DeepSeek R2 Scores 92.7% AIME on 32B Dense Model — Runs on a Single RTX 4090
DeepSeek R2, released in April 2026 under the MIT license, marks a deliberate architectural reversal. Where R1 was a 671-billion-parameter Mixture-of-Experts model requiring at minimum an 8× H100 cluster, R2 is a 32-billion-parameter dense transformer that fits within the 24 GB VRAM envelope of a single RTX 4090 or A6000.
The benchmark numbers are not incremental. R2 scores 92.7% on AIME 2025 — the competitive mathematics benchmark that has become a standard proxy for frontier-grade reasoning. R1, on independent re-evaluation, scored around 74% on the same benchmark. The gap is 18.7 percentage points on the same task family, from a model that is 21× smaller by parameter count.
Key Numbers
| Metric | DeepSeek R1 (Jan 2025) | DeepSeek R2 (Apr 2026) |
|---|---|---|
| Architecture | 671B MoE (37B active) | 32B dense |
| AIME 2025 | ~74% (independent) | 92.7% (announced) |
| Local hardware floor | 8× H100 cluster | 1× RTX 4090 (24 GB) |
| License | MIT | MIT |
| Token cost vs. Western frontier | — | ~70% cheaper |
Why the Architecture Shift Matters
MoE architectures deliver efficient inference at scale — only a fraction of parameters activate per token — but they impose severe minimum hardware requirements and are difficult to run locally. R2’s shift to a dense 32B design trades raw capacity for accessibility: a model in this weight class runs on hardware that thousands of developers already own, with no multi-node coordination required.
The reasoning capability improvement suggests DeepSeek has found significant gains through training methodology rather than parameter count. The Parcae paper (Prairie et al., 2026) has separately documented that Recursive Depth Transformers can match the output quality of models nearly twice their parameter count — pointing to architecture efficiency research converging with DeepSeek’s own trajectory.
Pricing and Competitive Position
At approximately 70% below comparable Western frontier reasoning API prices, R2 continues the pattern DeepSeek established with V3 and R1: price at a level that pressures the cost floor for the entire category. For developers running AIME-class reasoning workloads, R2 presents the first consumer-hostable option that clears 90% on the benchmark — previously only achievable via cloud APIs with significantly higher per-token costs.
The release lands as the company separately advances DeepSeek V4, a 1-trillion-parameter MoE model targeting multimodal reasoning at the frontier. R2 and V4 appear to serve different markets: R2 optimises for accessibility and cost, V4 for maximum capability.
MIT licensing means R2 weights can be fine-tuned and deployed commercially without restriction — a significant differentiator from most frontier-adjacent reasoning models.