Simon Willison’s Weblog

Improved Gemini 2.5 Flash and Flash-Lite (via) Two new preview models from Google - updates to their fast and inexpensive Flash and Flash Lite families:

The latest version of Gemini 2.5 Flash-Lite was trained and built based on three key themes:

• Better instruction following: The model is significantly better at following complex instructions and system prompts.
• Reduced verbosity: It now produces more concise answers, a key factor in reducing token costs and latency for high-throughput applications (see charts above).
• Stronger multimodal & translation capabilities: This update features more accurate audio transcription, better image understanding, and improved translation quality.

[...]

This latest 2.5 Flash model comes with improvements in two key areas we heard consistent feedback on:

• Better agentic tool use: We've improved how the model uses tools, leading to better performance in more complex, agentic and multi-step applications. This model shows noticeable improvements on key agentic benchmarks, including a 5% gain on SWE-Bench Verified, compared to our last release (48.9% → 54%).
• More efficient: With thinking on, the model is now significantly more cost-efficient—achieving higher quality outputs while using fewer tokens, reducing latency and cost (see charts above).

They also added two new convenience model IDs: gemini-flash-latest and gemini-flash-lite-latest, which will always resolve to the most recent model in that family.

I released llm-gemini 0.26 adding support for the new models and new aliases. I also used the response.set_resolved_model() method added in LLM 0.27 to ensure that the correct model ID would be recorded for those -latest uses.

llm install -U llm-gemini

Both of these models support optional reasoning tokens. I had them draw me pelicans riding bicycles in both thinking and non-thinking mode, using commands that looked like this:

llm -m gemini-2.5-flash-preview-09-2025 -o thinking_budget 4000 "Generate an SVG of a pelican riding a bicycle"

I then got each model to describe the image it had drawn using commands like this:

llm -a https://static.simonwillison.net/static/2025/gemini-2.5-flash-preview-09-2025-thinking.png -m gemini-2.5-flash-preview-09-2025 -o thinking_budget 2000 'Detailed single line alt text for this image'

gemini-2.5-flash-preview-09-2025-thinking

A minimalist stick figure graphic depicts a person with a white oval body and a dot head cycling a gray bicycle, carrying a large, bright yellow rectangular box resting high on their back.

gemini-2.5-flash-preview-09-2025

A simple cartoon drawing of a pelican riding a bicycle, with the text "A Pelican Riding a Bicycle" above it.

gemini-2.5-flash-lite-preview-09-2025-thinking

A quirky, simplified cartoon illustration of a white bird with a round body, black eye, and bright yellow beak, sitting astride a dark gray, two-wheeled vehicle with its peach-colored feet dangling below.

gemini-2.5-flash-lite-preview-09-2025

A minimalist, side-profile illustration of a stylized yellow chick or bird character riding a dark-wheeled vehicle on a green strip against a white background.

# 25th September 2025, 7:27 pm / llm-release, gemini, llm, google, llm-reasoning, pelican-riding-a-bicycle, llms

GPT-5-Codex. OpenAI half-released this model earlier this month, adding it to their Codex CLI tool but not their API.

Today they've fixed that - the new model can now be accessed as gpt-5-codex. It's priced the same as regular GPT-5: $1.25/million input tokens, $10/million output tokens, and the same hefty 90% discount for previously cached input tokens, especially important for agentic tool-using workflows which quickly produce a lengthy conversation.

It's only available via their Responses API, which means you currently need to install the llm-openai-plugin to use it with LLM:

llm install -U llm-openai-plugin
llm -m openai/gpt-5-codex -T llm_version 'What is the LLM version?'

Outputs:

The installed LLM version is 0.27.1.

I added tool support to that plugin today, mostly authored by GPT-5 Codex itself using OpenAI's Codex CLI.

The new prompting guide for GPT-5-Codex is worth a read.

GPT-5-Codex is purpose-built for Codex CLI, the Codex IDE extension, the Codex cloud environment, and working in GitHub, and also supports versatile tool use. We recommend using GPT-5-Codex only for agentic and interactive coding use cases.

Because the model is trained specifically for coding, many best practices you once had to prompt into general purpose models are built in, and over prompting can reduce quality.

The core prompting principle for GPT-5-Codex is “less is more.”

I tried my pelican benchmark at a cost of 2.156 cents.

llm -m openai/gpt-5-codex "Generate an SVG of a pelican riding a bicycle"

I asked Codex to describe this image and it correctly identified it as a pelican!

llm -m openai/gpt-5-codex -a https://static.simonwillison.net/static/2025/gpt-5-codex-api-pelican.png \
  -s 'Write very detailed alt text'

Cartoon illustration of a cream-colored pelican with a large orange beak and tiny black eye riding a minimalist dark-blue bicycle. The bird’s wings are tucked in, its legs resemble orange stick limbs pushing the pedals, and its tail feathers trail behind with light blue motion streaks to suggest speed. A small coral-red tongue sticks out of the pelican’s beak. The bicycle has thin light gray spokes, and the background is a simple pale blue gradient with faint curved lines hinting at ground and sky.

# 23rd September 2025, 11:59 pm / openai, llm-reasoning, ai, llms, llm-release, gpt-5, prompt-engineering, ai-assisted-programming, generative-ai, pelican-riding-a-bicycle, codex-cli

Qwen3-VL: Sharper Vision, Deeper Thought, Broader Action (via) I've been looking forward to this. Qwen 2.5 VL is one of the best available open weight vision LLMs, so I had high hopes for Qwen 3's vision models.

Firstly, we are open-sourcing the flagship model of this series: Qwen3-VL-235B-A22B, available in both Instruct and Thinking versions. The Instruct version matches or even exceeds Gemini 2.5 Pro in major visual perception benchmarks. The Thinking version achieves state-of-the-art results across many multimodal reasoning benchmarks.

Bold claims against Gemini 2.5 Pro, which are supported by a flurry of self-reported benchmarks.

This initial model is enormous. On Hugging Face both Qwen3-VL-235B-A22B-Instruct and Qwen3-VL-235B-A22B-Thinking are 235B parameters and weigh 471 GB. Not something I'm going to be able to run on my 64GB Mac!

