Simon Willison’s Weblog

Start building with Gemini 2.5 Flash (via) Google Gemini's latest model is Gemini 2.5 Flash, available in (paid) preview as gemini-2.5-flash-preview-04-17.

Building upon the popular foundation of 2.0 Flash, this new version delivers a major upgrade in reasoning capabilities, while still prioritizing speed and cost. Gemini 2.5 Flash is our first fully hybrid reasoning model, giving developers the ability to turn thinking on or off. The model also allows developers to set thinking budgets to find the right tradeoff between quality, cost, and latency.

Gemini AI Studio product lead Logan Kilpatrick says:

This is an early version of 2.5 Flash, but it already shows huge gains over 2.0 Flash.

You can fully turn off thinking if needed and use this model as a drop in replacement for 2.0 Flash.

I added support to the new model in llm-gemini 0.18. Here's how to try it out:

llm install -U llm-gemini
llm -m gemini-2.5-flash-preview-04-17 'Generate an SVG of a pelican riding a bicycle'

Here's that first pelican, using the default setting where Gemini Flash 2.5 makes its own decision in terms of how much "thinking" effort to apply:

Here's the transcript. This one used 11 input tokens and 4266 output tokens of which 2702 were "thinking" tokens.

I asked the model to "describe" that image and it could tell it was meant to be a pelican:

A simple illustration on a white background shows a stylized pelican riding a bicycle. The pelican is predominantly grey with a black eye and a prominent pink beak pouch. It is positioned on a black line-drawn bicycle with two wheels, a frame, handlebars, and pedals.

The way the model is priced is a little complicated. If you have thinking enabled, you get charged $0.15/million tokens for input and $3.50/million for output. With thinking disabled those output tokens drop to $0.60/million. I've added these to my pricing calculator.

For comparison, Gemini 2.0 Flash is $0.10/million input and $0.40/million for output.

So my first prompt - 11 input and 4266 output(with thinking enabled), cost 1.4933 cents.

Let's try 2.5 Flash again with thinking disabled:

llm -m gemini-2.5-flash-preview-04-17 'Generate an SVG of a pelican riding a bicycle' -o thinking_budget 0

11 input, 1705 output. That's 0.1025 cents. Transcript here - it still shows 25 thinking tokens even though I set the thinking budget to 0 - Logan confirms that this will still be billed at the lower rate:

In some rare cases, the model still thinks a little even with thinking budget = 0, we are hoping to fix this before we make this model stable and you won't be billed for thinking. The thinking budget = 0 is what triggers the billing switch.

Here's Gemini 2.5 Flash's self-description of that image:

A minimalist illustration shows a bright yellow bird riding a bicycle. The bird has a simple round body, small wings, a black eye, and an open orange beak. It sits atop a simple black bicycle frame with two large circular black wheels. The bicycle also has black handlebars and black and yellow pedals. The scene is set against a solid light blue background with a thick green stripe along the bottom, suggesting grass or ground.

And finally, let's ramp the thinking budget up to the maximum:

llm -m gemini-2.5-flash-preview-04-17 'Generate an SVG of a pelican riding a bicycle' -o thinking_budget 24576

I think it over-thought this one. Transcript - 5174 output tokens of which 3023 were thinking. A hefty 1.8111 cents!

A simple, cartoon-style drawing shows a bird-like figure riding a bicycle. The figure has a round gray head with a black eye and a large, flat orange beak with a yellow stripe on top. Its body is represented by a curved light gray shape extending from the head to a smaller gray shape representing the torso or rear. It has simple orange stick legs with round feet or connections at the pedals. The figure is bent forward over the handlebars in a cycling position. The bicycle is drawn with thick black outlines and has two large wheels, a frame, and pedals connected to the orange legs. The background is plain white, with a dark gray line at the bottom representing the ground.

One thing I really appreciate about Gemini 2.5 Flash's approach to SVGs is that it shows very good taste in CSS, comments and general SVG class structure. Here's a truncated extract - I run a lot of these SVG tests against different models and this one has a coding style that I particularly enjoy. (Gemini 2.5 Pro does this too).

