Simon Willison’s Weblog

qwen2.5vl in Ollama. Ollama announced a complete overhaul of their vision support the other day. Here's the first new model they've shipped since then - a packaged version of Qwen 2.5 VL which was first released on January 26th 2025. Here are my notes from that release.

I upgraded Ollama (it auto-updates so I just had to restart it from the tray icon) and ran this:

ollama pull qwen2.5vl

This downloaded a 6GB model file. I tried it out against my photo of Cleo rolling on the beach:

llm -a https://static.simonwillison.net/static/2025/cleo-sand.jpg \
  'describe this image' -m qwen2.5vl

And got a pretty good result:

The image shows a dog lying on its back on a sandy beach. The dog appears to be a medium to large breed with a dark coat, possibly black or dark brown. It is wearing a red collar or harness around its chest. The dog's legs are spread out, and its belly is exposed, suggesting it might be rolling around or playing in the sand. The sand is light-colored and appears to be dry, with some small footprints and marks visible around the dog. The lighting in the image suggests it is taken during the daytime, with the sun casting a shadow of the dog to the left side of the image. The overall scene gives a relaxed and playful impression, typical of a dog enjoying time outdoors on a beach.

Qwen 2.5 VL has a strong reputation for OCR, so I tried it on my poster:

llm -a https://static.simonwillison.net/static/2025/poster.jpg \
  'convert to markdown' -m qwen2.5vl

The result that came back:

It looks like the image you provided is a jumbled and distorted text, making it difficult to interpret. If you have a specific question or need help with a particular topic, please feel free to ask, and I'll do my best to assist you!

I'm not sure what went wrong here. My best guess is that the maximum resolution the model can handle is too small to make out the text, or maybe Ollama resized the image to the point of illegibility before handing it to the model?

Update: I think this may be a bug relating to URL handling in LLM/llm-ollama. I tried downloading the file first:

wget https://static.simonwillison.net/static/2025/poster.jpg
llm -m qwen2.5vl 'extract text' -a poster.jpg

This time it did a lot better. The results weren't perfect though - it ended up stuck in a loop outputting the same code example dozens of times.

I tried with a different prompt - "extract text" - and it got confused by the three column layout, misread Datasette as "Datasetette" and missed some of the text. Here's that result.

These experiments used qwen2.5vl:7b (6GB) - I expect the results would be better with the larger qwen2.5vl:32b (21GB) and qwen2.5vl:72b (71GB) models.

Fred Jonsson reported a better result using the MLX model via LM studio (~9GB model running in 8bit - I think that's mlx-community/Qwen2.5-VL-7B-Instruct-8bit). His full output is here - looks almost exactly right to me.

# 18th May 2025, 12:31 pm / vision-llms, llm, ollama, generative-ai, ai, qwen, llms, mlx, ocr, local-llms, ai-in-china

OpenAI Codex. Announced today, here's the documentation for OpenAI's "cloud-based software engineering agent". It's not yet available for us $20/month Plus customers ("coming soon") but if you're a $200/month Pro user you can try it out now.

At a high level, you specify a prompt, and the agent goes to work in its own environment. After about 8–10 minutes, the agent gives you back a diff.

You can execute prompts in either ask mode or code mode. When you select ask, Codex clones a read-only version of your repo, booting faster and giving you follow-up tasks. Code mode, however, creates a full-fledged environment that the agent can run and test against.

This 4 minute demo video is a useful overview. One note that caught my eye is that the setup phase for an environment can pull from the internet (to install necessary dependencies) but the agent loop itself still runs in a network disconnected sandbox.

It sounds similar to GitHub's own Copilot Workspace project, which can compose PRs against your code based on a prompt. The big difference is that Codex incorporates a full Code Interpeter style environment, allowing it to build and run the code it's creating and execute tests in a loop.

Copilot Workspaces has a level of integration with Codespaces but still requires manual intervention to help exercise the code.

Also similar to Copilot Workspaces is a confusing name. OpenAI now have four products called Codex:

• OpenAI Codex, announced today.
• Codex CLI, a completely different coding assistant tool they released a few weeks ago that is the same kind of shape as Claude Code. This one owns the openai/codex namespace on GitHub.
• codex-mini, a brand new model released today that is used by their Codex product. It's a fine-tuned o4-mini variant. I released llm-openai-plugin 0.4 adding support for that model.
• OpenAI Codex (2021) - Internet Archive link, OpenAI's first specialist coding model from the GPT-3 era. This was used by the original GitHub Copilot and is still the current topic of Wikipedia's OpenAI Codex page.