The Qwen 2.5 VL family included models at 72B, 32B, 7B and 3B sizes. Given the rate Qwen are shipping models at the moment I wouldn't be surprised to see smaller Qwen 3 VL models show up in just the next few days.

Also from Qwen today, three new API-only closed-weight models: upgraded Qwen 3 Coder, Qwen3-LiveTranslate-Flash (real-time multimodal interpretation), and Qwen3-Max, their new trillion parameter flagship model, which they describe as their "largest and most capable model to date".

Plus Qwen3Guard, a "safety moderation model series" that looks similar in purpose to Meta's Llama Guard. This one is open weights (Apache 2.0) and comes in 8B, 4B and 0.6B sizes on Hugging Face. There's more information in the QwenLM/Qwen3Guard GitHub repo.

# 23rd September 2025, 11:51 pm / vision-llms, llm-reasoning, ai, qwen, llms, ai-in-china, llm-release, generative-ai

Grok 4 Fast. New hosted vision-enabled reasoning model from xAI that's designed to be fast and extremely competitive on price. It has a 2 million token context window and "was trained end-to-end with tool-use reinforcement learning".

It's priced at $0.20/million input tokens and $0.50/million output tokens - 15x less than Grok 4 (which is $3/million input and $15/million output). That puts it cheaper than GPT-5 mini and Gemini 2.5 Flash on llm-prices.com.

The same model weights handle reasoning and non-reasoning based on a parameter passed to the model.

I've been trying it out via my updated llm-openrouter plugin, since Grok 4 Fast is available for free on OpenRouter for a limited period.

Here's output from the non-reasoning model. This actually output an invalid SVG - I had to make a tiny manual tweak to the XML to get it to render.

llm -m openrouter/x-ai/grok-4-fast:free "Generate an SVG of a pelican riding a bicycle" -o reasoning_enabled false

(I initially ran this without that -o reasoning_enabled false flag, but then I saw that OpenRouter enable reasoning by default for that model. Here's my previous invalid result.)

And the reasoning model:

llm -m openrouter/x-ai/grok-4-fast:free "Generate an SVG of a pelican riding a bicycle" -o reasoning_enabled true

In related news, the New York Times had a story a couple of days ago about Elon's recent focus on xAI: Since Leaving Washington, Elon Musk Has Been All In on His A.I. Company.

# 20th September 2025, 11:59 pm / llm-release, grok, generative-ai, llm-reasoning, ai, llms, pelican-riding-a-bicycle, openrouter, llm, llm-pricing, vision-llms, xai

GPT‑5-Codex and upgrades to Codex. OpenAI half-released a new model today: GPT‑5-Codex, a fine-tuned GPT-5 variant explicitly designed for their various AI-assisted programming tools.

Update: OpenAI call it a "version of GPT-5", they don't explicitly describe it as a fine-tuned model. Calling it a fine-tune was my mistake here.

I say half-released because it's not yet available via their API, but they "plan to make GPT‑5-Codex available in the API soon".

I wrote about the confusing array of OpenAI products that share the name Codex a few months ago. This new model adds yet another, though at least "GPT-5-Codex" (using two hyphens) is unambiguous enough not to add to much more to the confusion.

At this point it's best to think of Codex as OpenAI's brand name for their coding family of models and tools.

The new model is already integrated into their VS Code extension, the Codex CLI and their Codex Cloud asynchronous coding agent. I'd been calling that last one "Codex Web" but I think Codex Cloud is a better name since it can also be accessed directly from their iPhone app.

Codex Cloud also has a new feature: you can configure it to automatically run code review against specific GitHub repositories (I found that option on chatgpt.com/codex/settings/code-review) and it will create a temporary container to use as part of those reviews. Here's the relevant documentation.

Some documented features of the new GPT-5-Codex model:

• Specifically trained for code review, which directly supports their new code review feature.
• "GPT‑5-Codex adapts how much time it spends thinking more dynamically based on the complexity of the task." Simple tasks (like "list files in this directory") should run faster. Large, complex tasks should use run for much longer - OpenAI report Codex crunching for seven hours in some cases!
• Increased score on their proprietary "code refactoring evaluation" from 33.9% for GPT-5 (high) to 51.3% for GPT-5-Codex (high). It's hard to evaluate this without seeing the details of the eval but it does at least illustrate that refactoring performance is something they've focused on here.
• "GPT‑5-Codex also shows significant improvements in human preference evaluations when creating mobile websites" - in the past I've habitually prompted models to "make it mobile-friendly", maybe I don't need to do that any more.
• "We find that comments by GPT‑5-Codex are less likely to be incorrect or unimportant" - I originally misinterpreted this as referring to comments in code but it's actually about comments left on code reviews.

The system prompt for GPT-5-Codex in Codex CLI is worth a read. It's notably shorter than the system prompt for other models - here's a diff.

Here's the section of the updated system prompt that talks about comments:

Add succinct code comments that explain what is going on if code is not self-explanatory. You should not add comments like "Assigns the value to the variable", but a brief comment might be useful ahead of a complex code block that the user would otherwise have to spend time parsing out. Usage of these comments should be rare.

Theo Browne has a video review of the model and accompanying features. He was generally impressed but noted that it was surprisingly bad at using the Codex CLI search tool to navigate code. Hopefully that's something that can fix with a system prompt update.

Finally, can it drew a pelican riding a bicycle? Without API access I instead got Codex Cloud to have a go by prompting:

Generate an SVG of a pelican riding a bicycle, save as pelican.svg

Here's the result:

# 15th September 2025, 6:55 pm / openai, pelican-riding-a-bicycle, ai, llms, llm-release, gpt-5, codex-cli, ai-assisted-programming, generative-ai, coding-agents

Qwen3-Next-80B-A3B. Qwen announced two new models via their Twitter account (and here's their blog): Qwen3-Next-80B-A3B-Instruct and Qwen3-Next-80B-A3B-Thinking.

