Four developer stories from Monday, October 26, 2026, each read from its primary source: Anthropic's first public S-1, the LLM price cuts behind a lot of this week's Hacker News talk, a vLLM setting that fixes tool calls on open models, and a dermatologist's test of two frontier chatbots against a ten-year-old image network. No investment or medical advice here, only what the documents say.

TL;DR

  • Anthropic's Form S-1, accepted by SEC EDGAR on October 26 at 09:00 New York time, reports revenue of $16,286 million for the first six months of 2026 and a net loss of $80,748 million, of which $72,388 million is a change in the fair value of convertible notes. Price, share count and the newest quarter are blank.
  • OpenAI says GPT-5.6 Luna got 80 percent cheaper on July 30 and lists GPT-6.1 Sol at $2 input and $10 output per million tokens. Anthropic says Opus 5.5 costs about 40 percent less to run than Opus 5.
  • Ubicloud measured 92 percent tool-call schema accuracy for Kimi K2.6 on vLLM 0.28 and 100 percent after switching on constrained decoding.
  • A dermatologist's 200-lesion test: a fine-tuned Inception v3 network was right 62 percent of the time, GPT-6 Astra and Claude Fable 5.1 42 percent each. He lists the caveats himself.
  • Verdict: NEEDS REVIEW.

1. Anthropic's S-1: the numbers, and the blanks

The filing is a public Form S-1, and EDGAR's index page shows it accepted on October 26, 2026 at 09:00:11. It is preliminary. The cover leaves the price range blank, the share count is blank, and so are the revenue for the three months to September 30 and a run-rate figure dated October 31. Anthropic has applied to list Class A shares on the Nasdaq Global Select Market under the symbol ANT.

The summary table on page 23 has what is filled in. Revenue was $41 million in 2023, $386 million in 2024 and $4,585 million in 2025. For the first six months of 2026 it was $16,286 million, against $1,163 million for the same months of 2025. Compute and infrastructure expenses for those six months were $8,945 million. The operating loss was $693 million, which the filing's non-GAAP table shows as an operating margin of minus 4 percent. The same table shows an adjusted operating income of $1,618 million, a 10 percent adjusted margin, after adding back stock-based compensation of $2,185 million and $126 million of non-cash charitable contributions.

The net loss was $80,748 million, and the reason is one line. The change in fair value of convertible notes, derivative liabilities and SAFEs was a loss of $72,388 million. A footnote says these changes were primarily driven by an increase in the fair value of the outstanding convertible promissory notes, resulting from an increase in the company's enterprise valuation. In plain words, as the company becomes worth more, the notes it owes become worth more, and the accounts record that as a loss. The filing puts the accumulated deficit at $132.9 billion, of which $113.2 billion is these fair-value changes, and says it held $112.4 billion in cash and cash equivalents on June 30.

Two other items caught my eye. The risk factors describe the Bartz copyright settlement, agreed in September 2025 for $1.5 billion and approved by the court in July 2026; it also has its own line in the 2025 expense table. We covered the story behind it in our OpenAI pirated books episode. And for developers, the business section says that in the three months to September 30, Claude Enterprise users who did not use Claude Code spent less than $75 a month on average, while the top 5 percent who did use it spent approximately 20 times that amount.

On governance, a Long-Term Benefit Trust elects a majority of the board through its own Class T shares, and a founders' company will hold a single Class F share worth 50.1 percent of the votes. The filing calls Anthropic a controlled company.

I am leaving valuation, pricing and timing to the document. Those are blank.

2. Why OpenAI and Anthropic cut LLM prices

Max Woolf (minimaxir) published a timeline of the price changes since June that reached the Hacker News front page with 388 points. I checked the vendor pages behind the main entries. OpenAI's July 30 post says GPT-5.6 Luna "will cost 80% less" and Terra 20 percent less. OpenAI's GPT-6.1 Sol page lists Astra at $10 input, $50 output and $1 cached per million tokens, and GPT-6.1 Sol at $2, $10 and $0.10, "one-fifth of Astra's standard input and output token prices". Anthropic's Opus 5.5 announcement says the model performs at the level of Claude Fable 5.1 on most work and costs 40 percent less to run than Opus 5. We covered the first round of this in our September price war episode.

