Introducing Muse Glimmer
Introducing Muse Glimmer Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.
The article announces the release of llm-meta-ai 0.2, featuring Meta AI’s new Muse Wonder and Muse Wonder Full models, described as the most capable models yet. These models are suggested to be Meta’s first Fable‑class models, with Artificial Analysis scoring Muse Wonder (Max) equivalent to GPT‑6.1 Sol. The author updated the llm-meta-ai plugin to support the new model and provides pelican‑drawing benchmarks across six reasoning levels, noting significant improvements over previous releases.
Introducing Muse Glimmer Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.
Simon Willison tested GPT‑6 Astra by generating SVG pelicans riding bicycles at various reasoning levels and compared the results to GPT‑5.6 Sol, Terra, and Luna. The Astra pelicans consistently outperformed the other models, especially at low and xhigh reasoning levels, and even the Astra max version produced high‑quality images. Astra also used fewer tokens and was roughly twice as expensive as Sol, yet its low‑level output was cheaper and superior to any Sol model.
Qwen 3. 8 27B scores 52 on the Artificial Analysis Intelligence Index That's the same score as GPT-5.
Simon Willison comments on GPT 6.1‑Sol, describing it as "Near‑Astra intelligence for a fifth of the price." He notes that the model’s pelican illustrations are similar to those of the GPT‑6 family and provides links to the live‑blog of the keynote and to the pelican images. The post is tagged with AI, OpenAI, generative‑AI, LLMs, and playful references to pelican‑riding‑a‑bicycle.
The article is a comment by Simon Willison on the Mistral Large 4 model, posted on Hacker News. He discusses the saturation of benchmarks and humorously references a benchmark involving an armadillo in fishnet tights jaywalking on Mars, comparing the performance of several large language models including Claude Opus, GPT, Gemini, and Mistral Large 4.
Release: llm-gemini 0. 33 It's been a while since the last llm-gemini release.
The article announces the release of llm‑gemini 0.35, adding support for the new Gemini 4 Argon model available through the Gemini API and for customers on the $100/month subscription plan. It highlights Argon’s pricing—$2/million input and $10/million output at launch, with a planned price increase on December 1st—and compares its cost-effectiveness to other models, noting it remains cheaper than many competitors even after the increase. The post also showcases example SVG outputs and creative illustrations generated by Argon, such as a pelican riding a bicycle and an armadillo in fishnet tights on Mars.
The release of llm-mistral 0.16 introduces support for reasoning models, notably the newly released Mistral Large 4. This update expands the library’s capabilities to handle more advanced language model tasks that involve reasoning. The release is tagged under llm, mistral, and llm-reasoning.
The article discusses Meta’s new AI system, Muse, which offers each user a persistent Linux VM in the cloud and is marketed as an easy-to-use, consumer‑friendly agentic AI. It highlights the system’s technical innovation and the company’s packaging as a cute mascot, while noting that users may not fully grasp the power and potential danger of such an advanced tool, especially when running it locally on a Mac.
DeepSeek V4 Pro 0813 (on OpenRouter) The latest DeepSeek Pro model is now available, via API only. I had to link to OpenRouter because DeepSeek don't have any obvious announcement page for their new model.
Mistral has released a preview of its new Mistral Large 4 model, a 1 trillion‑parameter, 49 billion‑active‑parameter language model trained on a cluster of 3,800 NVIDIA Grace‑Blackwell GPUs. The preview is available through their API, with two reasoning levels—"none" and "high"—and the company plans to release the open‑weights version by the end of the month. In preliminary tests, the model scores 38 on Artificial Analysis, outperforming last year’s Mistral Large 3 and approaching the performance of larger competitors.
Qwen3.8-Flash-Next is an open‑weights multimodal Mixture‑of‑Experts (MoE) model previewing the architecture of Qwen4. It contains 125 B tokens with only 6 B active, giving a performance boost. The author has tested it on a DGX Spark with Unsloth quantized models, exploring variants like UD‑IQ1_S and UD‑Q2_K_XL, and highlighted a high‑reasoning‑effort example from UD‑Q2_K_XL.