- Uncertainty estimates and any relevant risk of test-set contamination
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