与「Max_Ver」相关的搜索结果

Ethan Pimstone sets a new max vertical jump world record with an incredible 1.33m (52.5”) leap. 🚀 The hang time on this jump is absolutely unreal. 😳 (Via: ethanpimstone1/IG)

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

Sophie Birenbaum está lista para brillar en el escenario... pero el verdadero drama lo tiene en casa. 'No se desea buena suerte', protagonizada por Sunny Sandler, Melanie Lynskey y Max Greenfield. Disponible el 14 de agosto.

Grok Build just got another major update, giving developers deeper visibility into token usage and costs, more flexible model configuration, smarter prompt batching, and enhanced diagnostics for troubleshooting Release Notes: v0.2.109 Features: • /usage now shows token counts and cost for the current session. • grok doctor fix terminal.ssh-wrap can install the recommended SSH wrapper alias. • [model_providers.] lets operators share gateway settings across custom models. • Reasoning effort now accepts max as its own tier (above xhigh) when the model advertises it. • Queued follow-ups can now be batched into a single model turn with the new combine_queued_prompts setting. • /doctor is now the main slash command for terminal, tmux, clipboard and keyboard diagnostics. • read_file now returns full Markdown files inside skills/directories without truncation. Bug Fixes: • Voice dictation now explains when the microphone delivered only silence (macOS permission) versus no speech detected. • Duplicate 'Worked for' markers no longer stack in the transcript when background tasks defer during a parked turn. • The idle status row now clearly says '1 subagent still running' instead of 'watching · 1 subagent' when background work remains. • Background /loop iterations no longer overlap when descendant subagents are still running.

Qwen3.8 is launching and going open-weight soon!🌐 With a massive 2.4T parameters, this model is continuously evolving. We believe it’s one of the most powerful model available today, compatible to leading frontier AI models , second only to Fable 5. You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork. Be among the very first to try it out. Can't wait to hear what you build. Stay tuned! 🚀 Token Plan international：

Qwen3.8 is launching and going open-weight soon!🌐 With a massive 2.4T parameters, this model is continuously evolving. We believe it’s one of the most powerful model available today, compatible to leading frontier AI models , second only to Fable 5. You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork. Be among the very first to try it out. Can't wait to hear what you build. Stay tuned! 🚀  Token Plan international： China：