#模型发布

Congratulations to the Ling team @AntLingAGI on the release of Ling-3.0-flash! 🎉 A 124B MoE model activating just 5.1B parameters per token—with hybrid-linear attention and native 256K context—is an exciting contribution to production-scale agent systems. We also applaud the team’s announce-first, open-source-next release approach. Separating the announcement from the weight release lets the model team freeze the final checkpoint, configuration, tokenizer, and serving semantics, while giving open-source inference projects a stable window for correctness testing, performance tuning, Docker builds, and recipe validation. The community also gets a clear timeline instead of an ambiguous “coming soon.” This is not a step back from day-0 support—it is a more sustainable way to deliver day-0 support for the artifacts users will actually run. vLLM’s open-source support for Ling-3.0-flash is coming soon and will be available when the model weights are open-sourced. We hope more model vendors adopt this release pattern in the future! Congrats again—we can’t wait to see what the community builds! 🚀

Anthropic launched Claude Opus 5 at half the price of its most powerful model Fable 5, while at the same time beating Feble 5 in almost all benchmarks. Costs the same as Opus 4.8, $5/$25 per mn input/ output tokens, but exactly half as much as Fable 5. Frankly I am trying to figure out at this price of Opus 5 and with this great benchmark result why would I ever need to use Fable 5. Opus 5 also has less restrictive cyber classifiers than Fable, automatic fallback options, mid-conversation tool changes and no special data-retention requirement for general access. The cheaper model is winning specifically on economically valuable activities—coding, operating computers, research and completing business processes—not merely trivia or academic question answering. Anthropic repeatedly describes Opus 5 as more willing to verify, recover and continue until a task is actually complete. Examples include: - Creating its own computer-vision pipeline to reconstruct a 3D machine component from raw pixels - Finding the root cause of an open-source bug and fixing an edge case missed by the existing community patch - Building a market-data integration and then creating its own test harness when no live validation feed was available - Completing Zapier’s full churn-prevention workflow when previous models failed These are signs of adaptive problem-solving: the model notices that a required capability or validation mechanism is missing and constructs one instead of stopping. Anthropic also describes Opus 5 as more thorough about validating its work and recovering when necessary. The launch gives examples in which it constructed its own vision pipeline, identified the underlying cause of a software bug and built a test harness when no live data feed existed. That is potentially more commercially meaningful than a modest benchmark gain. An agent that finishes 70% of jobs without intervention can be dramatically more valuable than one that produces slightly better individual responses but frequently stalls. GPT-5.6 Sol leads DeepSWE: 72.7% versus Opus 5’s 68.8%

隆重推出 Claude Opus 5。 这是一个深思熟虑且主动的模型，以一半的价格接近 Fable 5 的前沿智能。

隆重推出 Claude Opus 5。 这是一个深思熟虑且主动的模型，以一半的价格接近 Fable 5 的前沿智能。

隆重推出 Claude Opus 5。 这是一个深思熟虑且主动的模型，以一半的价格接近 Fable 5 的前沿智能。

Ling-3.0-flash from @AntLingAGI looks great for production agents: 124B MoE with just 5.1B active, KDA+MLA hybrid attention, 256K context We're working with the team to bring day-0 support to SGLang, stay tuned!

Ling-3.0-flash is now free on OpenCode inclusionAI's latest model optimized for token efficiency

The gap between flash and frontier models keeps shrinking. Ling 3.0 Flash is live on Kilo now, free for a limited time, and it's punching way above its weight for agentic coding tasks. Big thanks to @NovitaAI for the speed behind it!

Ling-3.0-flash, the new MoE model from @AntLingAGI, is now free in Nous Portal for the next week! At 124B parameters and 5.1B active, it's quick to run and built for agent workloads: coding, search, research, and tool use. Try it free today at http://portal.nousresearch.com/signup

Ling-3.0-flash is live on OpenRouter via @novita_labs. A 124B-parameter MoE model with ~5.1B active per token, built for token-efficient, production-scale agentic inference. Free through August 3. Try it: https://openrouter.ai/inclusionai/ling-3.0-flash:free