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Model: "opus-4.7"
not much happened today
grok-4.5 opus-4.7 opus-4.8 gpt-5.6 xai cursor scaling01 coding agents model-scaling context-window model-pricing token-efficiency model-training model-performance elonmusk
xAI publicly launched Grok 4.5, a new coding-and-agents-focused frontier model emphasizing capability-per-dollar rather than benchmark supremacy. Elon Musk described it as "Opus-class" but faster, more token-efficient, and lower cost, with a 1.5 trillion parameter size, making it 3x larger than Grok 4.3. The model is priced at $2 per 1M input tokens and $6 per 1M output tokens, with discounts for cache hits and a context window expected to return to 1 million tokens soon. Cursor partnered in training Grok 4.5, highlighting it as their most powerful model yet and expanding beyond software engineering. Early ecosystem support includes Grok Build/API, Hermes Agent, Portal, and OpenRouter.
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claude-mythos opus-4.8 opus-4.7 gpt-5.5 gemini-3.1-pro gemini-3.5-flash claude-opus-4.7 anthropic sakana-ai meta-ai-fair princeton recursive-self-improvement benchmarking agent-evaluation long-horizon-tasks reliability reinforcement-learning sample-efficiency economically-meaningful-tasks agent-coherence anti-reward-hacking tooling rl-environments kimmonismus lechmazur teortaxestex hardmaru andrew_n_carr steverab pauliusztin_
Anthropic's Mythos/Opus cycle sparked mixed reactions with praise for Claude Mythos's one-shot workflows and concerns over Opus 4.8 benchmark regressions. Opus 4.7 showed strong chemistry task performance, "making Claude a chemist." Sakana AI launched an RSI Lab focusing on recursive self-improvement under compute constraints, marking RSI as a formal research program. New benchmarks like Agents' Last Exam (ALE) and SWE-Marathon test agents on long-horizon, economically meaningful tasks, revealing low pass rates and coherence challenges. Princeton's ICML 2026 paper found models like GPT 5.5, Gemini 3.1 Pro / 3.5 Flash, and Claude Opus 4.7 still lack meaningful reliability improvements. Tooling trends favor RL-environment-style frameworks for agent evaluation, exemplified by Meta's OpenEnv.
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gpt-5.5 gpt-5.4 opus-4.7 mimo-v2.5-pro mimo-v2.5 kimi-k2.6 codex copilot openai microsoft google amazon github xiaomi openai-devs vllm_project kimi-moonshot model-distribution cloud-computing benchmarking usage-based-billing model-orchestration open-source large-context-models agent-scaling coding model-training fp8 attention-mechanisms multi-agent-systems sama scaling01 kimmonismus ajassy simonw htihle arena gdb hangsiin eliebakouch _luofuli teortaxestex
OpenAI loosens its Azure exclusivity, allowing distribution across Google TPU, AWS Trainium, and Bedrock with commitments through 2032 and revenue share through 2030. GPT-5.5 shows improved benchmarks but is not uniformly dominant, ranking variably across coding, document, math, and vision tasks. GitHub's Copilot shifts to usage-based billing starting June 1, reflecting increased runtime costs. OpenAI open-sourced Symphony, an orchestration layer for issue tracking and Codex agents. Xiaomi released MiMo-V2.5 and MiMo-V2.5-Pro, large context models with up to 1M-token context and trillions of tokens trained, emphasizing complex agent and omni-modal capabilities. Kimi K2.6 leads OpenRouter's leaderboard, noted for coding and long-horizon agent capabilities with large-scale sub-agent coordination.