Woolf's explanation is competition, especially open-weight models. For scale: Kimi K3.1 Pro, an open-weight model released October 23, lists on OpenRouter at $2 input and $10 output, the same list price as GPT-6.1 Sol. His other numbers, such as the estimate that cache reads are 96 to 99 percent of agent token spend, are his own; I did not re-derive them. His personal reaction is a good detail: he cut his Codex plan to a fifth of the usage limit, because the same model now went four times further.

Anthropic's S-1 gives its own account. It says Fable 5.1 and Opus 5.5 each reduced customers' cost per task, by about 25 percent and about 40 percent against their predecessors, as "a combination of a lower price and improved efficiency", and that "because of our compute efficiency, we have been able to make these improvements while preserving our margins." It adds that after Opus 4.5 launched in November 2025 at a lower price, quarter-on-quarter revenue growth went from 48 percent to 132 percent, with several factors involved.

What to do: re-run your cost estimates against current price pages. In agent work the cached-input line is the one that moves the bill.

3. Ubicloud's fix for tool calls on open models

Ubicloud (October 20) measured how often Kimi K2.6 on vLLM 0.28 produced a tool call that matched the tool's JSON schema: 92 percent, which means 2 in every 25 failed. Failed calls pollute the context with error messages, lower quality and cost retries.

The fix is constrained decoding. At each step the server only allows tokens that keep the tool call valid under the schema, using XGrammar's structural tags, so the model cannot pick a token that would break the schema. In vLLM 0.31 it is a server flag, and the post says --tool-strict-level is how deployments turn tool-call strictness on. Ubicloud measured 100 percent schema accuracy on Kimi and DeepSeek models in Moonshot's K2VV ToolCall benchmark, with negligible performance overhead.

vLLM keeps it off by default because users reported issues with some models after it was enabled by default, with GLM 5.1 among those named. Ubicloud's conclusion is that the engine and its settings change the output quality you see. This is a vendor blog about its own serving setup, so test your own model and workload before trusting the number.

4. A ten-year-old CNN against two frontier chatbots

Dr Magnus Lynch, a consultant dermatologist at Guy's Hospital and honorary reader at King's College London, published a test on October 25. He took 200 biopsy-proven lesions from the public MILK10k dataset, 100 that should not be discharged and 100 that are safe to discharge, and gave GPT-6 Astra and Claude Fable 5.1 both a clinical photograph and a dermoscopy image. He also fine-tuned Inception v3, an image network Google published in 2015, on 68,011 ISIC Archive images for about 20 minutes on one H100.

The network named the right diagnosis out of six for 125 lesions (62 percent). The chatbots named it for 84 and 85 (42 percent). On melanoma first choice it scored 13 of 30 against 8 and 11. The chatbots had favourites: GPT-6 Astra called 75 lesions basal cell carcinoma when 25 were.

Lynch lists the caveats himself. Two hundred cases show a pattern and cannot rank close results, each chatbot was run once, every lesion had been biopsied so the cases are harder than usual, Inception v3 is a baseline, and regulated products built on newer methods perform better. The model runs in a phone browser through WebGPU and nothing is uploaded. This is a blog test and nothing here is medical advice.

One more detail: Lynch writes that the analysis and first drafts were produced with Claude Code, and that one of the two chatbots tested is also Anthropic's.

Verdict: NEEDS REVIEW

The Anthropic filing has a loss on paper, a profit in an adjusted measure and a lot of blanks. The price story rests partly on one blogger's reading. The skin cancer test is 200 cases run once. Read the filing yourself.

Sources

This article expands on an episode of **The Daily Diff, a five-minute daily video on what shipped and what broke in tech.
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