Simon Willison’s Weblog

I added support to llm-gemini for the new Gemini 4 Argon model, made available today via the Gemini API and for customers of their $100/month subscription plan.

Here are the low, medium and high pelicans. As expected, they are all excellent: Gemini has a track record of producing great SVGs, and are the only lab where I know their staff have invested in SVG specifically (and who have boasted about their pelicans before.)

Here's the "high" illustration, which cost me 17.166 cents:

The pricing is interesting. At launch, Argon is $2/million input and $10/million output - the same price as GPT-6 Sol, half the price of Claude Opus 5.5 and 1/5th the price of Claude Fable 5.1 and GPT-6 Astra.

But on December 1st Argon is set to double in price - so it will be the same price as Opus 5.5.

Prices like this aren't really comparable these days since what really matters is how many tokens a model takes to achieve a goal within an agentic harness.

One useful clue for that comes from Artificial Analysis, who publish the total cost to run their benchmark using each model. Argon is looking very good there - costing $2,407 to run the benchmark, putting it close to GLM-5.3 and significantly cheaper than OpenAI, Anthropic, and Meta's latest models.

When the price doubles it will still be a little cheaper than GPT-6 Astra.

(I used ChatGPT to edit that chart! The data is all correct, I checked - I needed to get that Gemini 4 Argon info box to display without overlapping the other numbers.)

Meta AI release Muse Wonder (and Muse Wonder Full), their new "most capable models". These look like they may be Meta's first Fable class models - Artificial Analysis scores Muse Wonder (Max) as equivalent to GPT-6.1 Sol.

I updated my llm-meta-ai plugin for the new model. Here are pelicans for Muse Wonder for the six reasoning levels: minimal, low, medium, high, xhigh, max. This is the max one:

For comparison, here are pelicans for Muse 1.3 (released September 2nd) and Muse 1.2 (released August 5th). Meta AI are putting out a new model a month, and the improvements between each model (at least in terms of drawing pelicans) are material.

I was working on a project from my phone using Codex Remote when I found myself wanting to edit a credentials file without round-tripping those credentials through the LLM itself.

So... I had Codex and GPT-6 Astra Extra High build this Python web app which provides a web UI for viewing and editing files in a folder. It's intended to be run over Tailscale, so I can have an agent start this running and then edit files via my Tailscale network directly from my phone.

Here's the interface for browsing files in a folder:

Editing uses CodeMirror 6 - I was going to use Monaco but that still doesn't have a great mobile experience yet.

I released ttok 0.4, ran uv tool upgrade ttok, piped a file into the new version... and realized that it was defaulting to the GPT-4 tokenizer when it should very clearly default to GPT-5/GPT-6 instead!

I figured switching the default was a reasonable excuse to finally ship a 1.0.

OpenAI haven't actually confirmed that GPT-6 uses the same tokenizer as the GPT-5 family yet - there's an angry issue about it - but I found this commit by William Liu which reports on an experiment he ran confirming that the tokenizers are likely the same:

All seven GPT models (5.5, 5.6 Sol/Terra/Luna, 6 Astra/Sol/Luna) report 44,794 tokens and match each other on every one of the 31 fixtures. GPT-6 introduces no input-count change on this corpus.

ttok is my CLI tool for counting tokens, using OpenAI's open source tiktoken library.

It hasn't been in updated in a couple of years, but I finally fixed a Click warning, updated CI, and added a --list-models command to list available models.

It works with uvx, so you can count tokens in anything like this:

cat file.txt | uvx ttok

I wanted to see if Claude Opus 5.5 could compose music, so I tried this:

I want you to write some computer game music for me. First design simple text based format for the music and build an artifact that can play it out loud - include some example tracks in that artifact

I am looking for music of the quality of the original secret of Monkey Island

It leaned a lot harder into the Monkey Island theme than I had intended, but the results are surprisingly good.

I wonder if the ability to compose competent music is similar to the 3D graphics thing - a new capability for text models that emerged in the past few months?

Would need some careful experiments with other recent and not-so-recent models to confirm if this is new or if they've been able to do this for a while.

Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results:

I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment.

I pasted his image into a Codex Remote session (GPT-6 Astra) and had it run the same experiment using Qwen3.8-27B-Q4_K_M.gguf. Here's the result for a run of 30 attempts per combination with reasoning disabled:

Then I ran it again with reasoning enabled. This took a lot longer per pair, so instead of running 30 samples per square I ran just one - which results in a much less visually appealing heatmap since each square is either 100% or 0%:

It got the right answer in 167 out of 169 attempts, and since these were one-shot I'm confident a second run would produce different results here.

Here's a version of the report that includes the reasoning traces from some of those larger calculations, which include text like this:

Wait, let me redo this more carefully.

4,299,366,105,622
6,088,794,067,970

Let me align them:
4 2 9 9 3 6 6 1 0 5 6 2 2
6 0 8 8 7 9 4 0 6 7 9 7 0

Adding from right to left:
Position 1 (units): 2 + 0 = 2
Position 2 (tens): 2 + 7 = 9
Position 3 (hundreds): 6 + 9 = 15, write 5, carry 1

I presented a closing keynote for the WeAreDevelopers World Congress North America yesterday. As a STAR moment I decided to weave in references to the record breaking kākāpō breeding season we had in 2026.

For my closing slide I wanted to celebrate, and I had seen some buzz around how good Claude Opus 5.5 was at creating pixel art animations. So I rounded up three Kakapo photos from Google image search and dropped them into Claude with this prompt:

Here are some photos of kakapo parrots just to remind you what they look like

I need you to make an animation in animated pixel art on HTML 5 canvas of obviously pixel art kakapo jumping up and down having a party with confetti and suchlike - there should be at least 20 of them

Here's the transcript, and this is the resulting page. It's pretty great!

I wanted to embed it in a Keynote presentation file, so I downloaded the HTML and told a local Claude Code session:

Make me a video of file:///Users/simon/Downloads/kakapo-party.html - you need to load it in a browser and click on it a few times to get the confetti effect, the video should be 15s long

don't start clicking until 3s in

make sure several clicks are spread around the clickable area

Claude Code used Playwright (transcript here) and produced this video, which was exactly what I needed for my final slide:

Here's the full Playwright script it used, which was pleasingly short:

# /// script
# dependencies = ["playwright"]
# ///
import time
from playwright.sync_api import sync_playwright
W, H = 1280, 720
# Canvas fills the viewport; spread clicks across corners, edges and centre
clicks = [
    (3.0, 640, 360),   # centre
    (4.2, 160, 120),   # top-left
    (5.4, 1120, 120),  # top-right
    (6.6, 180, 600),   # bottom-left
    (7.8, 1100, 600),  # bottom-right
    (9.0, 640, 100),   # top-centre
    (10.0, 380, 380),  # mid-left
    (11.0, 900, 380),  # mid-right
    (12.2, 640, 620),  # bottom-centre
    (13.2, 640, 300),  # finale centre
]
with sync_playwright() as p:
    b = p.chromium.launch()
    ctx = b.new_context(viewport={"width":W,"height":H}, record_video_dir="vids", record_video_size={"width":W,"height":H})
    page = ctx.new_page()
    t0 = time.time()
    page.goto("file:///Users/simon/Downloads/kakapo-party.html")
    for t,x,y in clicks:
        time.sleep(max(0, t-(time.time()-t0)))
        page.mouse.click(x,y)
    time.sleep(max(0, 16.0-(time.time()-t0)))
    ctx.close(); b.close()