Developers are road-testing OpenAI’s GPT-5.6 Sol and comparing it with other models based on real tasks like content generation, math problem solving, and coding workflows. Shouqiao Wang says he solved six open Erdős problems in five days using GPT-5.6 Sol with the “ultra” reasoning effort setting. Users report stronger capability in long, rigorous searches and higher-level architecture help, but also note it can overengineer and may still be less suitable for some smaller frontend tasks.
DeepSeek moved its V4-Flash model out of preview and published benchmark claims that it beats V4-Pro on coding and agentic tasks despite a much lower price. V4-Flash is listed at $0.14 per 1M input tokens and $0.28 per 1M output tokens, about one-third of V4-Pro’s $0.435 and $0.87. In side-by-side OpenCode tests on a Rich-codebase suite, Flash and Pro tied on bug-fix and feature tasks, but Flash performed better on a 5,000-row table optimization while Pro stayed cheaper on token volume for a slightly smaller speedup.
ChinaTalk launched a rolling evals-and-essay project contest focused on testing AI models for policy and national-security decision support. The contest offers $75k in total prize money. This will fund and organize new benchmarking work, with submissions that examine how models handle strategic and high-stakes questions beyond coding tasks.
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