Weekly News Roundup: CW41 - Haiku 5.5 cuts the price for real

Anthropic closed out its Claude 5.5 family on 7 October with Claude Haiku 5.5, and this time the price moved with it. For prompts up to 100,000 tokens the small model lists at $0.10 for input and $0.50 for output per million tokens against Haiku 4.5’s $1 and $5, a 90% cut; above that length it halves to $0.50 and $2.50. Anthropic’s own headline is a different number, “on average, it now costs around 75% less to run”, and a footnote explains the gap: the 75% blends the two rates and also accounts for the new tokenizer, which spends slightly more tokens per task. The sticker price is real. The headline is a cost-per-task average that has already netted the tokenizer change out.

Around that launch, Cisco supplied the week’s sobering number. Its new benchmark asks whether a model can find a vulnerable file in a real repository at all, and on Cisco’s own leaderboard the best system reaches 0.229 File F1 while 190 of the 500 tasks defeat every model tested.

tl;dr

• 💰 Haiku 5.5 closes out the Claude 5.5 family: Anthropic’s smallest model costs $0.10 and $0.50 per million tokens up to 100K tokens, a real 90% cut, with the 75% headline being Anthropic’s own blended cost-per-task figure.
• 🧩 Claude’s artifacts leave beta: Dashboards and Motion open as betas on paid plans and on Team and Enterprise, while Docs, Slides and Design come out of beta on every plan including Free.
• 🐘 Mistral’s trillion-parameter model arrives without its weights: Mistral Large 4 is a public preview at 1 trillion total and 52 billion active parameters, with open weights promised by the end of the month and no licence published.
• 🔐 Cyber capability, measured and used: Cisco’s VLoc Bench puts the strongest model at 0.229 File F1 with 38.4% of tasks unsolved by every system, as a released methodology and an MCP tool catalogue put the same capability to work.
• 🎨 A clean-room Adobe suite, built in Rust: seven Adobe applications reimplemented from scratch in Rust under a permissive licence, an early alpha its own README says is not yet a Photoshop replacement for daily work.
• 🍌 Two model releases from Google: EmbeddingGemma 2 puts text, code, image, video and audio into one 768-dimension space at 740M parameters, and Nano Banana 2.1 is generally available with its predecessor deprecated the same day.

Haiku 5.5 closes out the Claude 5.5 family

Two weeks after Sonnet 5.5 arrived with its list price unchanged, the cut landed on the smallest model in the family. Claude Haiku 5.5 costs $0.10 and $0.50 per million tokens on prompts up to 100,000 tokens against Haiku 4.5’s $1 and $5, and $0.50 and $2.50 above that threshold, with cache reads at $0.01 and $0.05. Anthropic’s footnote supplies the reason its headline is smaller than its table: 90% of Haiku 4.5 requests fell under the 100,000-token line, so the average saving blends a 90% cut with a 50% one, and the updated tokenizer pulls it down a little further.

Two other numbers moved in the same announcement. Cache reads on Sonnet 5.5 halve from $0.20 to $0.10, which Anthropic says makes Sonnet 5.5 around 20% cheaper on most agentic work, and a new monthly API credit arrives for Max and Team subscribers. Haiku 5.5 is the first Haiku-class model with an adjustable effort setting, so the same weights can be tuned for cost or for capability. It ships under the model id claude-haiku-5-5 on the Claude Platform, in Amazon Bedrock and on AWS, on Google Cloud and Microsoft Foundry, in Claude Code, and in Claude.ai from Free through Enterprise.

The benchmark table is where the numbers get loud, and every one of them is Anthropic’s. Terminal-Bench 4.0 goes from 0.0% on Haiku 4.5 to 39.2%; the OSWorld 2.1 offline subset from 15.7% to 72.4%; Humanity’s Last Exam from 10.2% to 45.9% without tools and 18.7% to 57.4% with them; GDPval-AA v2.1 from 735 to 1620. Against Sonnet 5.5’s 70.6% on Terminal-Bench the ordering inside the family is intact, and the small model is not pretending to lead it. No independent run of these evaluations was located this week.

The takeaway: the price at the common prompt length is genuinely down 90%, and that figure sits in a pricing table anyone can check. The 75% headline and the benchmark rows are Anthropic-reported figures, and no independent run of those evaluations was located this week.

Claude’s artifacts leave beta

Anthropic’s artifact products moved in both directions at once on 8 October. Dashboards and Motion opened as betas, and Docs, Slides and Design closed theirs. Dashboards is in beta on paid plans and connects to a data platform (BigQuery, Databricks, Snowflake, Amazon Redshift, ClickHouse) or a CRM such as Salesforce, then builds a dashboard from a plain-language question and keeps it current. Every chart carries the query behind it and the time the data was last refreshed, which is the detail that decides whether this survives contact with an office where somebody will ask how a number was produced. Anthropic also lists an export path into Amplitude, Grafana, Hex, Mixpanel, Omni, Perplexity, PostHog and Sigma for questions that need deeper analysis.