<svg width="800" height="500" viewBox="0 0 800 500" xmlns="http://www.w3.org/2000/svg">
  <style>
    .bike-frame { fill: none; stroke: #333; stroke-width: 8; stroke-linecap: round; stroke-linejoin: round; }
    .wheel-rim { fill: none; stroke: #333; stroke-width: 8; }
    .wheel-hub { fill: #333; }
    /* ... */
    .pelican-body { fill: #d3d3d3; stroke: black; stroke-width: 3; }
    .pelican-head { fill: #d3d3d3; stroke: black; stroke-width: 3; }
    /* ... */
  </style>
  <!-- Ground Line -->
  <line x1="0" y1="480" x2="800" y2="480" stroke="#555" stroke-width="5"/>
  <!-- Bicycle -->
  <g id="bicycle">
    <!-- Wheels -->
    <circle class="wheel-rim" cx="250" cy="400" r="70"/>
    <circle class="wheel-hub" cx="250" cy="400" r="10"/>
    <circle class="wheel-rim" cx="550" cy="400" r="70"/>
    <circle class="wheel-hub" cx="550" cy="400" r="10"/>
    <!-- ... -->
  </g>
  <!-- Pelican -->
  <g id="pelican">
    <!-- Body -->
    <path class="pelican-body" d="M 440 330 C 480 280 520 280 500 350 C 480 380 420 380 440 330 Z"/>
    <!-- Neck -->
    <path class="pelican-neck" d="M 460 320 Q 380 200 300 270"/>
    <!-- Head -->
    <circle class="pelican-head" cx="300" cy="270" r="35"/>
    <!-- ... -->

The LM Arena leaderboard now has Gemini 2.5 Flash in joint second place, just behind Gemini 2.5 Pro and tied with ChatGPT-4o-latest, Grok-3 and GPT-4.5 Preview.

# 17th April 2025, 8:56 pm / llm-release, gemini, llm, google, llm-reasoning, llm-pricing, llms, pelican-riding-a-bicycle, svg, logan-kilpatrick, lm-arena

deepseek-ai/DeepSeek-V3-0324. Chinese AI lab DeepSeek just released the latest version of their enormous DeepSeek v3 model, baking the release date into the name DeepSeek-V3-0324.

The license is MIT (that's new - previous DeepSeek v3 had a custom license), the README is empty and the release adds up a to a total of 641 GB of files, mostly of the form model-00035-of-000163.safetensors.

The model only came out a few hours ago and MLX developer Awni Hannun already has it running at >20 tokens/second on a 512GB M3 Ultra Mac Studio ($9,499 of ostensibly consumer-grade hardware) via mlx-lm and this mlx-community/DeepSeek-V3-0324-4bit 4bit quantization, which reduces the on-disk size to 352 GB.

I think that means if you have that machine you can run it with my llm-mlx plugin like this, but I've not tried myself!

llm mlx download-model mlx-community/DeepSeek-V3-0324-4bit
llm chat -m mlx-community/DeepSeek-V3-0324-4bit

The new model is also listed on OpenRouter. You can try a chat at openrouter.ai/chat?models=deepseek/deepseek-chat-v3-0324:free.

Here's what the chat interface gave me for "Generate an SVG of a pelican riding a bicycle":

I have two API keys with OpenRouter - one of them worked with the model, the other gave me a No endpoints found matching your data policy error - I think because I had a setting on that key disallowing models from training on my activity. The key that worked was a free key with no attached billing credentials.

For my working API key the llm-openrouter plugin let me run a prompt like this:

llm install llm-openrouter
llm keys set openrouter
# Paste key here
llm -m openrouter/deepseek/deepseek-chat-v3-0324:free "best fact about a pelican"

Here's that "best fact" - the terminal output included Markdown and an emoji combo, here that's rendered.

One of the most fascinating facts about pelicans is their unique throat pouch, called a gular sac, which can hold up to 3 gallons (11 liters) of water—three times more than their stomach!