They make some big claims on performance:

• Qwen3-Next-80B-A3B-Instruct approaches our 235B flagship.
• Qwen3-Next-80B-A3B-Thinking outperforms Gemini-2.5-Flash-Thinking.

The name "80B-A3B" indicates 80 billion parameters of which only 3 billion are active at a time. You still need to have enough GPU-accessible RAM to hold all 80 billion in memory at once but only 3 billion will be used for each round of inference, which provides a significant speedup in responding to prompts.

More details from their tweet:

• 80B params, but only 3B activated per token → 10x cheaper training, 10x faster inference than Qwen3-32B.(esp. @ 32K+ context!)
• Hybrid Architecture: Gated DeltaNet + Gated Attention → best of speed & recall
• Ultra-sparse MoE: 512 experts, 10 routed + 1 shared
• Multi-Token Prediction → turbo-charged speculative decoding
• Beats Qwen3-32B in perf, rivals Qwen3-235B in reasoning & long-context

The models on Hugging Face are around 150GB each so I decided to try them out via OpenRouter rather than on my own laptop (Thinking, Instruct).

I'm used my llm-openrouter plugin. I installed it like this:

llm install llm-openrouter
llm keys set openrouter
# paste key here

Then found the model IDs with this command:

llm models -q next

Which output:

OpenRouter: openrouter/qwen/qwen3-next-80b-a3b-thinking
OpenRouter: openrouter/qwen/qwen3-next-80b-a3b-instruct

I have an LLM prompt template saved called pelican-svg which I created like this:

llm "Generate an SVG of a pelican riding a bicycle" --save pelican-svg

This means I can run my pelican benchmark like this:

llm -t pelican-svg -m openrouter/qwen/qwen3-next-80b-a3b-thinking

Or like this:

llm -t pelican-svg -m openrouter/qwen/qwen3-next-80b-a3b-instruct

Here's the thinking model output (exported with llm logs -c | pbcopy after I ran the prompt):

I enjoyed the "Whimsical style with smooth curves and friendly proportions (no anatomical accuracy needed for bicycle riding!)" note in the transcript.

The instruct (non-reasoning) model gave me this:

"🐧🦩 Who needs legs!?" indeed! I like that penguin-flamingo emoji sequence it's decided on for pelicans.

# 12th September 2025, 4:07 am / llm, llm-reasoning, pelican-riding-a-bicycle, ai, qwen, llms, ai-in-china, llm-release, generative-ai, openrouter

Kimi-K2-Instruct-0905. New not-quite-MIT licensed model from Chinese Moonshot AI, a follow-up to the highly regarded Kimi-K2 model they released in July.

This one is an incremental improvement - I've seen it referred to online as "Kimi K-2.1". It scores a little higher on a bunch of popular coding benchmarks, reflecting Moonshot's claim that it "demonstrates significant improvements in performance on public benchmarks and real-world coding agent tasks".

More importantly the context window size has been increased from 128,000 to 256,000 tokens.

Like its predecessor this is a big model - 1 trillion parameters in a mixture-of-experts configuration with 384 experts, 32B activated parameters and 8 selected experts per token.

I used Groq's playground tool to try "Generate an SVG of a pelican riding a bicycle" and got this result, at a very healthy 445 tokens/second taking just under 2 seconds total:

# 6th September 2025, 4:59 pm / llm-release, generative-ai, groq, pelican-riding-a-bicycle, ai, ai-in-china, llms, moonshot

Introducing gpt-realtime. Released a few days ago (August 28th), gpt-realtime is OpenAI's new "most advanced speech-to-speech model". It looks like this is a replacement for the older gpt-4o-realtime-preview model that was released last October.

This is a slightly confusing release. The previous realtime model was clearly described as a variant of GPT-4o, sharing the same October 2023 training cut-off date as that model.

I had expected that gpt-realtime might be a GPT-5 relative, but its training date is still October 2023 whereas GPT-5 is September 2024.

gpt-realtime also shares the relatively low 32,000 context token and 4,096 maximum output token limits of gpt-4o-realtime-preview.

The only reference I found to GPT-5 in the documentation for the new model was a note saying "Ambiguity and conflicting instructions degrade performance, similar to GPT-5."

The usage tips for gpt-realtime have a few surprises:

Iterate relentlessly. Small wording changes can make or break behavior.

Example: Swapping “inaudible” → “unintelligible” improved noisy input handling. [...]

Convert non-text rules to text: The model responds better to clearly written text.

Example: Instead of writing, "IF x > 3 THEN ESCALATE", write, "IF MORE THAN THREE FAILURES THEN ESCALATE."

There are a whole lot more prompting tips in the new Realtime Prompting Guide.

OpenAI list several key improvements to gpt-realtime including the ability to configure it with a list of MCP servers, "better instruction following" and the ability to send it images.

My biggest confusion came from the pricing page, which lists separate pricing for using the Realtime API with gpt-realtime and GPT-4o mini. This suggests to me that the old gpt-4o-mini-realtime-preview model is still available, despite it no longer being listed on the OpenAI models page.

gpt-4o-mini-realtime-preview is a lot cheaper:

The mini model also has a much longer 128,000 token context window.

Update: Turns out that was a mistake in the documentation, that mini model has a 16,000 token context size.

Update 2: OpenAI's Peter Bakkum clarifies:

There are different voice models in API and ChatGPT, but they share some recent improvements. The voices are also different.

gpt-realtime has a mix of data specific enough to itself that its not really 4o or 5

# 1st September 2025, 5:34 pm / audio, openai, llm-pricing, ai, multi-modal-output, llms, realtime, llm-release, generative-ai

DeepSeek 3.1. The latest model from DeepSeek, a 685B monster (like DeepSeek v3 before it) but this time it's a hybrid reasoning model.

DeepSeek claim:

DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.

Drew Breunig points out that their benchmarks show "the same scores with 25-50% fewer tokens" - at least across AIME 2025 and GPQA Diamond and LiveCodeBench.

The DeepSeek release includes prompt examples for a coding agent, a python agent and a search agent - yet more evidence that the leading AI labs have settled on those as the three most important agentic patterns for their models to support.

