DeepSeek R1-0528: AIME 2025 Jumps From 70% to 87.5% in Open-Source Reasoning Upgrade
DeepSeek pushed a checkpoint update to its R1 reasoning model on May 28, date-stamped R1-0528. The headline number: AIME 2025 improved from 70% to 87.5% — a 17.5-point gain on the mathematical reasoning benchmark that has become the standard test for reasoning model progress.
The update is available on Hugging Face and via API at $0.50/M input, $2.15/M output. 671B total parameters, 37B active per inference pass (MoE architecture). Context: 164K tokens. Open-source.
What Changed
The model card identifies two mechanisms behind the improvement:
- More compute at post-training — the update spends significantly more RL compute after pretraining, sharpening the model’s internal reward optimization without changing the base weights architecture
- Algorithmic optimization — targeted improvements to how R1 generates and verifies intermediate reasoning steps, aimed at math, coding, and formal logic specifically
A secondary improvement: instruction following is more reliable, and the verbose chain-of-thought traces that made early R1 awkward to deploy in production have been reduced. The model spends more reasoning budget reaching a better answer rather than displaying intermediate work at length.
The Trade-Off
The accuracy gain comes at a token cost. R1-0528 uses more tokens per question on average — a direct consequence of spending more reasoning budget to get from premise to conclusion. For high-stakes tasks (formal verification, competitive math, structured code generation) that trade-off is generally worth it. For high-volume applications, V4 Flash at $0.14/M output is still the cost-optimal path.
Competitive Position
At 87.5% AIME 2025, R1-0528 pulls level with GPT-5.4 on this specific benchmark and ahead of the original R1 by a significant margin. The comparison that matters: GPT-5.4 is $3/M input, $15/M output. R1-0528 is $0.50 input, $2.15 output — a 6x blended cost advantage.
For developers building reasoning-heavy pipelines who need o1-class math performance at a fraction of the cost, this update changes the routing calculus. The open weights on Hugging Face also mean self-hosted deployment on H100 hardware is viable for organizations with inference infrastructure.
Benchmark Context
| Model | AIME 2025 | Cost (output/M) |
|---|---|---|
| R1-0528 | 87.5% | $2.15 |
| R1 (original) | 70.0% | $2.15 |
| GPT-5.4 | ~87% | $15.00 |
| DeepSeek V4 Pro | — | $0.87 |
The update does not change R1’s position on SWE-bench or agentic benchmarks — those remain V4 Pro’s territory. R1-0528 is a reasoning model improvement, not a general-purpose model refresh.
DeepSeek has not announced a next major generation (R2). R1-0528 is iterative maintenance on the existing checkpoint — a sign the lab is managing a live model rather than staging a launch campaign.