📱 Run it on your phone or a GPU-less PC → POCKET · 🚀 Try it live (CPU chat)

POCKET is quantized straight from this model. VIDRAFT's on-device family: a 35B model that runs on iPhone and on CPU with no GPU — stock llama.cpp, no fork.

Darwin-36B-Opus: Darwin V7 Evolutionary Merge on Qwen3.6-35B-A3B — 88.4% on GPQA Diamond

Qwen3.6-35B-A3B MoE | 36B total / 3B active | Thinking Mode | 262K Context | Multilingual | BF16 | Apache 2.0 Darwin V7 evolutionary merge: base parent × reasoning-distilled parent → 88.4% on GPQA Diamond

---

Abstract

Darwin-36B-Opus is a 36-billion-parameter mixture-of-experts (MoE) language model produced by the Darwin V7 evolutionary breeding engine from two publicly available parents:

• Base parent: Qwen/Qwen3.6-35B-A3B — the foundation MoE with hybrid attention and 256 routed experts.
• Reasoning parent: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled — a reasoning-distilled variant of the same base.

Darwin V7 recombines these two parents into a single descendant that preserves the reasoning parent's distilled chain-of-thought behavior while retaining the structural fidelity of the base parent's expert topology. The breeding process is fully automated and produces a deployable bfloat16 checkpoint in under an hour on a single GPU.

On the GPQA Diamond benchmark — 198 graduate-level questions in physics, chemistry, and biology — Darwin-36B-Opus achieves 88.4%, establishing it as the highest-performing model in the Darwin family and extending the series' record of producing state-of-the-art open models through evolution rather than retraining.

---

GPQA Diamond Leaderboard (April 23, 2026)

A 36B-parameter MoE model (3B active), tying the 397B dense-equivalent Qwen3.5-397B-A17B and surpassing flagship dense and sparse systems an order of magnitude larger.

---

What Is Darwin?

Darwin is the evolutionary model breeding engine developed by FINAL-Bench / VIDRAFT_LAB. Rather than allocating further compute to gradient optimization, Darwin treats trained checkpoints as a genetic pool and discovers high-performing descendants through principled recombination of their weight tensors.

Each Darwin generation (v1 through v7+) refines the breeding procedure. Darwin V7 is the current generation and the one used to produce this model. Specific algorithmic details of V7 are proprietary to FINAL-Bench; at a high level, the engine performs:

1. Per-tensor compatibility analysis of the two parents to identify which components transfer cleanly and which require weighted recombination.
2. Automated recombination guided by that analysis, producing a single coherent descendant.
3. Verification via a multi-phase scientific benchmark before release.

All Darwin models are released under Apache 2.0 and inherit fully from the parents' open-source licenses.

---

Parent Models

🔵 Base parent — Qwen/Qwen3.6-35B-A3B

• Model type: Qwen3.6 MoE, 35B total / ~3B active parameters
• Layers: 40, Hidden size: 2048
• Attention: hybrid 75% Gated DeltaNet + 25% Gated Attention (alternating)
• Experts: 256 routed (top-8) + 1 shared per layer
• Native scores: MMLU-Pro 85.2%, GPQA 86.0%, AIME26 92.7%
• Role: Structural backbone and MoE topology donor.

🔴 Reasoning parent — hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled

• Method: LoRA SFT on the base over 14,233 reasoning chain-of-thought samples
• Training regime: qwen3-thinking template, response-only masking
• Native score: MMLU-Pro (70 limit-5) 75.71%, +32.85 percentage points over the un-distilled base baseline
• Role: Reasoning signal donor — the source whose <think> trajectories Darwin preserves.

---

Evolution Process (High Level)

Darwin V7 produces the descendant through a deterministic recombination that does not require gradient optimization on the final assembly. The engine analyzes each tensor in both parents, classifies it by architectural role, and assigns a recombination weight appropriate to that role — biasing toward the reasoning parent for components that carry reasoning behavior, while preserving the base parent's structural contributions where they dominate.

Total breeding time on a single B200 GPU: under 10 minutes.

