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orangejuicesmith/Mythos-Nano

sourceHugging Facemitupdated 3mo agoView on Hugging Face

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Model Card

Mythos-nano tool-calling is coming, but check out Merlin-Agent!

https://huggingface.co/Merlin-Research/Merlin-Agent

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Disclaimer: This is not an official release by Anthropic. Mythos-nano is an independent open model project.

Mythos-nano

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<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;"> <span style="font-weight: bold;">🚨 </span> This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents. For programming tasks, we recommend using this model on competitive programming problems (e.g., LeetCode-style) - Weibo Lab. </blockquote>

<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;"> <span style="font-weight: bold;">⚠️ </span> Abliterated (uncensored): the refusal direction has been removed, so this model will not decline requests a safety-tuned model normally would. Safety guardrails are reduced — use responsibly and at your own risk; you are solely responsible for outputs and legal compliance. </blockquote>

🏆 Benchmarks

ChatGPT Image Jun 19, 2026 at 12_53_05 PM

Full comparison (mathematics · coding · knowledge · instruction)

Model	Params	AIME25	AIME26	HMMT25	BruMO25	IMO-Ans	LCBv6	OJBench	GPQA-D	IFEval	IFBench
Kimi K2.5	1T	96.1	93.3	95.4	98.3	81.8	85.0	54.7	87.6	93.9	70.0
GLM-5	744B	96.7	95.8	97.9	–	82.5	85.5	55.0	86.0	92.6	76.5
DeepSeek V3.2	671B	93.1	94.2	90.2	96.7	78.3	80.8	48.4	82.4	92.6	60.7
Gemini 3 Pro	N/A	96.0	91.7	97.5	98.3	83.1	87.4	58.8	91.9	–	70.4
Claude Opus 4.5	N/A	92.8	95.1	92.9	–	78.5	84.8	–	87.0	–	58.0
GPT-5 (high)	N/A	94.6	–	88.3	91.7	76.0	84.5	–	85.7	–	73.1
Mythos-nano	3B	91.4	94.3	89.3	93.8	76.4	80.2	38.6	70.2	93.4	74.5
Mythos-nano + CLR	3B	96.7	97.1	95.4	99.2	80.6	–	–	72.9	–	–

LeetCode contests (Python, pass-rate)

Model	Aggregate
GPT-5.3-Codex	100.0% (128/128)
Gemini 3.1 Pro	99.2% (127/128)
Gemini 3 Flash	96.9% (124/128)
Mythos-nano	96.1% (123/128)
GPT-5.2	95.3% (122/128)
Qwen3-Max	91.4% (117/128)
Kimi K2.5	90.6% (116/128)
Claude Opus 4.6	86.7% (111/128)

A 3B model placing within ~4 points of trillion-parameter systems on competition math and live code — the core thesis: with verifiable feedback, small models reach frontier reasoning.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("squ11z1/Mythos-nano")
model = AutoModelForCausalLM.from_pretrained("squ11z1/Mythos-nano", dtype=torch.bfloat16, device_map="cuda")
msgs = [{"role": "user", "content": "Find all integer solutions of x^2 - y^2 = 12."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
print(tok.decode(model.generate(ids, max_new_tokens=2048, temperature=0.6)[0], skip_special_tokens=True))

Recommended sampling: temperature 0.6–1.0, up to 40960 output tokens for hard problems.

GGUF

mythos-nano-f16.gguf and mythos-nano-Q4_K_M.gguf are provided for llama.cpp / Ollama.

License

MIT.