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Sunday

  3. Story19 reports · 4 facts

    Anthropic releases Claude Opus 5.5

    On September 22, 2026, Anthropic released Claude Opus 5.5. According to official materials, the model ranks first on Artificial Analysis's Intelligence Index v4.3 with a score of 58, 7 points higher than Claude Opus 5, and tops Vals AI's Vals Index at 69.69%; it supports up to 1 million tokens of input and 128,000 tokens of output, with reasoning enabled by default across five levels, and API pricing at $4/$0.25/$20 per million input/cached/output tokens. Earlier reports said its output price was $20 per million tokens, lower than the previous generation's $25. On September 23, Yuchen Jin said it was about 30% faster and about 40% cheaper per task than Opus 5. On September 25, Thomas Wolf said he used Opus 5.5 to generate a video from a Google Doc. On October 4, Ado Kukic said that using Opus 5.5 at medium and high effort he did not hit the 5-hour limit, using at most about 74% in a single session. On October 9, Ado again said that the weekend after Opus 5.5's release was his most productive coding day of the year so far. During the same period, Anthropic also released Sonnet 5.5 and Haiku 5.5, and lowered Sonn

    On September 22, 2026, Anthropic released Claude Opus 5.5. According to official materials, the model ranks first on Artificial Analysis's Intelligence Index v4.3 with a score of 58, 7 points higher than Claude Opus 5, and tops Vals AI's Vals Index at 69.69%; it supports up to 1 million tokens of input and 128,000 tokens of output, with reasoning enabled by default across five levels, and API pricing at $4/$0.25/$20 per million input/cached/output tokens. Earlier reports said its output price wa

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Wednesday

Sunday

  1. Story1 reports · 1 facts

    Qwen Releases Omni-Modal Model Qwen3.8-Omni-Flash

    On September 18, 2026, Qwen released the omni-modal model Qwen3.8-Omni-Flash. According to official information, this is its first omni-modal model built around agent capabilities, natively supporting audio-visual understanding, reasoning, and tool calling. The company says its audio-visual capabilities are close to Gemini 3.8 Flash, with agent performance improving by an average of 19.5 points on WildClawBench-MM and UniClawBench; it supports a 1M token context and uses 51.8% fewer tokens than static understanding on OmniVideoBench. Video input costs are about 89% lower than Qwen3.5-Omni-Plus. Alongside the release, Qwen-MM-Plugins is being open-sourced, and Qwen-Live Harness is coming soon. There are currently no subsequent independent development facts.

    On September 18, 2026, Qwen released the omni-modal model Qwen3.8-Omni-Flash. According to official information, this is its first omni-modal model built around agent capabilities, natively supporting audio-visual understanding, reasoning, and tool calling. The company says its audio-visual capabilities are close to Gemini 3.8 Flash, with agent performance improving by an average of 19.5 points on WildClawBench-MM and UniClawBench; it supports a 1M token context and uses 51.8% fewer tokens than

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  2. Story1 reports · 1 facts

    Bailing releases and open-sources native multimodal model Ling-3.0-flash-VL

    On September 9, 2026, Ant Bailing released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, the model has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K-token context window. According to the company, native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, and it introduces a visual feedback loop of observe, act, verify, and correct, used for front-end replication, GUI agents, and medical report interpretation. The model is currently available for free trial on Ling Studio, and the BF16 and FP8 versions have been open-sourced on Hugging Face and ModelScope.

    On September 9, 2026, Ant Bailing released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, the model has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K-token context window. According to the company, native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4

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Thursday