---

GPQA Diamond Evaluation

Methodology

We employed a two-pass adaptive evaluation protocol (identical across all Darwin Opus models to preserve cross-model comparability):

Pass 1 — Greedy Baseline

• All 198 GPQA Diamond questions, deterministic decoding (do_sample=False)
• Maximum 5,120 new tokens per question (allows full <think> trajectories)
• Standard multiple-choice prompt format

Pass 2 — Stochastic Retry with Tiebreaker

• Questions incorrectly answered in Pass 1 are re-evaluated with majority-of-8 stochastic generations (temperature=0.7, max_tokens=5120)
• Where the vote margin is inconclusive (3:3, 3:4, or 4:4), an additional 16-vote combined tiebreaker round (temperature=0.5) resolves the answer

Evaluation was performed in parallel across 8 × NVIDIA B200 GPUs, each running an independent full copy of the model on a disjoint subset of the benchmark (round-robin question assignment).

Aggregate Results

The Pass-2 gain of +30 questions (+15.2 pp) demonstrates that the reasoning parent's inherited <think> reasoning yields substantially more correct answers under stochastic decoding than under greedy, confirming that the evolutionary merge preserved reasoning depth.

Results by Shard

Notably, GPU3 achieved a perfect 25/25 score on its 25-question partition — every Pass-1 error on that shard was successfully recovered through the stochastic retry cascade.

---

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-36B-Opus", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "FINAL-Bench/Darwin-36B-Opus",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "Derive the equation for relativistic kinetic energy."}
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=5120, temperature=0.6, do_sample=True)
print(tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Answer Extraction for Evaluations

This is a thinking model — responses always begin with a <think> reasoning trace. For benchmarks, extract the final answer after </think>:

response = tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
idx = response.rfind("</think>")
answer_part = response[idx + len("</think>"):].strip() if idx >= 0 else response

Recommended Settings

• Temperature: 0.6–0.7 for reasoning / majority voting; 0.0 for greedy deterministic
• max_new_tokens: ≥5120 to accommodate full <think> trajectories
• Chat template: <|im_start|>assistant\n<think>\n auto-inserted by apply_chat_template(add_generation_prompt=True)

---

Model Specifications

---

VRAM Requirements

---

Darwin Model Family

---

Key Findings

1. Evolutionary merging continues to scale. Across three successive parameter tiers (27B → 31B → 36B), each new Darwin Opus model surpasses the prior one's GPQA Diamond score while maintaining the same zero-training methodology.

2. Hybrid-attention MoE preserves reasoning under recombination. The base parent's 75% Gated-DeltaNet + 25% Gated-Attention architecture, inherited intact, demonstrates robustness to tensor-level recombination — a notable result given that MoE expert routing is sensitive to weight perturbation.

3. Stochastic retry closes the greedy gap. The +15.2 percentage-point lift from Pass 1 (73.2%) to Pass 2 (88.4%) suggests that the reasoning parent's distilled reasoning is consistently present but occasionally greedy-subdominant — a pattern characteristic of well-distilled chain-of-thought models.

---

References

• Idavidrein et al., GPQA: A Graduate-Level Google-Proof Q&A Benchmark, 2024. dataset
• Qwen Team, Qwen3.6 Technical Report, 2026.

---

Built By

FINAL-Bench / VIDRAFT_LAB — Darwin V7 evolutionary breeding engine.

• Base parent weights by the Qwen Team.
• Reasoning parent by @hesamation (reasoning-distilled).

---

Learn more

• How model merging works, and how Darwin reaches top scores without training: Can you make an AI model smarter without training?

Citation

@misc{darwin-36b-opus,
  title   = {Darwin-36B-Opus: Darwin V7 Evolutionary Merge on Qwen3.6-35B-A3B},
  author  = {FINAL-Bench and VIDRAFT_LAB},
  year    = {2026},
  url     = {https://huggingface.co/FINAL-Bench/Darwin-36B-Opus},
  note    = {Qwen3.6-35B-A3B base x reasoning-distilled variant, Darwin V7 engine, 88.4% GPQA Diamond}
}

This model is introduced in Darwin Family.