My favorite thing about this most recent Codex product is that OpenAI shared the full Dockerfile for the environment that the system uses to run code - in openai/codex-universal on GitHub because openai/codex was taken already.

This is extremely useful documentation for figuring out how to use this thing - I'm glad they're making this as transparent as possible.

And to be fair, If you ignore it previous history Codex Is a good name for this product. I'm just glad they didn't call it Ada.

# 16th May 2025, 7:12 pm / ai-assisted-programming, generative-ai, ai-agents, openai, ai, github, llms, llm-release, llm, cli, coding-agents, async-coding-agents

LLM 0.26a0 adds support for tools! It's only an alpha so I'm not going to promote this extensively yet, but my LLM project just grew a feature I've been working towards for nearly two years now: tool support!

I'm presenting a workshop about Building software on top of Large Language Models at PyCon US tomorrow and this was the one feature I really needed to pull everything else together.

Tools can be used from the command-line like this (inspired by sqlite-utils --functions):

llm --functions '
def multiply(x: int, y: int) -> int:
    """Multiply two numbers."""
    return x * y
' 'what is 34234 * 213345' -m o4-mini

You can add --tools-debug (shortcut: --td) to have it show exactly what tools are being executed and what came back. More documentation here.

It's also available in the Python library:

import llm

def multiply(x: int, y: int) -> int:
    """Multiply two numbers."""
    return x * y

model = llm.get_model("gpt-4.1-mini")
response = model.chain(
    "What is 34234 * 213345?",
    tools=[multiply]
)
print(response.text())

There's also a new plugin hook so plugins can register tools that can then be referenced by name using llm --tool name_of_tool "prompt".

There's still a bunch I want to do before including this in a stable release, most notably adding support for Python asyncio. It's a pretty exciting start though!

llm-anthropic 0.16a0 and llm-gemini 0.20a0 add tool support for Anthropic and Gemini models, depending on the new LLM alpha.

Update: Here's the section about tools from my PyCon workshop.

# 14th May 2025, 2 am / llm, generative-ai, projects, llm-tool-use, ai, llms, openai, gemini, anthropic

llm-gemini 0.19.1. Bugfix release for my llm-gemini plugin, which was recording the number of output tokens (needed to calculate the price of a response) incorrectly for the Gemini "thinking" models. Those models turn out to return candidatesTokenCount and thoughtsTokenCount as two separate values which need to be added together to get the total billed output token count. Full details in this issue.

I spotted this potential bug in this response log this morning, and my concerns were confirmed when Paul Gauthier wrote about a similar fix in Aider in Gemini 2.5 Pro Preview 03-25 benchmark cost, where he noted that the $6.32 cost recorded to benchmark Gemini 2.5 Pro Preview 03-25 was incorrect. Since that model is no longer available (despite the date-based model alias persisting) Paul is not able to accurately calculate the new cost, but it's likely a lot more since the Gemini 2.5 Pro Preview 05-06 benchmark cost $37.

I've gone through my gemini tag and attempted to update my previous posts with new calculations - this mostly involved increases in the order of 12.336 cents to 16.316 cents (as seen here).

# 8th May 2025, 5:49 am / paul-gauthier, gemini, llm, aider, generative-ai, llm-pricing, ai, llms

Medium is the new large. New model release from Mistral - this time closed source/proprietary. Mistral Medium claims strong benchmark scores similar to GPT-4o and Claude 3.7 Sonnet, but is priced at $0.40/million input and $2/million output - about the same price as GPT 4.1 Mini. For comparison, GPT-4o is $2.50/$10 and Claude 3.7 Sonnet is $3/$15.

The model is a vision LLM, accepting both images and text.

More interesting than the price is the deployment model. Mistral Medium may not be open weights but it is very much available for self-hosting:

Mistral Medium 3 can also be deployed on any cloud, including self-hosted environments of four GPUs and above.

Mistral's other announcement today is Le Chat Enterprise. This is a suite of tools that can integrate with your company's internal data and provide "agents" (these look similar to Claude Projects or OpenAI GPTs), again with the option to self-host.

Is there a new open weights model coming soon? This note tucked away at the bottom of the Mistral Medium 3 announcement seems to hint at that:

With the launches of Mistral Small in March and Mistral Medium today, it's no secret that we're working on something 'large' over the next few weeks. With even our medium-sized model being resoundingly better than flagship open source models such as Llama 4 Maverick, we're excited to 'open' up what's to come :)

I released llm-mistral 0.12 adding support for the new model.

# 7th May 2025, 9:14 pm / llm-release, mistral, generative-ai, ai, llms, llm-pricing, llm, vision-llms