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Topic: "competitive-programming"
OpenAI's IMO Gold model also wins IOI Gold
gpt-5 gpt-5-thinking gpt-5-mini gemini-2.5-pro claude opus-4.1 openai google-deepmind anthropic reinforcement-learning benchmarking model-performance prompt-engineering model-behavior competitive-programming user-experience model-naming model-selection hallucination-detection sama scaling01 yanndubs sherylhsu ahmed_el-kishky jerry_tworek noam_brown alex_wei amandaaskell ericmitchellai jon_durbin gdb jerryjliu0
OpenAI announced placing #6 among human coders at the IOI, reflecting rapid progress in competitive coding AI over the past two years. The GPT-5 launch faced significant user backlash over restrictive usage limits and removal of model selection control, leading to a reversal and increased limits to 3000 requests per week for Plus users. Confusion around GPT-5 naming and benchmarking was highlighted, with critiques on methodological issues comparing models like Claude and Gemini. Performance reviews of GPT-5 are mixed, with claims of near-zero hallucinations by OpenAI staff but user reports of confidence in hallucinations and steering difficulties. Benchmarks show GPT-5 mini performing well on document understanding, while the full GPT-5 is seen as expensive and middling. On the Chatbot Arena, Gemini 2.5 Pro holds a 67% winrate against GPT-5 Thinking. Prompting and model behavior remain key discussion points.
o1: OpenAI's new general reasoning models
o1 o1-preview o1-mini gpt-4o llama openai nvidia test-time-reasoning reasoning-tokens token-limit competitive-programming benchmarking scaling-laws ai-chip-competition inference training model-performance jason-wei jim-fan
OpenAI has released the o1 model family, including o1-preview and o1-mini, focusing on test-time reasoning with extended output token limits over 30k tokens. The models show strong performance, ranking in the 89th percentile on competitive programming, excelling in USA Math Olympiad qualifiers, and surpassing PhD-level accuracy on physics, biology, and chemistry benchmarks. Notably, o1-mini performs impressively despite its smaller size compared to gpt-4o. The release highlights new scaling laws for test-time compute that scale loglinearly. Additionally, Nvidia is reportedly losing AI chip market share to startups, with a shift in developer preference from CUDA to llama models for web development, though Nvidia remains dominant in training. This news reflects significant advances in reasoning-focused models and shifts in AI hardware competition.