Good morning and welcome to a brand new Friday, October the ninth, 2026. You are tuned to Not a Llama, the daily podcast where we separate the signal from the hype. Today we have three big stories to get through. First, Anthropic has shipped Claude Haiku 5.5, its cheapest and fastest small model yet. Second, StepFun's Step 5 Preview, a one million context model, is now live on OpenRouter. And third, we put decision models head to head to see which runs fastest on an RTX 4090.

Tonight, the lead story: Anthropic just shipped Claude Haiku 5.5, billed as its cheapest and fastest small model yet. According to Latent Space's AINews, this marks the first Haiku-tier update in about a year. Anthropic calls it the cheapest, fastest, and most capable small model it has ever released, and positions it as a subagent to pair with Opus 5.5 or Sonnet 5.5 for high-volume, cost-sensitive work.

On pricing, Anthropic says Haiku 5.5 costs roughly 75 percent less to run than Haiku 4.5 on average. For context, under 100K tokens, input runs $0.10 per million tokens and output $0.50, with cache reads at just $0.01. The model now offers a 1M-token context window, up from 200k for Haiku 4.5, and handles text and image input with text output. It is live on the Claude Platform and in Claude Code, with a same-day SDK update adding computer-use and browser-use toolsets.

According to third-party benchmarks from Artificial Analysis, Haiku 5.5 scores 43 on the Intelligence Index at max effort, trailing Sonnet 5.5 by 13 points. But there are caveats. Token usage is verbose, using roughly 162K output tokens per Index task, about three times that of GPT-6 Luna. AutomationBench-AA scored 35 percent, affected by a pre-release safety bug that Anthropic is fixing. Cost figures remain provisional. Still, this revives Anthropic's budget tier at a time when rivals are pushing hard on price. A bold move, and one worth watching closely.

Back to the open-weight arena, where the pace shows no signs of slowing. StepFun has surfaced its Step 5 Preview, a large mixture-of-experts model built for agentic work. It carries 27 billion active parameters out of 600 billion total, according to a Hacker News discussion from October 8th, 2026. It targets software engineering and professional knowledge work, with particular strength in finance, and is designed for long tasks across big codebases and documents. The headline feature is a one million token context window, supporting up to 64,000 completion tokens. It handles text, images, and video on input, and returns text, with tool calling and structured JSON outputs. On pricing, it lists 1.00 dollars per million input tokens, 2.70 dollars per million output tokens, and 0.05 dollars per million for cache reads, though the source notes caching and discounts often push the actual price well below the listed figure. It is hosted through OpenRouter, which forwards every request directly. Reported uptime sits at 99.99 percent over three days, with 3.52 billion tokens of activity. Public apps sending traffic include OpenClaw, the Hermes Agent from Nous Research, Cline, and Kilo Code. No benchmark numbers are stated in the source, so take performance claims with a grain of salt.

Let us get right into it. With a flood of new decision models arriving, two creators decided to benchmark them locally, and what they found shows just how much size and hardware actually matter. The first test put four models head to head on an RTX 4090. Each one read nine Wikipedia articles about centipedes, processing one word per request and flagging any word naming a centipede. According to a post on Reddit subreddit r/LocalLLaMA, Laya clocked in at 3.9 milliseconds per word, the fastest of the group. d1 ran at 6.0, Clef Flash at 24.4, and Lev at 51.0. In thirty two seconds, Laya processed 7,980 words, while Lev handled just 626. Accuracy stayed high across the board, with Lev leading at 98.9 percent. The author notes that accuracy is inflated, because answering no counts as correct. In a second experiment, six models including Kev 4B, GPT-6 Luna and Laya each played Pac-Man one hundred times. jev 1.13 took the top score at 2,750, while Laya came in last at 639. The author published a leaderboard and an open source repo so anyone can run their own model.

{ "text": "Now, a privacy-first note-taker is making waves. Google has launched AI Edge Foresight, an experimental app for Mac that transcribes meetings entirely on-device. According to The Verge and TechCrunch, it never connects to the cloud. That is a bold move against Granola, which has built its reputation on local inference."

The app runs on Google's EmbeddingGemma 2 model, which TechCrunch specifies has 740 million parameters. You get a split-screen view with shorthand notes on one side and AI-generated polished notes on the other. There is also a built-in assistant to answer questions about your transcripts. Users can upload PDFs, Google Docs, Microsoft Office formats, plain text, Markdown, and web bookmarks to build a knowledge base."

Google says files, meeting audio, and notes never leave your computer. It is free to use, optimized for Apple Silicon Macs. TechCrunch notes Google may later release a consumer version across video apps using cloud models. One caveat, though. TechCrunch's reference to a Gemma 4-powered assistant is unverified and appears inconsistent with the EmbeddingGemma 2 model cited elsewhere." }

Now to Google, which turned Gemini into an agentic assistant built for the workplace. According to TechCrunch, the announcement came at a Google Cloud event on Thursday, where a unified Gemini agent was unveiled, able to plan, execute tasks, and operate across business apps. The agent can delegate work to subagents, use multiple AI models, and even receives its own Workspace identity, complete with an email address. The Verge describes this as a coworker agent, reachable at an @agents.company.com address, running in the cloud and holding context across all connected devices. Early testers include On, Shopify, and PayPal. Accessible from iOS, Android, Windows, and Mac, plus the command line and Slack, it plugs into Google Workspace, Microsoft 365, Jira, BigQuery, and more. According to TechCrunch, CEO Sundar Pichai said Gemini now has over 1 billion monthly active users, with nearly 90 percent of Fortune 100 businesses using Gemini Enterprise. For now, this lands with enterprise customers in private preview, with a consumer rollout planned later, following the consumer-facing Gemini Spark agent launched in May.

Time to check in on the OpenRouter price board. According to OpenRouter, dated 2026-10-09, a batch of model prices just shifted, and the headline move is a DeepSeek drop. DeepSeek Flash Latest input price fell from 0.0184 USD to 0.016 USD per million tokens. But its output price rose from 1.0 USD to 1.2 USD per million tokens. The other changes lean the opposite way. Mistral Nemo input price rose from 0.019 USD to 0.029 USD per million tokens, with output holding steady at 0.03 USD per million tokens. DeepSeek Pro Latest went up across the board, input rising from 0.1575 USD to 0.18 USD per million tokens, and output jumping from 4.2 USD to 8.0 USD per million tokens. Then there is the big one at Z.ai, where GLM Flash Latest output price climbed from 0.7986 USD to 2.0 USD per million tokens, while input stayed put at 0.032 USD per million tokens. All four changes come from that single OpenRouter notice. There is no second source, no parameter counts, no context lengths, and no benchmark figures attached. It is a straight pricing rundown, nothing more.

Today the big takeaway is that small models are getting cheaper and faster, with Anthropic shipping Claude Haiku 5.5 as its most affordable option yet. Thank you for tuning in to Not a Llama. A brand new episode arrives tomorrow morning. Until then, stay curious and keep exploring the AI world.