# Google DeepMind Achieves 98th Percentile on Mathematical Reasoning Benchmark

> Google DeepMind reports that its latest mathematical reasoning system now scores in the top 1 percent on International Mathematical Olympiad problems, the proof-heavy competition questions that have long been a benchmark for genuine reasoning rather than pattern matching. A 98th percentile result puts the system in the company of the strongest human competitors on this particular test. Olympiad problems are a useful yardstick because they resist memorization. Each one demands a chain of logical steps and a construction the solver has to invent, which is very different from recalling a fact or completing a familiar template. Progress here has been one of the clearer signals that models are getting better at multi-step reasoning, not just retrieval. The caveat, as always, is scope. Doing well on curated competition mathematics is not the same as open-ended research mathematics, where problems are unbounded and there is no answer key. Still, as part of DeepMind's July research output it adds to a run of results suggesting that structured, verifiable reasoning is an area where these systems keep climbing, and where the ceiling is not yet in sight.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Google DeepMind · Published Saturday, August 1, 2026_

## Wortins' read

Google DeepMind reports that its latest mathematical reasoning system now scores in the top 1 percent on International Mathematical Olympiad problems, the proof-heavy competition questions that have long been a benchmark for genuine reasoning rather than pattern matching. A 98th percentile result puts the system in the company of the strongest human competitors on this particular test. Olympiad problems are a useful yardstick because they resist memorization. Each one demands a chain of logical steps and a construction the solver has to invent, which is very different from recalling a fact or completing a familiar template. Progress here has been one of the clearer signals that models are getting better at multi-step reasoning, not just retrieval. The caveat, as always, is scope. Doing well on curated competition mathematics is not the same as open-ended research mathematics, where problems are unbounded and there is no answer key. Still, as part of DeepMind's July research output it adds to a run of results suggesting that structured, verifiable reasoning is an area where these systems keep climbing, and where the ceiling is not yet in sight.

## Source

[Read the full story at Google DeepMind](https://deepmind.google/research/publications/)

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