DeepSeek-V4-Pro-Max beats GPT-5.2 and Gemini 3.0-Pro on standard reasoning benchmarks and lands just behind GPT-5.4 and Gemini 3.1-Pro

GPQAD | 94.4 | 90.9 | 92.7 | 92.4 | **95.1** | LCBv6 | 90.3 | 92.1 | 92.4 | 90.4 | **93.2** | SWEPro | 48.4 | 51.2 | _53.4_ | 51.3 | **54.2**

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

The takeaway is not whether Mythos is better or more powerful. It is that public models can already achieve much the same results.

Stronger models hallucinate less, so they can't see the problem in any side of the spectrum: the hallucination side of small models, and the real understanding side of Mythos.

It also surpasses all peer-scale dense models by a wide margin.

Older models were more prone to submitting prematurely, even when test cases weren't passing.

Performance on knowledge-heavy tasks depends strongly on model size and training, while reasoning-oriented models show clear gains on tasks requiring logic, learning, abstraction, and social inference.

we studied emotion-related representations in Claude Sonnet 4.5, a frontier LLM at the time of our investigation.

Compared to existing Ruby desktop frameworks, such as Shoes, Bowline's strengths are its adherence to MVC and use of HTML/JavaScript.