Here’s why it’s amazing:
- Fishing Tool: They use it like a net to scoop up fish, then drain the water before swallowing.
- Cooling Mechanism: On hot days, pelicans flutter the pouch to stay cool by evaporating water.
- Built-in "Shopping Cart": Some species even use it to carry food back to their chicks.

Bonus fact: Pelicans often fish cooperatively, herding fish into shallow water for an easy catch.

Would you like more cool pelican facts? 🐦🌊

In putting this post together I got Claude to build me this new tool for finding the total on-disk size of a Hugging Face repository, which is available in their API but not currently displayed on their website.

Update: Here's a notable independent benchmark from Paul Gauthier:

DeepSeek's new V3 scored 55% on aider's polyglot benchmark, significantly improving over the prior version. It's the #2 non-thinking/reasoning model, behind only Sonnet 3.7. V3 is competitive with thinking models like R1 & o3-mini.

# 24th March 2025, 3:04 pm / llm-release, hugging-face, generative-ai, deepseek, ai, llms, mlx, llm, ai-assisted-programming, tools, pelican-riding-a-bicycle, openrouter, local-llms

OpenAI platform: o1-pro. OpenAI have a new most-expensive model: o1-pro can now be accessed through their API at a hefty $150/million tokens for input and $600/million tokens for output. That's 10x the price of their o1 and o1-preview models and a full 1,000x times more expensive than their cheapest model, gpt-4o-mini!

Aside from that it has mostly the same features as o1: a 200,000 token context window, 100,000 max output tokens, Sep 30 2023 knowledge cut-off date and it supports function calling, structured outputs and image inputs.

o1-pro doesn't support streaming, and most significantly for developers is the first OpenAI model to only be available via their new Responses API. This means tools that are built against their Chat Completions API (like my own LLM) have to do a whole lot more work to support the new model - my issue for that is here.

Since LLM doesn't support this new model yet I had to make do with curl:

curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $(llm keys get openai)" \
  -d '{
    "model": "o1-pro",
    "input": "Generate an SVG of a pelican riding a bicycle"
  }'

Here's the full JSON I got back - 81 input tokens and 1552 output tokens for a total cost of 94.335 cents.

I took a risk and added "reasoning": {"effort": "high"} to see if I could get a better pelican with more reasoning:

curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $(llm keys get openai)" \
  -d '{
    "model": "o1-pro",
    "input": "Generate an SVG of a pelican riding a bicycle",
    "reasoning": {"effort": "high"}
  }'

Surprisingly that used less output tokens - 1459 compared to 1552 earlier (cost: 88.755 cents) - producing this JSON which rendered as a slightly better pelican:

It was cheaper because while it spent 960 reasoning tokens as opposed to 704 for the previous pelican it omitted the explanatory text around the SVG, saving on total output.

# 19th March 2025, 11:54 pm / o1, llm, openai, llm-reasoning, ai, llms, llm-release, generative-ai, pelican-riding-a-bicycle, llm-pricing

mlx-community/OLMo-2-0325-32B-Instruct-4bit (via) OLMo 2 32B claims to be "the first fully-open model (all data, code, weights, and details are freely available) to outperform GPT3.5-Turbo and GPT-4o mini". Thanks to the MLX project here's a recipe that worked for me to run it on my Mac, via my llm-mlx plugin.

To install the model:

llm install llm-mlx
llm mlx download-model mlx-community/OLMo-2-0325-32B-Instruct-4bit

That downloads 17GB to ~/.cache/huggingface/hub/models--mlx-community--OLMo-2-0325-32B-Instruct-4bit.

To start an interactive chat with OLMo 2:

llm chat -m mlx-community/OLMo-2-0325-32B-Instruct-4bit

Or to run a prompt:

llm -m mlx-community/OLMo-2-0325-32B-Instruct-4bit 'Generate an SVG of a pelican riding a bicycle' -o unlimited 1

The -o unlimited 1 removes the cap on the number of output tokens - the default for llm-mlx is 1024 which isn't enough to attempt to draw a pelican.