Motion is the narrower beta, on Team and Enterprise. It writes code that animates text, charts, shapes and images, with every word, number and timing editable and an MP4 export, and Anthropic says it does not use a video generation model, so there is no generated footage and no synthetic people. The hand-off list runs to Adobe, Descript, HeyGen, Higgsfield, invideo, Luma AI and Runway. Docs, Slides and Design are out of beta on every plan including Free, with shared editing, PowerPoint and PDF exports that keep their formatting, decks that open in Google Slides as editable files, and edits from the mobile app. For Enterprise admins the two new products are off by default, Docs, Slides and Design turn on by default on 15 October, and the standalone Claude Design site closes on 14 December. Anthropic published no price, no usage quota and no beta exit date for either beta, which is the part of this announcement easiest to fill in by accident.

Mistral’s trillion-parameter model arrives without its weights

Mistral Large 4 is a natively multimodal mixture-of-experts with 1 trillion total parameters and 52 billion active, published by Mistral in Paris on 6 October: trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own European datacentres, fluent in more than 160 languages including all the official EU languages, and priced on the model card at $1.36 and $4.18 per million tokens. The post that carried it never named the model. CW40 covered a different European lab making the same pitch at a smaller scale, and this is that argument with a frontier-sized model behind it.

The caveats here are of different sizes. Mistral’s post says 52 billion active parameters, while independent write-ups and Artificial Analysis report 49 billion, a discrepancy worth flagging rather than reconciling quietly. What actually shipped is a public preview on Mistral Studio, not open weights. The launch post promises the weights “by the end of the month” and says more details on the architecture and the licence follow then, which means the model is being described as open-weight before anything is open. Until the weights land, Mistral says it is red-teaming the model with cybersecurity leaders, vetted partners and state authorities who get reduced moderation and expanded cyber capabilities, a sentence that reads differently the week after a vendor benchmark suggested cyber capability is exactly where the guardrails start to matter.

The superlative attached to it has an independent source and a narrower scope than the launch post implies. Artificial Analysis put the preview at 38 on its Intelligence Index and called it the most intelligent model from outside the US and China, while several Chinese open-weight models scored higher on the same index, and on its Cyber Index the preview sits at 50. The vendor’s own claim is the wider one, that ML4 outperforms any open-weight model developed in the US or Europe. Independent measurement also puts the cost of a task higher than the cheapest models in its tier, at $1.13 per Intelligence Index task.

Cyber capability, measured and used

Cisco’s Foundation AI group spent September on a step most security benchmarks skip. Finding the vulnerable file is a different skill from fixing it, and VLoc Bench measures that first step: an agent gets a generic CWE description and read-only terminal access to a real repository, with no advisory text, no CVE identifier, no fixing commit and no file hint, and is scored on the files it names against the ground-truth patch. In Phase B it gets the patched snapshot and has to confirm the weakness is gone. The dataset is 500 real vulnerabilities from 290 repositories across six package ecosystems and 147 CWE categories, run against 27 language models and four static-analysis tools.

The leaderboard Cisco publishes is blunt about the state of it. Even the best model, GPT-5.5, reaches only 0.229 File F1; 190 of the 500 tasks defeat every system tested; performance falls by a factor of thirteen from the smallest repositories to the largest; and the systems that are best at finding vulnerable files are not the best at recognising that a fix held. Cisco’s own 3B Antares model ranks second overall at 0.223, ahead of general-purpose models far larger than it, which the benchmark reads as evidence that targeted training beats scale on this task.

A companion benchmark on attacker and defender asymmetry compares the same capability on source code against stripped, decompiled binaries across 95 paired C and C++ vulnerabilities: Antares-3B scores 0.823 File F1 on the source view and exactly 0.000 on the binary one, with the 1B and 350M models showing the same pattern. Cisco’s framing is that cyber safety is a question of where a model’s capability works, not whether it refuses. Every number here is a vendor result on a vendor benchmark, and no independent reproduction was located this week.

A day earlier, a security researcher published a vulnerability-hunting methodology as loadable skill files, a maintained fork of Capital One’s original VulnHunter that keeps the method independent of any single model and is built to load into Blackfrost AI’s Cyber-Frost Harness. What is verified is that the fork exists in that shape and claims a workflow that ends in an independent check of each finding. The outcomes are the author’s account, and third-party analysis of the release flags the obvious weak point, reporting that the same codebase produced anywhere from 3 to 42 confirmed findings across runs.

HexStrike AI is the reverse case, and it is older than the week. It is an MCP server that presents a catalogue of offensive security tooling to any model client that speaks the protocol; the project claims more than 150 tools and a dozen agents, including a decision engine, a bug-bounty workflow manager and an exploit generator. Every capability and count there is the project’s own and none of it was independently tested here, so the description stops at what it is: a catalogue wired to a model. The project’s 6.0 release dates to 2025, which makes the in-window fact the post rather than a release. Beside it sits one forecast: Aaron Levie, Box’s chief executive, argued on 7 October that cyber will be one of the defining domains for AI in the coming years, pointing at vibe-coded software, agentic attacks and accidental agent swarms. That is a prediction from a vendor executive, not a finding.

