We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open: An MIT open-source license — no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. This capability is reflected in GLM-5.2's performance on three long-horizon coding benchmarks. FrontierSWE measures whether an agent can complete open-ended technical projects at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. On this benchmark, GLM-5.2 trails Opus 4.8 by only 1%, while edging out GPT-5.5 by 1% and Opus 4.7 by 11%. On PostTrainBench, where each agent is given an H100 GPU and evaluated by how much it can improve small models through post-training, GLM-5.2 outperforms both Opus 4.7 and GPT-5.5, ranking second only to Opus 4.8. On SWE-Marathon, an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.

Lots of rumors about OpenAI's new model Astra and Fable 5.1 - great at long running agentic loops - Astra is better than Fable 5 - Anthropic to release a Fable 5.1 to compete with it - both models in safety testing - will likely release after GLM 5.5

our @Kia MVP for march is @MELOD1P 🕺🛸💕 ▫️ team-highs in PTS (20.7), 3PM (4.3), AST (6.1) and STL (1.5) ▫️ multiple 3PM in a career-high 20 straight games ▫️ 1 of 4 NBA players to average 20-5-5-1 in march

The global cryptocurrency market capitalization stands at approximately $2.28–2.37 trillion, having declined slightly by about 0.5%–1.4% over the past 24 hours. Trading volume remains around $4.5–5.5 billion. Bitcoin’s dominance rate is approximately 57.8%–57.9%, while Ethereum’s is about 10.5%. MicroStrategy may pause its Bitcoin accumulation, ending its 13-week streak of consecutive purchases, which has drawn market attention. I do not believe BTC can stabilize at this level; I recommend staying out of the market and waiting on the sidelines.

From Infrastructure to Interface: Closes the Loop. In response to community demand, we have officially synchronized our Web Chat with our API ecosystem. The four frontier models—including GPT-5.5-Instant, DeepSeek-V3.2, MiniMax-M2.7, and GLM-5.1—are now fully accessible to all web users. We have bridged the gap between developer-grade integration and consumer-facing interaction. Whether via API or Web, you can now experience the same production-grade reliability and reasoning consistency. Compute without limits. Innovate without boundaries. Start now: 从底层基建到场景应用： 正式打通全链路模型闭环。🌐 应社区用户的热切期待， 现已完成 API 与 Web Chat 的双端能力同步。此前在 API 侧首发的 GPT-5.5-Instant、DeepSeek-V3.2、MiniMax-M2.7 以及 GLM-5.1 四大顶尖模型，现已在网页端全量上线。 无论你是追求高自由度的开发者，还是深耕生产力的专业用户，现在都能在 获得一致的生产级可靠性与逻辑精度。 算力不设限，灵感无边界。 立即体验：

Revenue: KRW 171.5 trillion (+130.0% YoY, 1.7% below consensus) Operating profit: KRW 89.5 trillion (+1,813.8% YoY, 5.5% above consensus) Operating margin: 52.2% (+45.9ppt YoY, above expectations) Net income: KRW 71.3 trillion (+1,344.4% YoY, 1.8% above consensus)

BREAKING: Elon Musk announced that Grok 4.5 is now in private beta at SpaceX and Tesla, with early evaluations showing performance "close to, perhaps exceeding Opus" Grok 4.5 is based on the V9 foundation model, which has 1.5 trillion parameters. That is 3 times the size of the V8 model currently serving all Grok production traffic. The model was trained with a large amount of Cursor developer workflow data in supplemental training. Opus refers to Anthropic's Claude Opus 4.6, one of the leading general-purpose AI models alongside GPT-5.5. Musk's comparison is the first public benchmark claim against a frontier rival for Grok 4.5. Musk also said that SpaceX will release a completely new foundation model, trained from scratch, every month for the rest of 2026. That cadence is unprecedented. OpenAI, Anthropic and Google currently release major frontier models every 3 to 6 months. The private beta at Tesla and SpaceX is the first internal deployment of Grok 4.5. Reinforcement learning continues to improve the model. V9 foundation model training completed on May 26, 2026. SpaceX acquired Cursor for $60 billion earlier this month. (Source: @elonmusk on X, June 28, 2026)