The pelican it drew is refreshingly abstract:

# 16th March 2025, 3:30 am / llm, generative-ai, mlx, ai2, ai, llms, pelican-riding-a-bicycle, local-llms

Claude 3.7 Sonnet and Claude Code. Anthropic released Claude 3.7 Sonnet today - skipping the name "Claude 3.6" because the Anthropic user community had already started using that as the unofficial name for their October update to 3.5 Sonnet.

As you may expect, 3.7 Sonnet is an improvement over 3.5 Sonnet - and is priced the same, at $3/million tokens for input and $15/m output.

The big difference is that this is Anthropic's first "reasoning" model - applying the same trick that we've now seen from OpenAI o1 and o3, Grok 3, Google Gemini 2.0 Thinking, DeepSeek R1 and Qwen's QwQ and QvQ. The only big model families without an official reasoning model now are Mistral and Meta's Llama.

I'm still working on adding support to my llm-anthropic plugin but I've got enough working code that I was able to get it to draw me a pelican riding a bicycle. Here's the non-reasoning model:

And here's that same prompt but with "thinking mode" enabled:

Here's the transcript for that second one, which mixes together the thinking and the output tokens. I'm still working through how best to differentiate between those two types of token.

Claude 3.7 Sonnet has a training cut-off date of Oct 2024 - an improvement on 3.5 Haiku's July 2024 - and can output up to 64,000 tokens in thinking mode (some of which are used for thinking tokens) and up to 128,000 if you enable a special header:

Claude 3.7 Sonnet can produce substantially longer responses than previous models with support for up to 128K output tokens (beta)---more than 15x longer than other Claude models. This expanded capability is particularly effective for extended thinking use cases involving complex reasoning, rich code generation, and comprehensive content creation.

This feature can be enabled by passing an anthropic-beta header of output-128k-2025-02-19.

Anthropic's other big release today is a preview of Claude Code - a CLI tool for interacting with Claude that includes the ability to prompt Claude in terminal chat and have it read and modify files and execute commands. This means it can both iterate on code and execute tests, making it an extremely powerful "agent" for coding assistance.

Here's Anthropic's documentation on getting started with Claude Code, which uses OAuth (a first for Anthropic's API) to authenticate against your API account, so you'll need to configure billing.

Short version:

npm install -g @anthropic-ai/claude-code
claude

It can burn a lot of tokens so don't be surprised if a lengthy session with it adds up to single digit dollars of API spend.

# 24th February 2025, 8:25 pm / llm, anthropic, claude, ai-agents, llm-reasoning, ai, llms, ai-assisted-programming, generative-ai, pelican-riding-a-bicycle, oauth, llm-release

Andrej Karpathy’s initial impressions of Grok 3. Andrej has the most detailed analysis I've seen so far of xAI's Grok 3 release from last night. He runs through a bunch of interesting test prompts, and concludes:

As far as a quick vibe check over ~2 hours this morning, Grok 3 + Thinking feels somewhere around the state of the art territory of OpenAI's strongest models (o1-pro, $200/month), and slightly better than DeepSeek-R1 and Gemini 2.0 Flash Thinking. Which is quite incredible considering that the team started from scratch ~1 year ago, this timescale to state of the art territory is unprecedented.

I was delighted to see him include my Generate an SVG of a pelican riding a bicycle benchmark in his tests:

Grok 3 is currently sat at the top of the LLM Chatbot Arena (across all of their categories) so it's doing very well based on vibes for the voters there.

# 18th February 2025, 4:46 pm / andrej-karpathy, llms, ai, generative-ai, pelican-riding-a-bicycle, grok, llm-release

Gemini 2.0 is now available to everyone. Big new Gemini 2.0 releases today:

• Gemini 2.0 Pro (Experimental) is Google's "best model yet for coding performance and complex prompts" - currently available as a free preview.
• Gemini 2.0 Flash is now generally available.

• Gemini 2.0 Flash-Lite looks particularly interesting:

  We’ve gotten a lot of positive feedback on the price and speed of 1.5 Flash. We wanted to keep improving quality, while still maintaining cost and speed. So today, we’re introducing 2.0 Flash-Lite, a new model that has better quality than 1.5 Flash, at the same speed and cost. It outperforms 1.5 Flash on the majority of benchmarks.

