The article reflects on the shift in perspective after the release of Fable, a new model that promised to solve many coding challenges at a comparable or lower cost. Prior to Fable, developers felt it was pointless to invest heavily in coding tools or context strategies, as newer models would likely render them obsolete. However, Fable’s performance was so impressive that, despite its high cost, it prompted a reevaluation of how work was distributed across different models such as Opus, 5.6, K3, and GLM.
The release of llm-anthropic 0.28 introduces support for Claude Fable 5.1, enabling default display of reasoning traces for compatible models. It also adds a new llm_anthropic.ClaudeRefusal exception to handle cases where Claude returns a refusal. These updates enhance debugging and error handling for users of the library.
The article reports the release of new AI models: Claude Opus 5.5 by Anthropic and GPT‑6 Sol and GPT‑6 Luna by OpenAI, noting that GPT‑6 variants are priced at half the cost of their GPT‑5.6 counterparts. It provides a detailed pricing table comparing input, cached input, and output costs across several models, highlighting how GPT‑6 Luna is among the cheapest ever offered by OpenAI. The author also comments on visual differences in model outputs, noting that GPT‑6 outputs are more muted compared to GPT‑5.6.
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.
Claude Sonnet 5.5, a new model from Anthropic, is announced as running 30% faster and costing up to 30% less than its predecessor while matching or exceeding performance on benchmarks. It is now the model powering the free tier on claude.ai, offering a more capable free experience than OpenAI’s ChatGPT free tier, which uses Luna 5.6. The release also mentions upcoming availability of Haiku 5.5 in the coming weeks.
Maximize your Claude Fable 5 usage The post How to Get the Most Out of Claude Fable 5 appeared first on Towards Data Science .
By Eivind Kjosbakken
GPT‑6 Astra is a new OpenAI model rolling out today to a limited set of organizations and soon to all ChatGPT Plus, Pro, Business, Enterprise users, and via the OpenAI API and AWS. It is priced at $10/million input and $50/million output, matching Claude Fable 5/5.1, and outperforms Fable on most OpenAI self‑reported benchmarks, achieving 99.9% on the ARC‑AGI 3 benchmark with a custom Provider Adapter harness. Astra excels in security tasks—scoring 100% on ExploitBench, 42.4% on ExploitGym, and 99.2% on SRE‑Bench—and handles long context well, hitting 100% on OpenAI’s eight‑needle benchmark at 256K–512K tokens and 96.3% at 512K–1M tokens, though it remains behind Fable on the Intelligence Index and Meta’s Muse Spark 1.3.
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.
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.
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
Simon Willison reflects on how programming used to be a rewarding career, describing it as a job where developers solved complex logic puzzles for high pay and had full control over strategy and timelines. He notes that coding agents and large language models (LLMs) now handle many of these puzzles, offering a "gift that keeps on giving" for those who enjoy building systems without caring deeply about the underlying problems. However, for programmers who found joy in directly solving problems, this shift represents a significant loss.