Here's the pelican riding a bicycle it drew me (transcript), which I ran from my phone using OpenRouter chat.

# 22nd August 2025, 10:07 pm / llm-reasoning, pelican-riding-a-bicycle, deepseek, ai, ai-in-china, llms, llm-release, generative-ai, openrouter, coding-agents, prompt-engineering, drew-breunig

Introducing Gemma 3 270M: The compact model for hyper-efficient AI (via) New from Google:

Gemma 3 270M, a compact, 270-million parameter model designed from the ground up for task-specific fine-tuning with strong instruction-following and text structuring capabilities already trained in.

This model is tiny. The version I tried was the LM Studio GGUF one, a 241MB download.

It works! You can say "hi" to it and ask it very basic questions like "What is the capital of France".

I tried "Generate an SVG of a pelican riding a bicycle" about a dozen times and didn't once get back an SVG that was more than just a blank square... but at one point it did decide to write me this poem instead, which was nice:

+-----------------------+
|   Pelican Riding Bike |
+-----------------------+
|  This is the cat!  |
|  He's got big wings and a happy tail.  |
|  He loves to ride his bike!  |
+-----------------------+
|   Bike lights are shining bright.  |
|   He's got a shiny top, too!  |
|   He's ready for adventure!  |
+-----------------------+

That's not really the point though. The Gemma 3 team make it very clear that the goal of this model is to support fine-tuning: a model this tiny is never going to be useful for general purpose LLM tasks, but given the right fine-tuning data it should be able to specialize for all sorts of things:

In engineering, success is defined by efficiency, not just raw power. You wouldn't use a sledgehammer to hang a picture frame. The same principle applies to building with AI.

Gemma 3 270M embodies this "right tool for the job" philosophy. It's a high-quality foundation model that follows instructions well out of the box, and its true power is unlocked through fine-tuning. Once specialized, it can execute tasks like text classification and data extraction with remarkable accuracy, speed, and cost-effectiveness. By starting with a compact, capable model, you can build production systems that are lean, fast, and dramatically cheaper to operate.

Here's their tutorial on Full Model Fine-Tune using Hugging Face Transformers, which I have not yet attempted to follow.

I imagine this model will be particularly fun to play with directly in a browser using transformers.js.

Update: It is! Here's a bedtime story generator using Transformers.js (requires WebGPU, so Chrome-like browsers only). Here's the source code for that demo.

# 14th August 2025, 5:22 pm / gemini, llm, pelican-riding-a-bicycle, ai, local-llms, llms, gemma, llm-release, google, generative-ai, lm-studio

Claude Opus 4.1. Surprise new model from Anthropic today - Claude Opus 4.1, which they describe as "a drop-in replacement for Opus 4".

My favorite thing about this model is the version number - treating this as a .1 version increment looks like it's an accurate depiction of the model's capabilities.

Anthropic's own benchmarks show very small incremental gains.

Comparing Opus 4 and Opus 4.1 (I got 4.1 to extract this information from a screenshot of Anthropic's own benchmark scores, then asked it to look up the links, then verified the links myself and fixed a few):

• Agentic coding (SWE-bench Verified): From 72.5% to 74.5%
• Agentic terminal coding (Terminal-Bench): From 39.2% to 43.3%
• Graduate-level reasoning (GPQA Diamond): From 79.6% to 80.9%
• Agentic tool use (TAU-bench):
• Retail: From 81.4% to 82.4%
• Airline: From 59.6% to 56.0% (decreased)
• Multilingual Q&A (MMMLU): From 88.8% to 89.5%
• Visual reasoning (MMMU validation): From 76.5% to 77.1%
• High school math competition (AIME 2025): From 75.5% to 78.0%

Likewise, the model card shows only tiny changes to the various safety metrics that Anthropic track.

It's priced the same as Opus 4 - $15/million for input and $75/million for output, making it one of the most expensive models on the market today.

I had it draw me this pelican riding a bicycle:

For comparison I got a fresh new pelican out of Opus 4 which I actually like a little more:

I shipped llm-anthropic 0.18 with support for the new model.

# 5th August 2025, 5:17 pm / anthropic, claude, pelican-riding-a-bicycle, llm-pricing, ai, llms, llm-release, generative-ai, evals, llm

XBai o4 (via) Yet another open source (Apache 2.0) LLM from a Chinese AI lab. This model card claims:

XBai o4 excels in complex reasoning capabilities and has now completely surpassed OpenAI-o3-mini in Medium mode.

This a 32.8 billion parameter model released by MetaStone AI, a new-to-me lab who released their first model in March - MetaStone-L1-7B, then followed that with MetaStone-S1 1.5B, 7B and 32B in July and now XBai o4 in August.

The MetaStone-S1 models were accompanied with a paper, Test-Time Scaling with Reflective Generative Model.

There is very little information available on the English-language web about MetaStone AI. Their paper shows a relationship with USTC, University of Science and Technology of China in Hefei. One of their researchers confirmed on Twitter that their CEO is from KWAI which lead me to this Chinese language article from July last year about Li Yan, formerly of KWAI and now the founder of Wen Xiaobai and evidently now the CEO of MetaStone. www.wenxiaobai.com is listed as the "official website" linked to from the XBai-o4 README on GitHub.

Ivan Fioravanti got it working under MLX in 4bit, 5bit, 6bit, 8bit and 4bit-DWQ sizes. I tried his 6bit one (a 24.81GB download) in LM Studio and had it "Generate an SVG of a pelican riding a bicycle". It thought for 39.49 seconds (thinking trace here) and gave me this:

I also had it "Write an HTML and JavaScript page implementing space invaders", which you can try out here. It's not great - the invaders don't fire bullets (it listed that as a "potential improvement") and I don't think it's winnable due to timing issues, but it's an interesting addition to my collection.

LM Studio reported a constant memory usage of 25.54GB and according to Activity Monitor used 87.5% of my GPU and around 39% of a CPU core.