That means Gemini 2.0 Flash-Lite is priced at 7.5c/million input tokens and 30c/million output tokens - half the price of OpenAI's GPT-4o mini (15c/60c).

Gemini 2.0 Flash isn't much more expensive: 10c/million for text/image input, 70c/million for audio input, 40c/million for output. Again, cheaper than GPT-4o mini.

I pushed a new LLM plugin release, llm-gemini 0.10, adding support for the three new models:

llm install -U llm-gemini
llm keys set gemini
# paste API key here
llm -m gemini-2.0-flash "impress me"
llm -m gemini-2.0-flash-lite-preview-02-05 "impress me"
llm -m gemini-2.0-pro-exp-02-05 "impress me"

Here's the output for those three prompts.

I ran Generate an SVG of a pelican riding a bicycle through the three new models. Here are the results, cheapest to most expensive:

gemini-2.0-flash-lite-preview-02-05

gemini-2.0-flash

gemini-2.0-pro-exp-02-05

Full transcripts here.

I also ran the same prompt I tried with o3-mini the other day:

cd /tmp
git clone https://github.com/simonw/datasette
cd datasette
files-to-prompt datasette -e py -c | \
  llm -m gemini-2.0-pro-exp-02-05 \
  -s 'write extensive documentation for how the permissions system works, as markdown' \
  -o max_output_tokens 10000

Here's the result from that - you can compare that to o3-mini's result here.

# 5th February 2025, 4:37 pm / gemini, llm, google, generative-ai, llm-pricing, ai, llms, pelican-riding-a-bicycle, llm-release, files-to-prompt

microsoft/phi-4. Here's the official release of Microsoft's Phi-4 LLM, now officially under an MIT license.

A few weeks ago I covered the earlier unofficial versions, where I talked about how the model used synthetic training data in some really interesting ways.

It benchmarks favorably compared to GPT-4o, suggesting this is yet another example of a GPT-4 class model that can run on a good laptop.

The model already has several available community quantizations. I ran the mlx-community/phi-4-4bit one (a 7.7GB download) using mlx-llm like this:

uv run --with 'numpy<2' --with mlx-lm python -c '
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/phi-4-4bit")

prompt = "Generate an SVG of a pelican riding a bicycle"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True, max_tokens=2048)
print(response)'

Here's what I got back.

Update: The model is now available via Ollama, so you can fetch a 9.1GB model file using ollama run phi4, after which it becomes available via the llm-ollama plugin.

# 8th January 2025, 5:57 pm / phi, generative-ai, ai, microsoft, llms, uv, pelican-riding-a-bicycle, llm, ollama, llm-release, local-llms

Veo 2 (via) Google's text-to-video model, now available via waitlisted preview. I got through the waitlist and tried the same prompt I ran against OpenAI's Sora last week:

A pelican riding a bicycle along a coastal path overlooking a harbor

It generated these four videos:

Here's the larger video.

# 16th December 2024, 11:31 pm / ai, google, generative-ai, pelican-riding-a-bicycle, text-to-video

<model-viewer> Web Component by Google (via) I learned about this Web Component from Claude when looking for options to render a .glb file on a web page. It's very pleasant to use:

<model-viewer style="width: 100%; height: 200px"
  src="https://static.simonwillison.net/static/cors-allow/2024/a-pelican-riding-a-bicycle.glb"
  camera-controls="1" auto-rotate="1"
></model-viewer>

Here it is showing a 3D pelican on a bicycle I created while trying out BlenderGPT, a new prompt-driven 3D asset creating tool (my prompt was "a pelican riding a bicycle"). There's a comment from BlenderGPT's creator on Hacker News explaining that it's currently using Microsoft's TRELLIS model.

# 13th December 2024, 6:46 pm / web-components, google, generative-ai, ai, 3d, blender, microsoft, claude, pelican-riding-a-bicycle

Sora (via) OpenAI's released their long-threatened Sora text-to-video model this morning, available in most non-European countries to subscribers to ChatGPT Plus ($20/month) or Pro ($200/month).

