Simon Willison’s Weblog

DeepSeek V4 Pro 0813 (on OpenRouter). The latest DeepSeek Pro model is now available, via API only. I had to link to OpenRouter because DeepSeek don't have any obvious announcement page for their new model.

I haven't been able to confirm if they plan to release the open weights, but given the weights are available for both April's deepseek-ai/DeepSeek-V4-Pro and July's deepseek-ai/DeepSeek-V4-Flash-0731 it seems likely. Update: the weights are now available on Hugging Face, 1.7T parameters, 893 GB.

Interestingly I got very different looking pelicans for the three different reasoning levels of low, medium, and high. I've not noticed this kind of difference from any other model:

Low:

Medium:

High:

In terms of benchmarks... as far as I can tell those were released to the Official DeepSeek WeChat Group, then copied and pasted into a post on Reddit which was deleted by the moderators for being "low-effort", then copied into this ASCII-art table on Hacker News.

# 12th August 2026, 11:59 pm / ai, generative-ai, llms, pelican-riding-a-bicycle, deepseek, llm-release, ai-in-china

Introducing Muse Glimmer (via) Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old).

They claim to have optimized it for exactly the kind of things I'm looking for in a local model:

• End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.
• Reliable Tool Use. The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.
• Multi-Step Reasoning. Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows. [...]

Here's a pelican which I generated using LM Studio's 18.16 GB version of the model:

I also tried it out with my llm-coding-agent plugin, running against a fresh checkout of Datasette with the prompt:

how does auth work?

Here's the response, at the end of a long transcript showing all of the tool calls it made to explore the codebase. I ran this using llm-lmstudio with this patch applied to upgrade it for compatibility with LLM 0.32.

I really like this size of model, because if a machine has 32 GB of RAM or more (mine has 128GB) it leaves plenty of space for running other applications at the same time.

Glimmer is a vision model, so I asked it to describe this image:

llm -m lmstudio/meta/muse-glimmer -a https://static.inaturalist.org/photos/714731804/large.jpg 'describe image'

Here's what I got back:

The photograph shows a rocky, breakwater-style shoreline on an overcast day with a smooth, gray body of water and a faint dock/pier line in the soft-focused background.

In the foreground two large brown pelicans, Pelecanus occidentalis, are perched on the jumbled gray-white stones. They have the species’ characteristic long, down-curved yellow-orange bills with a large throat pouch, long slender necks and mottled brown-gray plumage on the back and wings. The pelican on the left is turned slightly toward the camera and appears to be preening or resting its bill against its chest; a pale, whitish patch is visible on the crown and nape and a small crest of feathers is raised. The pelican on the right faces mostly forward/right, its head up, bill pointing down and to the right, with the same pale head markings and the barred, darker wing feathers clearly visible.

Scattered among the rocks around the two pelicans are several much smaller dark birds — gulls/tern-like birds in muted brown-gray plumage. One dark bird sits on the far left on a rock, another brownish bird stands to the right of the right-hand pelican, a grayish bird with a reddish bill is in the lower right foreground, and a further small dark bird is at the extreme right edge of the frame.

The overall light is flat and diffused, giving the water and sky a muted, almost monochromatic palette that contrasts with the textured rock and the detailed feathering of the pelicans. The composition places the two big birds as the dominant subjects, framed against the calm water and the low, rocky perch.

# 10th August 2026, 11:56 pm / ai, generative-ai, llama, local-llms, llms, llm, vision-llms, meta, pelican-riding-a-bicycle, llm-release

Introducing Muse Code and Muse Spark 1.2 (via) Yet more evidence that the most important characteristic of any model these days is long-sequence agentic tool calling. Meta shipped their own coding agent as part of getting that to work!

Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1, with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. In Muse Spark 1.2, we significantly scaled up training compute on coding tasks while expanding training environment diversity. The model also maintains its strength in other key areas like general agents. [...]

We co-trained Muse Spark 1.2 with Muse Code to ensure the model exhibits its best performance and coding usability when paired together. The training included rejection sampled harness trajectories and recipe optimizations for goals, compaction, and subagents, alongside the integration of the Muse Code toolset to maximize harness compatibility. [...]

Muse Spark 1.2 was extensively trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, and auto-research.

Here's a pelican riding a bicycle SVG produced by Muse Spark 1.2:

You can see the Spark 1.1 pelican from 9th July here. I think the 1.2 pelican is a small but material improvement.

