DeepSeek Updates R1 Model, Narrows Gap With OpenAI, Google
The new R1-0528 version lifts math performance from 70% to 87.5% on the AIME 2025 benchmark and cuts hallucinations nearly in half, bringing DeepSeek's open model closer to o3 and Gemini 2.5 Pro.
DeepSeek has released an update to its reasoning model R1, dubbed R1-0528, that — based on available data — narrows the distance with the most advanced closed reasoning models on the market.
The performance jump is concrete and measurable: on AIME 2025, a benchmark that tests the ability to solve competition-level math problems, the model's accuracy climbs from 70% to 87.5%. The new version also cuts hallucinations — answers the model presents as true when they aren't — by roughly 45-50% compared to the previous version.
What closing the gap with o3 and Gemini 2.5 Pro means
The comparison isn't cosmetic. OpenAI's o3 and Google's Gemini 2.5 Pro are, alongside other reasoning models, the current benchmark for tasks that require step-by-step thinking before answering: advanced math, complex coding, or multi-step logic problems. An open-weight model — meaning one whose parameter file can be downloaded, run locally and freely modified — closing in on that level changes the calculus of who can afford to build on frontier AI.
Until now, accessing a reasoner of that caliber meant paying for API access from a closed company and accepting its terms of use, its rate limits and its opacity about how the model was trained. With open weights, any company, university lab or independent developer can download R1-0528, fine-tune it for their specific use case and run it on their own infrastructure, without relying on an outside provider or sending data to third-party servers.
The open-source threat to closed models
It's common to hear the argument in the industry that the capability gap between closed and open-weight models would remain wide: the latter could be useful, but not competitive at the frontier. The R1-0528 numbers complicate that argument: if an open model cuts hallucinations in half and gains nearly 18 percentage points on a demanding math benchmark in a single update, the distance to proprietary models appears to be closing fast.
This doesn't settle the debate: closed models still offer guarantees around support, integration and, in theory, safety controls that a downloadable model doesn't come with out of the box. But the economic argument for companies with tight budgets — or for those looking to avoid dependence on external infrastructure — is tilting increasingly toward open models.
What changes for today's AI users
For a developer or company already working with reasoning models, DeepSeek's update expands the real options available with no per-token licensing cost. For the ecosystem at large, it reinforces a trend that has been building for months: leadership in open weights is no longer the exclusive domain of second-tier models — it's starting to reach the top tier of performance on complex reasoning tasks.
It remains to be seen how OpenAI and Google respond to this narrowing gap, and whether they can hold onto their edge by doubling down on capabilities a downloadable model can't easily replicate, such as deep integration with proprietary tools or large-scale fine-tuning on proprietary data.