It did a lot of work in the thinking trace - which took 10m35s! - including writing the first draft of much of the code that ended up in the finished result. Here's an extract:

Let me think of variables:

let enemyDirection = 1; // 1 for right, -1 for left

In the game loop: enemies.forEach(enemy => {> enemy.x += enemyDirection * enemy.dx;> }); Then check if any enemy is out of bounds. If so, reverse direction and move all enemies down.

But how to check if any enemy is at the edge?

Maybe find the max x of all enemies and see if it exceeds canvas.width - enemy.width.

Alternatively, track the leftmost and rightmost positions of enemies. Maybe a bit complex.

For simplicity, perhaps check if any enemy's x + width > canvas.width or x < 0.

Here's the full transcript.

My initial impressions are that I'm not as impressed with this model for running on my own laptop as I was with Qwen3-Coder-30B-A3B-Instruct or GLM-4.5 Air.

But... how extraordinary is it that another Chinese AI lab has been able to produce a competitive model, this time with far less fanfare than we've seen from Qwen and Moonshot AI and Z.ai.

# 3rd August 2025, 10:21 pm / mlx, llm-reasoning, pelican-riding-a-bicycle, ai, ai-in-china, llms, space-invaders, llm-release, generative-ai, lm-studio, ivan-fioravanti

Deep Think in the Gemini app (via) Google released Gemini 2.5 Deep Think this morning, exclusively to their Ultra ($250/month) subscribers:

It is a variation of the model that recently achieved the gold-medal standard at this year's International Mathematical Olympiad (IMO). While that model takes hours to reason about complex math problems, today's release is faster and more usable day-to-day, while still reaching Bronze-level performance on the 2025 IMO benchmark, based on internal evaluations.

Google describe Deep Think's architecture like this:

Just as people tackle complex problems by taking the time to explore different angles, weigh potential solutions, and refine a final answer, Deep Think pushes the frontier of thinking capabilities by using parallel thinking techniques. This approach lets Gemini generate many ideas at once and consider them simultaneously, even revising or combining different ideas over time, before arriving at the best answer.

This approach sounds a little similar to the llm-consortium plugin by Thomas Hughes, see this video from January's Datasette Public Office Hours.

I don't have an Ultra account, but thankfully nickandbro on Hacker News tried "Create a svg of a pelican riding on a bicycle" (a very slight modification of my prompt, which uses "Generate an SVG") and got back a very solid result:

The bicycle is the right shape, and this is one of the few results I've seen for this prompt where the bird is very clearly a pelican thanks to the shape of its beak.

There are more details on Deep Think in the Gemini 2.5 Deep Think Model Card (PDF). Some highlights from that document:

• 1 million token input window, accepting text, images, audio, and video.
• Text output up to 192,000 tokens.
• Training ran on TPUs and used JAX and ML Pathways.
• "We additionally trained Gemini 2.5 Deep Think on novel reinforcement learning techniques that can leverage more multi-step reasoning, problem-solving and theorem-proving data, and we also provided access to a curated corpus of high-quality solutions to mathematics problems."
• Knowledge cutoff is January 2025.

# 1st August 2025, 5:09 pm / gemini, llm-reasoning, pelican-riding-a-bicycle, ai, llms, llm-release, google, generative-ai

Qwen3-30B-A3B-Thinking-2507 (via) Yesterday was Qwen3-30B-A3B-Instruct-2507. Qwen are clearly committed to their new split between reasoning and non-reasoning models (a reversal from Qwen 3 in April), because today they released the new reasoning partner to yesterday's model: Qwen3-30B-A3B-Thinking-2507.

I'm surprised at how poorly this reasoning mode performs at "Generate an SVG of a pelican riding a bicycle" compared to its non-reasoning partner. The reasoning trace appears to carefully consider each component and how it should be positioned... and then the final result looks like this:

I ran this using chat.qwen.ai/?model=Qwen3-30B-A3B-2507 with the "reasoning" option selected.

I also tried the "Write an HTML and JavaScript page implementing space invaders" prompt I ran against the non-reasoning model. It did a better job in that the game works:

It's not as playable as the on I got from GLM-4.5 Air though - the invaders fire their bullets infrequently enough that the game isn't very challenging.

This model is part of a flurry of releases from Qwen over the past two 9 days. Here's my coverage of each of those:

• Qwen3-235B-A22B-Instruct-2507 - 21st July
• Qwen3-Coder-480B-A35B-Instruct - 22nd July
• Qwen3-235B-A22B-Thinking-2507 - 25th July
• Qwen3-30B-A3B-Instruct-2507 - 29th July
• Qwen3-30B-A3B-Thinking-2507 - today

# 30th July 2025, 3:36 pm / llm-release, generative-ai, llm-reasoning, ai, qwen, llms, pelican-riding-a-bicycle, ai-in-china, space-invaders

Qwen3-30B-A3B-Instruct-2507. New model update from Qwen, improving on their previous Qwen3-30B-A3B release from late April. In their tweet they said:

Smarter, faster, and local deployment-friendly.

✨ Key Enhancements:
✅ Enhanced reasoning, coding, and math skills
✅ Broader multilingual knowledge
✅ Improved long-context understanding (up to 256K tokens)
✅ Better alignment with user intent and open-ended tasks
✅ No more <think> blocks — now operating exclusively in non-thinking mode

🔧 With 3B activated parameters, it's approaching the performance of GPT-4o and Qwen3-235B-A22B Non-Thinking

I tried the chat.qwen.ai hosted model with "Generate an SVG of a pelican riding a bicycle" and got this:

I particularly enjoyed this detail from the SVG source code:

<!-- Bonus: Pelican's smile -->
<path d="M245,145 Q250,150 255,145" fill="none" stroke="#d4a037" stroke-width="2"/>

I went looking for quantized versions that could fit on my Mac and found lmstudio-community/Qwen3-30B-A3B-Instruct-2507-MLX-8bit from LM Studio. Getting that up and running was a 32.46GB download and it appears to use just over 30GB of RAM.