Here's what I got for the very first test prompt I ran through it:

A pelican riding a bicycle along a coastal path overlooking a harbor

The Pelican inexplicably morphs to cycle in the opposite direction half way through, but I don't see that as a particularly significant issue: Sora is built entirely around the idea of directly manipulating and editing and remixing the clips it generates, so the goal isn't to have it produce usable videos from a single prompt.

# 9th December 2024, 6:35 pm / video, generative-ai, openai, pelican-riding-a-bicycle, ai, text-to-video

New Gemini model: gemini-exp-1206. Google's Jeff Dean:

Today’s the one year anniversary of our first Gemini model releases! And it’s never looked better.

Check out our newest release, Gemini-exp-1206, in Google AI Studio and the Gemini API!

I upgraded my llm-gemini plugin to support the new model and released it as version 0.6 - you can install or upgrade it like this:

llm install -U llm-gemini

Running my SVG pelican on a bicycle test prompt:

llm -m gemini-exp-1206 "Generate an SVG of a pelican riding a bicycle"

Provided this result, which is the best I've seen from any model:

Here's the full output - I enjoyed these two pieces of commentary from the model:

<polygon>: Shapes the distinctive pelican beak, with an added line for the lower mandible.
[...]
transform="translate(50, 30)": This attribute on the pelican's <g> tag moves the entire pelican group 50 units to the right and 30 units down, positioning it correctly on the bicycle.

The new model is also currently in top place on the Chatbot Arena.

Update: a delightful bonus, here's what I got from the follow-up prompt:

llm -c "now animate it"

Transcript here.

# 6th December 2024, 6:05 pm / gemini, llm, ai, llms, svg, releases, google, generative-ai, pelican-riding-a-bicycle, llm-release

QwQ: Reflect Deeply on the Boundaries of the Unknown. Brand new openly licensed (Apache 2) model from Alibaba Cloud's Qwen team, this time clearly inspired by OpenAI's work on reasoning in o1.

I love the flowery language they use to introduce the new model:

Through deep exploration and countless trials, we discovered something profound: when given time to ponder, to question, and to reflect, the model’s understanding of mathematics and programming blossoms like a flower opening to the sun. Just as a student grows wiser by carefully examining their work and learning from mistakes, our model achieves deeper insight through patient, thoughtful analysis.

It's already available through Ollama as a 20GB download. I initially ran it like this:

ollama run qwq

This downloaded the model and started an interactive chat session. I tried the classic "how many rs in strawberry?" and got this lengthy but correct answer, which concluded:

Wait, but maybe I miscounted. Let's list them: 1. s 2. t 3. r 4. a 5. w 6. b 7. e 8. r 9. r 10. y Yes, definitely three "r"s. So, the word "strawberry" contains three "r"s.

Then I switched to using LLM and the llm-ollama plugin. I tried prompting it for Python that imports CSV into SQLite:

Write a Python function import_csv(conn, url, table_name) which acceopts a connection to a SQLite databse and a URL to a CSV file and the name of a table - it then creates that table with the right columns and imports the CSV data from that URL

It thought through the different steps in detail and produced some decent looking code.

Finally, I tried this:

llm -m qwq 'Generate an SVG of a pelican riding a bicycle'

For some reason it answered in Simplified Chinese. It opened with this:

生成一个SVG图像，内容是一只鹈鹕骑着一辆自行车。这听起来挺有趣的！我需要先了解一下什么是SVG，以及如何创建这样的图像。

Which translates (using Google Translate) to:

Generate an SVG image of a pelican riding a bicycle. This sounds interesting! I need to first understand what SVG is and how to create an image like this.

It then produced a lengthy essay discussing the many aspects that go into constructing a pelican on a bicycle - full transcript here. After a full 227 seconds of constant output it produced this as the final result.

I think that's pretty good!

# 27th November 2024, 11:59 pm / llm, ollama, generative-ai, ai, qwen, llms, local-llms, pelican-riding-a-bicycle, llm-reasoning, llm-release

Say hello to gemini-exp-1121. Google Gemini's Logan Kilpatrick on Twitter:

Say hello to gemini-exp-1121! Our latest experimental gemini model, with:

• significant gains on coding performance
• stronger reasoning capabilities
• improved visual understanding

Available on Google AI Studio and the Gemini API right now

The 1121 in the name is a release date of the 21st November. This comes fast on the heels of last week's gemini-exp-1114.

