FINAL-Bench / Darwin-397B-ZTC

Darwin-397B-ZTC

397B Mixture-of-Experts built on Qwen 3.5 · FP8 · GPQA Diamond 93.43 % · ZTC on board

reasoning · MoE · FP8 · 262K long context · Korean + English · hallucination detection · tool calling

Half the footprint, GPQA Diamond 93.43 %. And this model stops itself before it acts on an answer it is about to get wrong.

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🧬 The Darwin Family

Darwin is VIDRAFT 's measurement-driven reasoning model family — roughly 20 official models , 400+ community derivatives , and a standing place among the top open models on GPQA.

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🧬 Darwin — transplanting the experts that work

A large MoE model is made of hundreds of experts . Darwin V9 selects the experts that perform best across several high-performing models, transplants them onto a base backbone, and fuses them with trust-weighted evolutionary merging.

Nothing is trained from scratch — proven capability is grafted on. That is why the same method holds across every model size.

Lineage

• Darwin V9 — evolutionary FFN/expert transplant and trust-weighted merging onto large MoE backbones
• FINAL Bench — VIDRAFT's evaluation framework
• Four-layer Pre-AGI roadmap — Darwin → AETHER → PROMETHEUS → HEPHAESTUS

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🏛️ ZTC — it knows before it answers

Until now there were two ways to find out whether a model is about to be wrong. Both of them only work after the answer already exists .

ZTC is a third path. It reads the model's own internal state once, before generation begins.

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📊 Measured — on this model

① It judges its own answers (PubMedQA, 539 items, 146 incorrect)

Permutation null control: z = 13.31 — shuffle the labels and the signal disappears.

② It judges other models' answers (Korean KMMLU, 400 items — law, math, biology, history)

Same 400 items, same conditions: +0.138 over the open-source judge model.

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📦 The probe ships with this model

z = np.load("ztc/ztc_probe_darwin397b.npz")
s = ((h - z["mu"]) / z["sd"]) @ z["w"]        # h = last-token hidden state, 4096-dim
p = 1 / (1 + np.exp(-(z["cal_A"] * (s - z["s_mean"]) / z["s_std"] + z["cal_B"])))

One matrix product. No second model, no extra tokens, no network call. The probe is specific to this model's hidden space (4096-dim) and does not transfer to others.

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🤖 Why this is decisive for agents — after-the-fact report vs. pre-action stop

In an agent loop the expensive thing is not tokens. It is actions . Files get edited, APIs get called, payments go through, mail leaves the building.

External judge :  [generate] → [tool runs] → [cost, time, side effects] → [judge] → "that was wrong"
ZTC            :  [read state, 0.52 ms] → stop here if risky → the action never happens

In front of an irreversible action, an after-the-fact verdict is an incident report.

Patterns

Gate deployment, measured

At effectively zero cost it can stay on for every request.

Use cases — hallucination detection · uncertainty quantification · confidence calibration · selective prediction · routing risky queries upstream · pre-action gating for agents

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🏆 GPQA Diamond 93.43 %

GPQA Diamond, all 198 items · greedy · single sample · no test-time engine

Comparison figures: Qwen3.5-397B-A17B official model card.

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⚙️ Specifications

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🚀 Quickstart

Serving with vLLM (4 × H100 80GB)

vllm serve FINAL-Bench/Darwin-397B-ZTC \
  --served-model-name darwin-397b \
  --tensor-parallel-size 1 --pipeline-parallel-size 4 \
  --gpu-memory-utilization 0.92 --max-model-len 262144 \
  --cpu-offload-gb 20 --enforce-eager --trust-remote-code \
  --reasoning-parser qwen3 --enable-auto-tool-choice \
  --port 8000

SGLang

python -m sglang.launch_server --model-path FINAL-Bench/Darwin-397B-ZTC \
  --port 8000 --tp-size 8 --context-length 262144

Chat Completions (OpenAI-compatible)

from openai import OpenAI
c = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

r = c.chat.completions.create(
    model="darwin-397b",
    messages=[{"role": "user", "content": "Why is the Riemann hypothesis hard?"}],
    temperature=0.0, max_tokens=8192,
)
m = r.choices[0].message
print(m.reasoning_content)   # thinking trace
print(m.content)             # final answer

🛠️ Tool calling

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather for a city",
        "parameters": {"type": "object",
                       "properties": {"city": {"type": "string"}},
                       "required": ["city"]},
    },
}]

r = c.chat.completions.create(
    model="darwin-397b", tools=tools,
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
)
print(r.choices[0].message.tool_calls)

🤖 Agents and coding CLIs

The endpoint is OpenAI-compatible, so existing tooling connects unchanged.

opencode — ~/.config/opencode/opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "darwin": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Darwin (local)",
      "options": { "baseURL": "http://localhost:8000/v1", "apiKey": "EMPTY" },
      "models": { "darwin-397b": { "name": "Darwin-397B-ZTC" } }
    }
  }
}

Any OpenAI-compatible client (Cline, Continue, Aider, …)

export OPENAI_BASE_URL=http://localhost:8000/v1
export OPENAI_API_KEY=EMPTY
export OPENAI_MODEL=darwin-397b

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🎯 Intended use

• Graduate-level STEM reasoning (GPQA, science qualifying exams)
• Mathematics and long multi-step chains of thought
• Code generation and debugging
• 🤖 Agent workflows — ZTC blocks irreversible tool calls before they run
• Bilingual Korean + English reasoning (Chinese and Japanese supported)
• Work where a wrong answer is expensive — ZTC filters risky answers before they ship

🔗 Links

• 🌐 vidraft.net — VIDRAFT
• 🤗 FINAL-Bench — all models
• 📱 POCKET — on-device line that runs on phones and GPU-less PCs

📚 Citation

@misc{darwin397b_ztc_2026,
  title = {Darwin-397B-ZTC: FP8 Mixture-of-Experts with Zero-Token Confidence},
  year  = {2026},
  url   = {https://vidraft.net},
  note  = {Base: Qwen/Qwen3.5-397B-A17B}
}