The pelican I got from that one wasn't as good:

I then tried that local model on the "Write an HTML and JavaScript page implementing space invaders" task that I ran against GLM-4.5 Air. The output looked promising, in particular it seemed to be putting more effort into the design of the invaders (GLM-4.5 Air just used rectangles):

// Draw enemy ship
ctx.fillStyle = this.color;

// Ship body
ctx.fillRect(this.x, this.y, this.width, this.height);

// Enemy eyes
ctx.fillStyle = '#fff';
ctx.fillRect(this.x + 6, this.y + 5, 4, 4);
ctx.fillRect(this.x + this.width - 10, this.y + 5, 4, 4);

// Enemy antennae
ctx.fillStyle = '#f00';
if (this.type === 1) {
    // Basic enemy
    ctx.fillRect(this.x + this.width / 2 - 1, this.y - 5, 2, 5);
} else if (this.type === 2) {
    // Fast enemy
    ctx.fillRect(this.x + this.width / 4 - 1, this.y - 5, 2, 5);
    ctx.fillRect(this.x + (3 * this.width) / 4 - 1, this.y - 5, 2, 5);
} else if (this.type === 3) {
    // Armored enemy
    ctx.fillRect(this.x + this.width / 2 - 1, this.y - 8, 2, 8);
    ctx.fillStyle = '#0f0';
    ctx.fillRect(this.x + this.width / 2 - 1, this.y - 6, 2, 3);
}

But the resulting code didn't actually work:

That same prompt against the unquantized Qwen-hosted model produced a different result which sadly also resulted in an unplayable game - this time because everything moved too fast.

This new Qwen model is a non-reasoning model, whereas GLM-4.5 and GLM-4.5 Air are both reasoners. It looks like at this scale the "reasoning" may make a material difference in terms of getting code that works out of the box.

# 29th July 2025, 6:57 pm / llm-release, generative-ai, mlx, lm-studio, ai, qwen, llms, llm-reasoning, ai-in-china, space-invaders

GLM-4.5: Reasoning, Coding, and Agentic Abililties. Another day, another significant new open weight model release from a Chinese frontier AI lab.

This time it's Z.ai - who rebranded (at least in English) from Zhipu AI a few months ago. They just dropped GLM-4.5-Base, GLM-4.5 and GLM-4.5 Air on Hugging Face, all under an MIT license.

These are MoE hybrid reasoning models with thinking and non-thinking modes, similar to Qwen 3. GLM-4.5 is 355 billion total parameters with 32 billion active, GLM-4.5-Air is 106 billion total parameters and 12 billion active.

They started using MIT a few months ago for their GLM-4-0414 models - their older releases used a janky non-open-source custom license.

Z.ai's own benchmarking (across 12 common benchmarks) ranked their GLM-4.5 3rd behind o3 and Grok-4 and just ahead of Claude Opus 4. They ranked GLM-4.5 Air 6th place just ahead of Claude 4 Sonnet. I haven't seen any independent benchmarks yet.

The other models they included in their own benchmarks were o4-mini (high), Gemini 2.5 Pro, Qwen3-235B-Thinking-2507, DeepSeek-R1-0528, Kimi K2, GPT-4.1, DeepSeek-V3-0324. Notably absent: any of Meta's Llama models, or any of Mistral's. Did they deliberately only compare themselves to open weight models from other Chinese AI labs?

Both models have a 128,000 context length and are trained for tool calling, which honestly feels like table stakes for any model released in 2025 at this point.

It's interesting to see them use Claude Code to run their own coding benchmarks:

To assess GLM-4.5's agentic coding capabilities, we utilized Claude Code to evaluate performance against Claude-4-Sonnet, Kimi K2, and Qwen3-Coder across 52 coding tasks spanning frontend development, tool development, data analysis, testing, and algorithm implementation. [...] The empirical results demonstrate that GLM-4.5 achieves a 53.9% win rate against Kimi K2 and exhibits dominant performance over Qwen3-Coder with an 80.8% success rate. While GLM-4.5 shows competitive performance, further optimization opportunities remain when compared to Claude-4-Sonnet.

They published the dataset for that benchmark as zai-org/CC-Bench-trajectories on Hugging Face. I think they're using the word "trajectory" for what I would call a chat transcript.

Unlike DeepSeek-V3 and Kimi K2, we reduce the width (hidden dimension and number of routed experts) of the model while increasing the height (number of layers), as we found that deeper models exhibit better reasoning capacity.

They pre-trained on 15 trillion tokens, then an additional 7 trillion for code and reasoning:

Our base model undergoes several training stages. During pre-training, the model is first trained on 15T tokens of a general pre-training corpus, followed by 7T tokens of a code & reasoning corpus. After pre-training, we introduce additional stages to further enhance the model's performance on key downstream domains.

They also open sourced their post-training reinforcement learning harness, which they've called slime. That's available at THUDM/slime on GitHub - THUDM is the Knowledge Engineer Group @ Tsinghua University, the University from which Zhipu AI spun out as an independent company.

This time I ran my pelican bechmark using the chat.z.ai chat interface, which offers free access (no account required) to both GLM 4.5 and GLM 4.5 Air. I had reasoning enabled for both.

Here's what I got for "Generate an SVG of a pelican riding a bicycle" on GLM 4.5. I like how the pelican has its wings on the handlebars:

And GLM 4.5 Air:

Ivan Fioravanti shared a video of the mlx-community/GLM-4.5-Air-4bit quantized model running on a M4 Mac with 128GB of RAM, and it looks like a very strong contender for a local model that can write useful code. The cheapest 128GB Mac Studio costs around $3,500 right now, so genuinely great open weight coding models are creeping closer to being affordable on consumer machines.

Update: Ivan released a 3 bit quantized version of GLM-4.5 Air which runs using 48GB of RAM on my laptop. I tried it and was really impressed, see My 2.5 year old laptop can write Space Invaders in JavaScript now.

# 28th July 2025, 4:56 pm / llm-release, llms, ai, generative-ai, llm-reasoning, pelican-riding-a-bicycle, local-llms, mlx, ai-in-china, ivan-fioravanti

Qwen3-235B-A22B-Thinking-2507 (via) The third Qwen model release week, following Qwen3-235B-A22B-Instruct-2507 on Monday 21st and Qwen3-Coder-480B-A35B-Instruct on Tuesday 22nd.

