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Mythos-nano tool-calling is coming, but check out Merlin-Agent! https://huggingface.co/Merlin-Research/Merlin-Agent
Disclaimer: This is not an official release by Anthropic.
Mythos-nano is an independent open model project.
🚨 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.
⚠️ 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.
| 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 | – | – |
| 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) |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3tok = AutoTokenizer.from_pretrained("squ11z1/Mythos-nano")
4model = AutoModelForCausalLM.from_pretrained("squ11z1/Mythos-nano", dtype=torch.bfloat16, device_map="cuda")
5msgs = [{"role": "user", "content": "Find all integer solutions of x^2 - y^2 = 12."}]
6ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
7print(tok.decode(model.generate(ids, max_new_tokens=2048, temperature=0.6)[0], skip_special_tokens=True))mythos-nano-f16.gguf and mythos-nano-Q4_K_M.gguf are provided for llama.cpp / Ollama.