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.
Simon Willison announces the release of Kimi K3.1 Pro on the Kimi API, noting it is not yet open‑weights but full weights will be available by Nov 4 2026. The model offers a high‑speed variant 2‑3× faster at double the price, with US dollar pricing of $2.00/million input tokens and $10/million output tokens—matching GPT‑6.1 Sol and half the cost of Claude Opus 5.5. Benchmarks from Moonshot show Kimi roughly on par with Opus 5.5 and GPT‑6 Astra, scoring 55 in Artificial Analysis, ahead of several competitors.
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.
Anthropic’s top AI model is struggling to attract users even as cheaper alternatives thrive. The company’s July revenue is projected at $65 bn, up from $47 bn in May, and it expects Q3 profitability while boasting 6,000 high‑spending customers. In contrast, OpenAI’s revenue has risen 35 % this quarter, spurred by GPT‑5.6, and a Ramp AI index shows Anthropic’s newer models (e.g., Fable) are less popular than older ones like Opus 4.8.
Qwen 3. 8 27B scores 52 on the Artificial Analysis Intelligence Index That's the same score as GPT-5.
The article discusses Anthropic’s Claude Fable 5.1 release, highlighting its claimed improvements in coding, knowledge work, and problem‑solving, particularly a 52.6% score on the new Terminal‑Bench‑Science 0.1 benchmark. The author examines the model’s performance on the pelican benchmark, noting that Fable 5.1’s five reasoning levels (low, medium, high, xhigh, max) sometimes skip reasoning entirely for certain prompts, as evidenced by token counts and cost metrics. The piece provides detailed transcript data for each reasoning level when generating an SVG of a pelican riding a bicycle.
Simon Willison comments on the long‑term pricing trend of Amazon S3, noting that after a decade of price reductions the rate has plateaued at $0.023 per GB‑month. He lists the historical price points from 2006 to the present, highlighting the lack of further decreases since 2016.
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.
Anthropic redesigned its source control system, creating a custom alternative called Scion to replace Git. The change was driven by a dramatic rise in code changes—from about 2,500 per day in May 2026 to over 30,000 per day by September—requiring a system that could handle a 100 GB main‑branch history and 25 million daily checkouts. Scion was built by a seven‑person team, migrated from Git in five months, and includes a new code‑review system named Antique, while offline support was deemed unnecessary due to Claude’s constant online use.
Anthropic reported to investors that its quarterly revenue rose nearly 50% to $16.8 bn in Q3, up from $11.5 bn in Q2 and $4.8 bn in Q1. The company highlighted that $1.70 of revenue was earned for every $1 spent on computing power, and its adjusted operating margin (excluding stock‑based compensation) was 11%. It also disclosed that distillation—training models on outputs from more advanced models—accounted for 5‑7% of revenue, a practice it has materially reduced in the third quarter.
arXiv:2609.00378v1 Announce Type: new
Abstract: Large language models pay a well-documented tax on non-English text: the same content costs several times more tokens, and because attention is quadrat...
By Madhulatha Mandarapu, Sandeep Kunkunuru
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.