Those two were both non-reasoning models - a change from the previous models in the Qwen 3 family which combined reasoning and non-reasoning in the same model, controlled by /think and /no_think tokens.

Today's model, Qwen3-235B-A22B-Thinking-2507 (also released as an FP8 variant), is their new thinking variant.

Qwen claim "state-of-the-art results among open-source thinking models" and have increased the context length to 262,144 tokens - a big jump from April's Qwen3-235B-A22B which was "32,768 natively and 131,072 tokens with YaRN".

Their own published benchmarks show comparable scores to DeepSeek-R1-0528, OpenAI's o3 and o4-mini, Gemini 2.5 Pro and Claude Opus 4 in thinking mode.

The new model is already available via OpenRouter.

But how good is its pelican?

I tried it with "Generate an SVG of a pelican riding a bicycle" via OpenRouter, and it thought for 166 seconds - nearly three minutes! I have never seen a model think for that long. No wonder the documentation includes the following:

However, since the model may require longer token sequences for reasoning, we strongly recommend using a context length greater than 131,072 when possible.

Here's a copy of that thinking trace. It was really fun to scan through:

The finished pelican? Not so great! I like the beak though:

# 25th July 2025, 10:52 pm / llm-release, generative-ai, llm-reasoning, pelican-riding-a-bicycle, ai, qwen, llms, ai-in-china

Qwen3-Coder: Agentic Coding in the World (via) It turns out that as I was typing up my notes on Qwen3-235B-A22B-Instruct-2507 the Qwen team were unleashing something much bigger:

Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct — a 480B-parameter Mixture-of-Experts model with 35B active parameters which supports the context length of 256K tokens natively and 1M tokens with extrapolation methods, offering exceptional performance in both coding and agentic tasks.

This is another Apache 2.0 licensed open weights model, available as Qwen3-Coder-480B-A35B-Instruct and Qwen3-Coder-480B-A35B-Instruct-FP8 on Hugging Face.

I used qwen3-coder-480b-a35b-instruct on the Hyperbolic playground to run my "Generate an SVG of a pelican riding a bicycle" test prompt:

I actually slightly prefer the one I got from qwen3-235b-a22b-07-25.

It's also available as qwen3-coder on OpenRouter.

In addition to the new model, Qwen released their own take on an agentic terminal coding assistant called qwen-code, which they describe in their blog post as being "Forked from Gemini Code" (they mean gemini-cli) - which is Apache 2.0 so a fork is in keeping with the license.

They focused really hard on code performance for this release, including generating synthetic data tested using 20,000 parallel environments on Alibaba Cloud:

In the post-training phase of Qwen3-Coder, we introduced long-horizon RL (Agent RL) to encourage the model to solve real-world tasks through multi-turn interactions using tools. The key challenge of Agent RL lies in environment scaling. To address this, we built a scalable system capable of running 20,000 independent environments in parallel, leveraging Alibaba Cloud’s infrastructure. The infrastructure provides the necessary feedback for large-scale reinforcement learning and supports evaluation at scale. As a result, Qwen3-Coder achieves state-of-the-art performance among open-source models on SWE-Bench Verified without test-time scaling.

To further burnish their coding credentials, the announcement includes instructions for running their new model using both Claude Code and Cline using custom API base URLs that point to Qwen's own compatibility proxies.

Pricing for Qwen's own hosted models (through Alibaba Cloud) looks competitive. This is the first model I've seen that sets different prices for four different sizes of input:

This kind of pricing reflects how inference against longer inputs is more expensive to process. Gemini 2.5 Pro has two different prices for above or below 200,00 tokens.

Awni Hannun reports running a 4-bit quantized MLX version on a 512GB M3 Ultra Mac Studio at 24 tokens/second using 272GB of RAM, getting great results for "write a python script for a bouncing yellow ball within a square, make sure to handle collision detection properly. make the square slowly rotate. implement it in python. make sure ball stays within the square".

# 22nd July 2025, 10:52 pm / llm-release, coding-agents, generative-ai, ai, qwen, llms, ai-assisted-programming, llm-pricing, openrouter, pelican-riding-a-bicycle, ai-in-china

Qwen/Qwen3-235B-A22B-Instruct-2507. Significant new model release from Qwen, published yesterday without much fanfare. (Update: probably because they were cooking the much larger Qwen3-Coder-480B-A35B-Instruct which they released just now.)

This is a follow-up to their April release of the full Qwen 3 model family, which included a Qwen3-235B-A22B model which could handle both reasoning and non-reasoning prompts (via a /no_think toggle).

The new Qwen3-235B-A22B-Instruct-2507 ditches that mechanism - this is exclusively a non-reasoning model. It looks like Qwen have new reasoning models in the pipeline.

This new model is Apache 2 licensed and comes in two official sizes: a BF16 model (437.91GB of files on Hugging Face) and an FP8 variant (220.20GB). VentureBeat estimate that the large model needs 88GB of VRAM while the smaller one should run in ~30GB.

The benchmarks on these new models look very promising. Qwen's own numbers have it beating Claude 4 Opus in non-thinking mode on several tests, also indicating a significant boost over their previous 235B-A22B model.

I haven't seen any independent benchmark results yet. Here's what I got for "Generate an SVG of a pelican riding a bicycle", which I ran using the qwen3-235b-a22b-07-25:free on OpenRouter:

llm install llm-openrouter
llm -m openrouter/qwen/qwen3-235b-a22b-07-25:free \
  "Generate an SVG of a pelican riding a bicycle"

# 22nd July 2025, 10:07 pm / llm-release, generative-ai, pelican-riding-a-bicycle, openrouter, ai, qwen, llms, llm, ai-in-china

Gemini 2.5 Flash-Lite is now stable and generally available. The last remaining member of the Gemini 2.5 trio joins Pro and Flash in General Availability today.

