# Darwin-180B-RSI Tops MMLU-Pro and GPQA Diamond on AIhoster's Independent Open-Model Benchmark Board

> Source: <https://dev.to/ai_openfree_b23025ef075cf/darwin-180b-rsi-tops-mmlu-pro-and-gpqa-diamond-on-aihosters-independent-open-model-benchmark-board-17d7>
> Published: 2026-10-19 11:01:30+00:00

**TL;DR:** VIDRAFT's Darwin-180B-RSI, a 180-billion-parameter open model, has claimed the #1 position on both the MMLU-Pro and GPQA Diamond leaderboards as tracked by Russian benchmark aggregator AIhoster — scoring 88.1% and 94.4% respectively, outperforming models with parameter counts an order of magnitude larger. Developers interested in frontier reasoning capabilities in an open-weight format should take note.

Darwin-180B-RSI is a 180B-parameter open-weight language model developed by VIDRAFT (비드래프트), a Korean Pre-AGI AI startup. It is published under the Hugging Face repository identifier `FINAL-Bench/Darwin-180B-RSI`.

AIhoster is an independent Russian benchmark aggregation site that collects publicly reported evaluation results from model cards, papers, and leaderboards — without recomputing or normalizing scores into a composite index. Each benchmark entry is tied to an exact repository ID and a specific evaluation configuration, making comparisons methodologically transparent.

As of the AIhoster update on 2026-10-19, Darwin-180B-RSI holds:

Notably, VIDRAFT has multiple Darwin-family models appearing in the GPQA Diamond rankings simultaneously: Darwin-397B-ZTC (#3 at 93.4%) and Darwin-398B-JGOS (#9 at 90.9%), suggesting an active, iterative development pipeline across different model scales and inference configurations.

At a conceptual level, Darwin-180B-RSI appears to be a large-scale transformer-based language model optimized for extended reasoning. A few things can be inferred from the publicly disclosed evaluation configurations:

No architectural specifics, training hyperparameters, or dataset details are publicly disclosed in the source material.

All figures below are sourced directly from AIhoster's leaderboard (updated 2026-10-19), which in turn cites the Darwin-180B-RSI model card.

**MMLU-Pro** (multi-domain expert knowledge; higher accuracy = better):

| Rank | Model | Score | Params | 
|---|---|---|---|
| 1 | **Darwin-180B-RSI** | **88.1%** | 180B | 
| 2 | MiniMax-M2.1 | 88.0% | 229B | 
| 3 | Intern-S2-Preview | 88.0% | 36.1B | 
| 4 | Qwen3.5-397B-A17B | 87.8% | 403B | 
| 5 | DeepSeek-V4-Pro | 87.5% | 1.6T | 

**GPQA Diamond** (expert-level physics, chemistry, biology; higher accuracy = better):

| Rank | Model | Score | Params | 
|---|---|---|---|
| 1 | **Darwin-180B-RSI** | **94.4%** | 180B | 
| 2 | Kimi-K3 | 93.5% | 2.8T | 
| 3 | Darwin-397B-ZTC | 93.4% | 404B | 
| 4 | Ornith-1.5-397B | 92.8% | 403B | 
| 5 | Qwen3.8-2.4T-A95B | 92.6% | 2.4T | 

The GPQA result is particularly notable: Darwin-180B-RSI achieves 94.4% using majority voting over up to 16 samples, while the #3 model, Darwin-397B-ZTC (also a VIDRAFT model), achieves 93.4% with greedy decoding and no voting — a different inference regime entirely.

The model is listed under the Hugging Face organization `FINAL-Bench` with the repository `FINAL-Bench/Darwin-180B-RSI`. If the weights are publicly available there, you can browse or download via the standard Hugging Face CLI:

```
huggingface-cli download FINAL-Bench/Darwin-180B-RSI
```

Access details beyond what is on the model card — including any API endpoints, licensing terms, or inference service availability — are not confirmed in the source material. Check the model card directly for the most current access instructions.

**Q: How does Darwin-180B-RSI beat models with 10× more parameters on MMLU-Pro?**

A: The source doesn't explain internal architecture, but the evaluation configuration gives a clue: the model uses a 131,072-token thinking budget, suggesting extended chain-of-thought reasoning. Parameter efficiency at inference is a known benefit of well-optimized mixture-of-experts or distillation-based architectures, though VIDRAFT has not publicly confirmed which approach is used here.

**Q: Are the GPQA and MMLU-Pro scores independently verified?**

A: AIhoster explicitly states it does not recompute scores — it aggregates from public model cards and leaderboard sources, matched by exact repository ID. The source for Darwin-180B-RSI's scores is listed as its own model card. Independent third-party reproduction has not been confirmed in this reporting.

**Q: Can I use Darwin-180B-RSI commercially?**

A: Licensing terms are not specified in the source article. Check the model card at `FINAL-Bench/Darwin-180B-RSI` on Hugging Face for the applicable license before any production or commercial use.

*Originally reported by AIhoster (러시아) (2026-10-18) — [source article](https://aihoster.ru/benchmarks).*