Editorial illustration for OpenMythos: 770M‑parameter PyTorch clone matches 1.3B Claude model, reasoning
Open-Source Claude Clone Matches 1.3B Model Performance
OpenMythos: 770M‑parameter PyTorch clone matches 1.3B Claude model, reasoning
Bigger models have been the default answer to better reasoning. More parameters, more layers, more data, the brute-force path. OpenMythos takes a different route: looped depth.
With only 770 million parameters, it matches a 1.3-billion parameter standard transformer, not by scaling up but by reusing a single hidden state across multiple inference-time iterations. The same architecture that fails on 10-hop reasoning after training only on 5 hops now generalizes naturally, harder questions simply trigger more loops. Stability was the old barrier.
Residual explosion and drifting past the solution have historically made looped models brittle. OpenMythos borrows a Linear Time-Invariant constraint to guarantee spectral radius below 1, keeps recurrence bounded with Adaptive Computation Time halting, and introduces Depth-Wise LoRA adapters so each loop step behaves slightly differently without a parameter explosion. The result is a lean, stable transformer that reasons deeper on demand.
And it’s built in open-source PyTorch.
A new open-source project called OpenMythos, released on GitHub by Kye Gomez, attempts something ambitious: a first-principles theoretical reconstruction of what the Claude Mythos architecture might actually be, built entirely in PyTorch and grounded in peer-reviewed research.
OpenMythos flips the narrative on its head. It proves that bigger isn’t always better , smarter is. By stitching together recurrence, stability guarantees, and adaptive compute, this 770M-parameter model matches a 1.3B transformer in raw reasoning power.
That’s not a tweak. It’s a fundamental shift in how we think about scaling. The same inference-time loops that extend reasoning depth also let the model decide when to stop thinking.
Hard problems devour compute; easy ones breeze through. And it all stays stable, iteration after iteration, because the architecture enforces its own discipline. The result is a leaner, more honest form of intelligence , one that earns its performance through design, not just mass.
OpenMythos isn’t just a clone. It’s a road map.
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Common Questions Answered
How does OpenMythos achieve comparable performance to the 1.3B Claude model with only 770 million parameters?
OpenMythos leverages a novel architectural approach that focuses on multi-step reasoning capabilities without increasing model size. The model uses continuous latent thoughts that can encode multiple alternative reasoning paths simultaneously, allowing it to handle complex inference tasks more efficiently than traditional transformers.
What makes the Recurrent-Depth Transformer unique in handling reasoning chains?
The Recurrent-Depth Transformer can naturally extend reasoning depth beyond its training constraints by running additional inference-time loops. Unlike standard transformers that fail when tested on reasoning chains longer than their training depth, this approach allows for more flexible and dynamic reasoning across multiple inference steps.
Why is the OpenMythos project significant for machine learning research?
OpenMythos democratizes access to a sophisticated transformer architecture previously kept private by Anthropic by providing an open-source PyTorch reconstruction. The project demonstrates that high-performance language models can be developed using first-principles theory and peer-reviewed research, potentially accelerating collaborative AI development.
Further Reading
- OpenMythos Recasts Claude Mythos as Looped MoE Transformer — Awesome Agents
- Papers with Code - Latest NLP Research — Papers with Code
- Hugging Face Daily Papers — Hugging Face
- ArXiv CS.CL (Computation and Language) — ArXiv