Gemini 2.5 Flash-Lite is the cheapest of the 2.5 family, at $0.10/million input tokens and $0.40/million output tokens. This puts it equal to GPT-4.1 Nano on my llm-prices.com comparison table.

The preview version of that model had the same pricing for text tokens, but is now cheaper for audio:

We have also reduced audio input pricing by 40% from the preview launch.

I released llm-gemini 0.24 with support for the new model alias:

llm install -U llm-gemini
llm -m gemini-2.5-flash-lite \
  -a https://static.simonwillison.net/static/2024/pelican-joke-request.mp3

I wrote more about the Gemini 2.5 Flash-Lite preview model last month.

# 22nd July 2025, 8:50 pm / gemini, llm, llm-pricing, ai, llms, llm-release, google, generative-ai

moonshotai/Kimi-K2-Instruct (via) Colossal new open weights model release today from Moonshot AI, a two year old Chinese AI lab with a name inspired by Pink Floyd’s album The Dark Side of the Moon.

My HuggingFace storage calculator says the repository is 958.52 GB. It's a mixture-of-experts model with "32 billion activated parameters and 1 trillion total parameters", trained using the Muon optimizer as described in Moonshot's joint paper with UCLA Muon is Scalable for LLM Training.

I think this may be the largest ever open weights model? DeepSeek v3 is 671B.

I created an API key for Moonshot, added some dollars and ran a prompt against it using my LLM tool. First I added this to the extra-openai-models.yaml file:

- model_id: kimi-k2
  model_name: kimi-k2-0711-preview
  api_base: https://api.moonshot.ai/v1
  api_key_name: moonshot

Then I set the API key:

llm keys set moonshot
# Paste key here

And ran a prompt:

llm -m kimi-k2 "Generate an SVG of a pelican riding a bicycle" \
  -o max_tokens 2000

(The default max tokens setting was too short.)

This is pretty good! The spokes are a nice touch. Full transcript here.

This one is open weights but not open source: they're using a modified MIT license with this non-OSI-compliant section tagged on at the end:

Our only modification part is that, if the Software (or any derivative works thereof) is used for any of your commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display "Kimi K2" on the user interface of such product or service.

Update: MLX developer Awni Hannun reports:

The new Kimi K2 1T model (4-bit quant) runs on 2 512GB M3 Ultras with mlx-lm and mx.distributed.

1 trillion params, at a speed that's actually quite usable

# 11th July 2025, 6:33 pm / llm-release, llm, generative-ai, pelican-riding-a-bicycle, ai, llms, mlx, ai-in-china, moonshot

Grok 4. Released last night, Grok 4 is now available via both API and a paid subscription for end-users.

Update: If you ask it about controversial topics it will sometimes search X for tweets "from:elonmusk"!

Key characteristics: image and text input, text output. 256,000 context length (twice that of Grok 3). It's a reasoning model where you can't see the reasoning tokens or turn off reasoning mode.

xAI released results showing Grok 4 beating other models on most of the significant benchmarks. I haven't been able to find their own written version of these (the launch was a livestream video) but here's a TechCrunch report that includes those scores. It's not clear to me if these benchmark results are for Grok 4 or Grok 4 Heavy.

I ran my own benchmark using Grok 4 via OpenRouter (since I have API keys there already).

llm -m openrouter/x-ai/grok-4 "Generate an SVG of a pelican riding a bicycle" \
  -o max_tokens 10000

I then asked Grok to describe the image it had just created:

llm -m openrouter/x-ai/grok-4 -o max_tokens 10000 \
  -a https://static.simonwillison.net/static/2025/grok4-pelican.png \
  'describe this image'

Here's the result. It described it as a "cute, bird-like creature (resembling a duck, chick, or stylized bird)".

The most interesting independent analysis I've seen so far is this one from Artificial Analysis:

We have run our full suite of benchmarks and Grok 4 achieves an Artificial Analysis Intelligence Index of 73, ahead of OpenAI o3 at 70, Google Gemini 2.5 Pro at 70, Anthropic Claude 4 Opus at 64 and DeepSeek R1 0528 at 68.

The timing of the release is somewhat unfortunate, given that Grok 3 made headlines just this week after a clumsy system prompt update - presumably another attempt to make Grok "less woke" - caused it to start firing off antisemitic tropes and referring to itself as MechaHitler.

My best guess is that these lines in the prompt were the root of the problem:

- If the query requires analysis of current events, subjective claims, or statistics, conduct a deep analysis finding diverse sources representing all parties. Assume subjective viewpoints sourced from the media are biased. No need to repeat this to the user.
- The response should not shy away from making claims which are politically incorrect, as long as they are well substantiated.

If xAI expect developers to start building applications on top of Grok they need to do a lot better than this. Absurd self-inflicted mistakes like this do not build developer trust!

As it stands, Grok 4 isn't even accompanied by a model card.

Update: Ian Bicking makes an astute point:

It feels very credulous to ascribe what happened to a system prompt update. Other models can't be pushed into racism, Nazism, and ideating rape with a system prompt tweak.

Even if that system prompt change was responsible for unlocking this behavior, the fact that it was able to speaks to a much looser approach to model safety by xAI compared to other providers.

Update 12th July 2025: Grok posted a postmortem blaming the behavior on a different set of prompts, including "you are not afraid to offend people who are politically correct", that were not included in the system prompts they had published to their GitHub repository.

Grok 4 is competitively priced. It's $3/million for input tokens and $15/million for output tokens - the same price as Claude Sonnet 4. Once you go above 128,000 input tokens the price doubles to $6/$30 (Gemini 2.5 Pro has a similar price increase for longer inputs). I've added these prices to llm-prices.com.

Consumers can access Grok 4 via a new $30/month or $300/year "SuperGrok" plan - or a $300/month or $3,000/year "SuperGrok Heavy" plan providing access to Grok 4 Heavy.

# 10th July 2025, 7:36 pm / vision-llms, llm-reasoning, pelican-riding-a-bicycle, llm-pricing, ai, llms, ai-ethics, llm-release, grok, generative-ai, openrouter, system-prompts, artificial-analysis, xai