The takeaway: the week’s most useful statement about cyber capability came from a benchmark that measures where a model is helpful rather than how often it refuses, and its own numbers say the first step, finding the file, is not solved.

A clean-room Adobe suite, built in Rust

Two posts pointed at one repository on 6 October, and only one of them is usable. The first announced that someone had rebuilt all of Adobe for free, behind a short link; the second carried the substance. What exists is PhotoCraft and six siblings, published by the ArtCraft project as clean-room reimplementations of seven Adobe applications: PhotoCraft for Photoshop, VectorCraft for Illustrator, FilmCraft for Premiere Pro, LightCraft for Lightroom, PdfCraft for Acrobat, EffectCraft for After Effects and DesignCraft for InDesign. The suite is listed on the project’s site, written in pure Rust, native on macOS, Windows and Linux, and six of the seven also run in a browser through WebAssembly, with FilmCraft the exception. Licensing is permissive: Apache-2.0 for the suite, and dual MIT and Apache-2.0 for PhotoCraft.

What separates it from the usual clean-room clone story is the project’s own account of how far along it is: the README says much of Photoshop’s feature surface exists “in some form” and then states plainly that this is “not yet a Photoshop replacement for daily professional work”: an early alpha missing around twenty tools, with thin typography and no plug-in compatibility, and a roadmap assessment that puts true parity well under 50%. The attribution that travelled furthest is the softest part of the story. The developer, Brandon Thomas, has said publicly that he used AI coding tools and that he is not a casual vibe coder, and the claim that the suite was built with Opus 5.5 is his and the circulating post’s, not something the repository states. The repositories themselves were created on 30 September, outside this week’s window, so the 6 October posts are the in-window break.

Two model releases from Google

EmbeddingGemma 2 maps text including code, images, video and audio into a single 768-dimension space, and it is built to run on a phone or a laptop rather than in a datacentre. Google DeepMind’s card puts it at 740M total parameters: a 270M text model assembled from a 130M backbone and a 140M embedder, plus modular vision (170M) and audio (300M) encoders, over an 8,192-token context window and under Apache 2.0. Representations can be truncated to 256 dimensions at what the card describes as minimal quality loss, which is the property that makes on-device retrieval practical. The post that surfaced the release says the model needs 0.5 GB of RAM. Google’s card states no memory figure at all, and the number that does exist belongs to the first, text-only EmbeddingGemma, which runs under 200 MB quantized. The 0.5 GB figure is the poster’s.

Nano Banana 2.1 is generally available under the id gemini-nano-banana-2.1, the replacement for Nano Banana 2, which Google’s deprecation table lists. The model page lists improvements to visual quality across 1K, 2K and 4K output, fixes for tiling artifacts on wide and panoramic aspect ratios, better text rendering and infographic layout, fusion of up to 14 reference images for character and object consistency, grounding through Google Search, and configurable thinking levels. Google’s deprecation table announces no shutdown date, and some third-party write-ups quote 29 October, which is not Google’s date to give. On how much better the images are, the only available claim is the vendor’s.

Elsewhere this week

Leaving the main thread. Smaller discoveries from the week, each one worth its own line.

Google Cloud announced a Gemini agent on 8 October, described as a universal work agent, shipping in private preview with wider availability promised for Workspace Business and Enterprise. A third-party post went further and reported that Claude Opus 5, Sonnet 5.5, Gemini Argon 4 and Flash 3.8 would be selectable inside it. Nothing on Google’s or Anthropic’s side confirms that, so it stays a report.

A harness-engineering essay resurfaced this week with the argument that the environment around a model decides more than the model does. It lays out environment memory, a verification loop, a division of labour, a template file of five personal rules and a two-pass prompt. The essay’s own date is 12 September, before this edition’s window.

OpenAI’s 28-day shipping sprint produced a day that was not about Codex at all. ChatGPT Meetings is a macOS beta for Pro and Business that turns a meeting into notes inside a ChatGPT Space and deletes the audio after processing, and it surfaced this week on day 2.3 of the sprint, where the day’s post pointed at the plugin rather than at the coding agent. A reply to that post objected to exactly that.

Snippets is a note-taking app that surfaced on r/NoteTaking: handcoded, offline-first and end-to-end encrypted, with sync across devices through a GitHub integration. The thread carries its own caveats, including an Android onboarding bug, unanswered questions about Linux and Windows, and a comparison to Bear Notes.

Closing

The week’s through-line was the gap between what a vendor publishes about itself and what a reader can check. Anthropic’s price table is the checkable kind: $0.10 and $0.50 per million tokens up to 100,000 is a number that will show up on a billing page, while the 75% beside it is a blend the company calculated for itself. Cisco’s benchmark is vendor work of the same shape, and its own summary admits that the best system scores 0.229 File F1 and that most tasks defeat every model tested. Mistral’s weights fall into a third category, promised by the end of the month and checkable by nobody yet.

That’s the week. See you in CW42.