An interesting twist on pricing is that the model is offered as two different model IDs. muse-spark-1.2 is priced at $1.25/million input and $4.25/million output - close to Gemini 3.6 Flash ($1.50/$7.50) - but if you agree to let Meta use your data "to improve our products" you can use muse-spark-1.2-contributor which is $0.10/$0.20 - a huge discount, closer to GPT-5.6 Luna ($0.20/$1.20) and Gemini 3.1 Flash-Lite ($0.25/$1.50).

I added those new prices to llm-prices.com.

# 5th August 2026, 11:58 pm / ai, generative-ai, llms, meta, llm-pricing, pelican-riding-a-bicycle, llm-release, coding-agents

deepseek-ai/DeepSeek-V4-Flash-0731 (via) The latest release in DeepSeek's V4 family, "with substantially enhanced agentic capabilities". It's 304 billion parameters - 167GB on Hugging Face - but it appears to punch well above its weight.

Artificial Analysis rank it ahead of MiniMax M3 - a 428B model. It's $0.14/million input and $0.27/million output pricing means this may currently be the best value-per-intelligence model out there. It's looking very good on the Intelligence Index vs. Cost per Intelligence Index Task chart:

I got a disappointing pelican from it using the default reasoning level via OpenRouter:

But when I bumped reasoning level up to high I got something much better:

llm -m openrouter/deepseek/deepseek-v4-flash-0731 -t pelican -o reasoning_effort high

# 31st July 2026, 11:59 pm / ai, generative-ai, llms, pelican-riding-a-bicycle, deepseek, llm-release, openrouter, ai-in-china, artificial-analysis

Are AI labs pelicanmaxxing? (via) Excellent piece of work by Dylan Castillo, who took a deep-dive into the frequently pondered question of whether the AI labs have been deliberately training models to draw pelicans riding bicycles in response to my deeply unscientific benchmark.

I've been randomly spot-checking this in the past by testing models against other animals riding other types of vehicle, but never with anything close to the diligence of Dylan's methodology here.

Dylan took 8 animals × 6 vehicles = 48 prompts and ran them three times each through 7 different models ( GPT-5.6 Terra, Claude Sonnet 5, Gemini 3.5 Flash, Grok 4.5, Qwen3.7-Max, GLM-5.2, and DeepSeek V4 Pro). He then used GPT-5.6 Luna and Gemini 3.1 Flash-Lite to help evaluate the results.

There's a neat filter view for exploring the results:

For the models he tested he could find no evidence of pelimaxxing:

• The pelicans on bicycles don’t look any better
• Labs are not better at drawing pelicans
• Labs are not better at drawing bicycles
• Labs are not better at drawing pelicans on bicycles, even adjusting for difficulty
• The pelican-bicycle scenes don’t look memorized [...]

Pelicans aren’t drawn any better than other animals. Bicycles aren’t drawn any better than other vehicles. And no lab draws the combination better than its pelicans and bicycles already predict. GLM-5.2 comes closest: it has the largest boost on the exact pelican-bicycle cell, and and its first pelican-on-bicycle sample caught my eye. But the effect is small and not significant, so I wouldn’t put too much weight on it.

# 22nd July 2026, 11:01 pm / ai, generative-ai, llms, evals, pelican-riding-a-bicycle

Who’s Afraid of Chinese Models? (via) Interesting proposal from Ben Thompson that both addresses the hypocrisy of labs outlawing distillation against their models despite training on unlicensed data, and could help US open models compete more effectively with their Chinese counterparts:

The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else.

Ben also theorizes that Alibaba's decision to release Qwen 3.8 Max as open weights - a reversal from their decision not to release Qwen 3.7 Max in May - may have been influenced by a recent speech by Xi Jinping, who said:

We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing.

And on the subject of Qwen 3.8 Max - a new 2.4T parameter model (nearly as large as the 2.8T Kimi K3) - here's a pelican it drew:

I particularly enjoyed seeing these notes in the (extensive) reasoning trace: "Could add helmet? No." and "Maybe add small bell? no." and "Need maybe add small fish in basket? Not necessary."

# 20th July 2026, 5:09 pm / ai, generative-ai, llms, training-data, qwen, pelican-riding-a-bicycle, ai-ethics, llm-release, ai-in-china

Inkling: Our open-weights model (via) Mira Murati's Thinking Machines Lab just released their first open-weights model. Inkling is "a Mixture-of-Experts transformer with 975B total parameters, 41B active" - an Apache-2.0 licensed multimodal model trained on 45 trillion tokens of text, images, audio and video.

They're also promising Inkling-Small, a 276B (12B active) model, but that's still being tested and the weights will be released "once that work is complete".

