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Model: "grok-4.5"
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kimi-k3 grok-4.5 chatgpt codex moonshot baseten nvidia red-hat-ai perplexity-ai togethercompute cursor_ai mixture-of-experts model-architecture attention-mechanisms reinforcement-learning infrastructure model-deployment agentic-ai mobile-ai multimodality model-distillation gpu-optimization system-design zhihufrontier rasbt bhavinjawade danizeres amansanger
Moonshot released the Kimi K3, a 2.8T-parameter MoE model with 104B active parameters/token, featuring innovations like Kimi Delta Attention (KDA), Gated MLA, and LatentMoE. The release includes infrastructure components such as MoonEP, FlashKDA, and AgentEnv, emphasizing system-level design. Despite open weights, running K3 requires significant hardware investment (minimum 8Ć MI355X GPUs, production at 64+ GPUs) with costs reaching six figures USD or tens of millions RMB. Hosted access is available via Perplexity, Baseten, and Together. Additionally, agent-based workflows are advancing with mobile orchestration, highlighted by ChatGPT Voice + Codex, Cursor's Start in India powered by Grok 4.5, and Perplexity's Personal Computer local agent with multi-model comparison via Model Council. "If you ever want to feel dumb just read the Kimi K3 technical report" captures community reaction to the dense technical details.
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gpt-5.6-sol grok-4.5 terra-max fable-5-max opus-4.8 100b-reasoning-model prime-intellect vllm langchain threepointone factory cognition arena artificial-analysis parlance-labs agentic-reinforcement-learning rollout-traces message-dags long-horizon-reinforcement-learning multimodality harness-design cost-per-task coding-agents benchmarks model-efficiency real-world-evaluation task-specialization johannes_hage willccbb mikasenghaas xeophon omarsar0 skirano imjaredz
Prime Intellect released verifiers v1, a redesigned environment stack for agentic reinforcement learning and evaluations, improving efficiency by storing rollout traces as message DAGs to reduce complexity from O(nĀ²) to O(n). This enables practical long-horizon multimodal rollouts, demonstrated with a 100B reasoning model running 40-turn SWE agent tasks on 6 H200 nodes in under 2 days. The ecosystem support includes vLLM integration to avoid tokenization drift. Discussions highlight that harnesses are becoming critical as the product surface for coding agents, with task-specialized harnesses favored over generic wrappers. Benchmarks are shifting focus from token price to cost per task, with models like Terra Max, Fable 5 Max, and Opus 4.8 compared on efficiency and cost. Real-world agent benchmarks show GPT-5.6 Sol ranking #2 and Grok-4.5 jumping to #13 on Arena's leaderboard, emphasizing cost per task as a key metric for long-horizon knowledge work.
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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.