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