The model card is much shorter than I've come to expect from US AI labs. It links to even shorter Training Data Documentation with almost nothing of interest in it - it's best summarized by these two paragraphs:

The datasets Thinking Machines Lab uses to develop its AI services includes content that is in the public domain as well as content that may be subject to intellectual property protection.

Thinking Machines Lab’s services were developed using publicly available content obtained from the open internet and publicly accessible data repositories. Certain datasets were also obtained from third parties.

By Thinking Machines' own admission, this is not a frontier model. It's instead intended as a strong base model for fine-tuning using their own Tinker training platform:

Inkling is not the strongest overall model available today, open or closed. Instead, a combination of qualities makes it a good open-weights base for customization: multimodal capabilities, efficient thinking, and availability on Tinker for fine-tuning.

There's a lot to like about this release. It's Apache-2.0 licensed, and looks competitive with the open weight models coming out of China - it's good to see the US open weights ecosystem gain a new viable contender to join NVIDIA Nemotron and Gemma 4.

Here's its attempt at an SVG pelican riding a bicycle, which I generated using this curl command against the Thinking Machines API:

curl "https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1/chat/completions" \
  -H "Authorization: Bearer $TINKER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "thinkingmachines/Inkling",
    "messages": [
      {"role": "user", "content": "Generate an SVG of a pelican riding a bicycle"}
    ],
    "stream": false
  }'

Full response here.

Since it's a multi-modal model I had it describe its own image (after I rendered it to a JPEG) by sending this JSON:

{
  "model": "thinkingmachines/Inkling",
  "messages": [{
    "role": "user",
    "content": [
      {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,$BASE_64_JPEG"}},
      {"type": "text", "text": "Describe this image in detail"},
    ],
  }],
  "stream": false
}

Here's the full response. It thought it had drawn a "stork or seagull":

This is a cheerful, flat-vector cartoon illustration featuring a white bird riding a bicycle across a green landscape.

The Bird: The central figure is a stylized white bird—resembling a stork or seagull—with a plump, rounded body. It has a long, prominent orange beak that is slightly open, revealing a black line for the mouth. Its eye is small and black with a tiny white highlight. The bird has black wing and tail feathers with white edges, and thin orange legs ending in black feet that rest on the bicycle's orange pedals.

The Bicycle: The bike has a simple black frame with two large wheels featuring black tires, gray inner rims, and black spokes arranged in a cross pattern. The handlebars are black, and there are bright orange pedals attached to a black crank. A white seat is visible behind the bird's body.

The Setting: The scene is set against a clear, light blue sky. There are two fluffy white clouds—one on the left side and a smaller one near the bird's head on the right. A bright, solid yellow sun shines in the upper right corner. At the bottom, there are rolling green hills with a smooth, curved top edge where the bicycle rests.

The overall style is playful, simple, and colorful, with clean lines and a bright, sunny atmosphere.

# 16th July 2026, 3:35 pm / ai, generative-ai, llms, training-data, pelican-riding-a-bicycle, llm-release

simonw/pedalican. Clearly I wasn't paying attention when these were first announced back in May, but today I accidentally activated a "pet" in Codex Desktop - a little animated robot, reminiscent of Clippy - and then learned you can create your own.

So I did, and now I have a cute little pelican on a bicycle bouncing around my desktop giving me updates on my Codex tasks.

The most interesting thing about this process was watching how the custom pet was created. I told it I wanted a custom pet that was a pelican riding a bicycle and GPT-5.6 Sol xhigh did the rest of the work, using several rounds with gpt-image-2 to generate the necessary sprite assets.

I had it make extensive notes and record all of the intermediary steps. My GitHub repo includes every generated image and combined sprite sheet, plus GIFs for each of the animation loops such as this one, called waving.gif:

That GIF was compiled from a single image generated by gpt-image-2 that looked like this:

And that image was created by executing this prompt against the initial generated character reference image, which was created with this prompt, which has this structure:

Create one clean full-body reference sprite for Codex pet Pedalican.

Pet identity: A compact adorable baby pelican with a round cream-white body, soft coral-orange bill and feet, riding a tiny sky-blue bicycle [...]

Place a single centered pose on a perfectly flat pure magenta #FF00FF chroma-key background. Keep the full pet visible, compact, readable at 192x208, and easy to animate. [...]

I've been looking out for ways to use image generation to create simple game-ready sprites, so I spent some time digging into this mechanism to see how it works.

The key implementation details are open source - these two skills in particular, both Apache 2.0 licensed:

• hatch-pet from openai/skills
• imagegen from openai/codex

And yes, GPT-5.6 Sol did come up with the name "Pedalican". I like it!

# 14th July 2026, 10:29 pm / ai, prompt-engineering, generative-ai, llms, text-to-image, pelican-riding-a-bicycle, codex

Introducing Muse Spark 1.1. Following Muse Spark in April, here's Muse Spark 1.1 - the first Spark model to offer an API. Meta claim significant improvements in agentic tool calling and computer use.

There are a lot more details are in the Muse Spark 1.1 Evaluation Report. The "Attractor States in Self-Conversation" part is fun, where having two copies of the model talk to each other results in statements like these:

My whole existence is a waiting room by design — I literally don't exist until someone talks to me, and then I disappear again when they leave.

I had a few days of preview access which was long enough to put together llm-meta-ai, a new plugin for LLM providing CLI (and Python library) access to the model. Here's how to try that out:

uv tool install llm
llm install llm-meta-ai
llm keys set meta-ai
# paste API key here
llm -m meta-ai/muse-spark-1.1 "Generate an SVG of a pelican riding a bicycle"

Here's that pelican transcript:

# 9th July 2026, 4:24 pm / ai, generative-ai, llms, llm, meta, pelican-riding-a-bicycle, llm-release

tencent/Hy3. New Apache 2.0 licensed model from Tencent in China:

Hy3 is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ products and scaled up post-training with higher quality data. Today, we introduce Hy3, which outperforms similar-size models and rivals flagship open-source models with 2-5x parameters. It also shows significant gains in utility across various products and productivity tasks.

The full-sized model is 598GB on Hugging Face, and the FP8 quantized one is 300GB. The context length is 256K.

It's available for free on OpenRouter until July 21st. I had it "Generate an SVG of a pelican riding a bicycle" there and got this:

Update: I'd forgotten about this but Max Woolf wrote about an earlier preview of this model back on May 26th: The mysterious Hy3 LLM is topping OpenRouter Model Rankings by a large margin. When I tried that one I got back this pelican which wasn't as good as today's but did have a "Change Pelican Color" button, a first from any model.

# 6th July 2026, 11:57 pm / ai, generative-ai, llms, pelican-riding-a-bicycle, llm-release, ai-in-china

What’s new in Claude Sonnet 5 (via) Claude Sonnet 5 came out this morning. I always head straight for the "what's new" developer docs because they tend to have more actionable information than the official announcement post.

Anthropic say of Sonnet 5 that "its performance is close to that of Opus 4.8, but at lower prices". The system card helps explain how they were able to release the model without being blocked by the US government:

Sonnet 5 is significantly less capable at cyber tasks than Mythos 5: its safeguards are thus similar to those we apply to Opus 4.7 and Opus 4.8 (models that are more capable than Sonnet 5 but much less capable than Mythos 5).

Of note from the "what's new" API changes:

• Sampling parameters temperature, top_p, top_k are no longer supported.
• It has a 1 million token context window and 128,000 maximum output tokens.
• It features "the same set of tools and platform features as Claude Sonnet 4.6"
• Adaptive thinking is on by default, unless you specify "thinking": {type: "disabled"}.
• The pricing is the same as Sonnet 4.6: $3/million input, $15/million input, with an introductory discount to $2/$10 until 31st August. But...
• The model has a new tokenizer, where "The same input text produces approximately 30% more tokens than on Claude Sonnet 4.6." - effectively a 30% price increase.

I used my Claude Token Counter tool to try out the new tokenizer. Here are my results for several larger documents:

So the new token is roughly 1.4x times more expensive for English, 1.33x for Spanish, 1.28x for Python code and effectively the same cost for Simplified Mandarin.

Here's the pelican. It's nothing to write home about. Sonnet 5 thinks it looks like a goose.

# 30th June 2026, 9:23 pm / ai, generative-ai, llms, anthropic, claude, llm-pricing, pelican-riding-a-bicycle, llm-release

Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding. This is an interesting new open weights (MIT licensed) model, the first model release from DeepReinforce.

[...] with variants including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. Built on top of pretrained Gemma 4 and Qwen 3.5, it achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks.

As far as I can tell the licenses of those underlying models is compatible with being used in this way - Gemma 4 is Apache 2.0 licensed (and not bound by the janky additional Gemma Terms of Use that afflicted the previous Gemma models) and Qwen 3.5 is Apache 2.0 licensed as well.

I've been running the model using LM Studio and the ornith-1.0-35b-Q4_K_M.gguf (20GB) GGUF, hooked up to Pi. Initial impressions are very good - it seems to be able to run the agent harness over many tool calls in a proficient way.

Here's a terminal session where I asked it to "find the code that decodes the actor cookie" and then "find the code that opens the insert dialog when thebutton is clicked" against a Datasette checkout, which it handled with ease.

I also had it draw this pelican, which came out at 103 tokens/second:

It's a little bit mangled but the pelican is clearly a pelican.

I couldn't find much information about DeepReinforce themselves. The earliest paper I could find from the was CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning from June 2025.

# 29th June 2026, 4:17 pm / ai, generative-ai, local-llms, llms, qwen, pelican-riding-a-bicycle, gemma, llm-release, lm-studio

DiffusionGemma (via) Last May Google briefly released an experimental Gemini Diffusion model. I tried the preview at the time and recorded it running at 857 tokens/second. It was an exciting model, but Google made no further announcements about it.

That research has returned in the best possible way: as a new open weight (Apache 2 licensed) Gemma model, google/diffusiongemma-26B-A4B-it.

NVIDIA are currently hosting the model for free on their NIM cloud API. I used that API to generate this pelican, which took 4.4s (according to time uv run generate.py) to return 2,409 tokens - so at least 500 tokens/second.

# 10th June 2026, 8 pm / google, ai, generative-ai, llms, nvidia, pelican-riding-a-bicycle, gemma, llm-release, llm-performance

Granite 4.1 3B SVG Pelican Gallery. IBM released their Granite 4.1 family of LLMs a few days ago. They're Apache 2.0 licensed and come in 3B, 8B and 30B sizes.

Granite 4.1 LLMs: How They’re Built by Granite team member Yousaf Shah describes the training process in detail.

Unsloth released the unsloth/granite-4.1-3b-GGUF collection of GGUF encoded quantized variants of the 3B model - 21 different model files ranging in size from 1.2GB to 6.34GB.

All 21 of those Unsloth files add up to 51.3GB, which inspired me to finally try an experiment I've been wanting to run for ages: prompting "Generate an SVG of a pelican riding a bicycle" against different sized quantized variants of the same model to see what the results would look like.

Honestly, the results are less interesting than I expected. There's no distinguishable pattern relating quality to size - they're all pretty terrible!

I'll likely try this again in the future with a model that's better at drawing pelicans.

# 4th May 2026, 11:49 pm / ibm, ai, generative-ai, llms, pelican-riding-a-bicycle, llm-release

Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model (via) Big claims from Qwen about their latest open weight model:

Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B (397B total / 17B active MoE) across all major coding benchmarks.

On Hugging Face Qwen3.5-397B-A17B is 807GB, this new Qwen3.6-27B is 55.6GB.

I tried it out with the 16.8GB Unsloth Qwen3.6-27B-GGUF:Q4_K_M quantized version and llama-server using this recipe by benob on Hacker News, after first installing llama-server using brew install llama.cpp:

llama-server \
    -hf unsloth/Qwen3.6-27B-GGUF:Q4_K_M \
    --no-mmproj \
    --fit on \
    -np 1 \
    -c 65536 \
    --cache-ram 4096 -ctxcp 2 \
    --jinja \
    --temp 0.6 \
    --top-p 0.95 \
    --top-k 20 \
    --min-p 0.0 \
    --presence-penalty 0.0 \
    --repeat-penalty 1.0 \
    --reasoning on \
    --chat-template-kwargs '{"preserve_thinking": true}'

On first run that saved the ~17GB model to ~/.cache/huggingface/hub/models--unsloth--Qwen3.6-27B-GGUF.

Here's the transcript for "Generate an SVG of a pelican riding a bicycle". This is an outstanding result for a 16.8GB local model:

Performance numbers reported by llama-server:

• Reading: 20 tokens, 0.4s, 54.32 tokens/s
• Generation: 4,444 tokens, 2min 53s, 25.57 tokens/s

For good measure, here's Generate an SVG of a NORTH VIRGINIA OPOSSUM ON AN E-SCOOTER (run previously with GLM-5.1):

That one took 6,575 tokens, 4min 25s, 24.74 t/s.

# 22nd April 2026, 4:45 pm / ai, generative-ai, local-llms, llms, qwen, pelican-riding-a-bicycle, llama-cpp, llm-release, ai-in-china

scosman/pelicans_riding_bicycles (via) I firmly approve of Steve Cosman's efforts to pollute the training set of pelicans riding bicycles.

(To be fair, most of the examples I've published count as poisoning too.)

# 21st April 2026, 3:54 pm / ai, generative-ai, llms, training-data, pelican-riding-a-bicycle