Both of these new experimental Gemini models have seen moments at the top of the Chatbot Arena. gemini-exp-1114 took the top spot a few days ago, and then lost it to a new OpenAI model called "ChatGPT-4o-latest (2024-11-20)"... only for the new gemini-exp-1121 to hold the top spot right now.

(These model names are all so, so bad.)

I released llm-gemini 0.4.2 with support for the new model - this should have been 0.5 but I already have a 0.5a0 alpha that depends on an unreleased feature in LLM core.

I tried my pelican benchmark:

llm -m gemini-exp-1121 'Generate an SVG of a pelican riding a bicycle'

Since Gemini is a multi-modal vision model, I had it describe the image it had created back to me (by feeding it a PNG render):

llm -m gemini-exp-1121 describe -a pelican.png

And got this description, which is pretty great:

The image shows a simple, stylized drawing of an insect, possibly a bee or an ant, on a vehicle. The insect is composed of a large yellow circle for the body and a smaller yellow circle for the head. It has a black dot for an eye, a small orange oval for a beak or mouth, and thin black lines for antennae and legs. The insect is positioned on top of a simple black and white vehicle with two black wheels. The drawing is abstract and geometric, using basic shapes and a limited color palette of black, white, yellow, and orange.

Update: Logan confirmed on Twitter that these models currently only have a 32,000 token input, significantly less than the rest of the Gemini family.

# 22nd November 2024, 6:14 am / vision-llms, gemini, llm, google, generative-ai, ai, llms, logan-kilpatrick, pelican-riding-a-bicycle, llm-release, lm-arena

Recraft V3. Recraft are a generative AI design tool startup based out of London who released their v3 model a few weeks ago. It's currently sat at the top of the Artificial Analysis Image Arena Leaderboard, beating Midjourney and Flux 1.1 pro.

The thing that impressed me is that it can generate both raster and vector graphics... and the vector graphics can be exported as SVG!

Here's what I got for raccoon with a sign that says "I love trash" - SVG here.

That's an editable SVG - when I open it up in Pixelmator I can select and modify the individual paths and shapes:

They also have an API. I spent $1 on 1000 credits and then spent 80 credits (8 cents) making this SVG of a pelican riding a bicycle, using my API key stored in 1Password:

export RECRAFT_API_TOKEN="$(
  op item get recraft.ai --fields label=password \
  --format json | jq .value -r)"

curl https://external.api.recraft.ai/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RECRAFT_API_TOKEN" \
  -d '{
    "prompt": "california brown pelican riding a bicycle",
    "style": "vector_illustration",
    "model": "recraftv3"
  }'

# 15th November 2024, 4:24 am / ai, generative-ai, text-to-image, pelican-riding-a-bicycle, svg

Pelicans on a bicycle. I decided to roll out my own LLM benchmark: how well can different models render an SVG of a pelican riding a bicycle?

I chose that because a) I like pelicans and b) I'm pretty sure there aren't any pelican on a bicycle SVG files floating around (yet) that might have already been sucked into the training data.

My prompt:

Generate an SVG of a pelican riding a bicycle

I've run it through 16 models so far - from OpenAI, Anthropic, Google Gemini and Meta (Llama running on Cerebras), all using my LLM CLI utility. Here's my (Claude assisted) Bash script: generate-svgs.sh

Here's Claude 3.5 Sonnet (2024-06-20) and Claude 3.5 Sonnet (2024-10-22):

Gemini 1.5 Flash 001 and Gemini 1.5 Flash 002:

GPT-4o mini and GPT-4o:

o1-mini and o1-preview:

Cerebras Llama 3.1 70B and Llama 3.1 8B:

And a special mention for Gemini 1.5 Flash 8B:

The rest of them are linked from the README.

# 25th October 2024, 11:56 pm / gemini, anthropic, llama, openai, ai, llms, svg, generative-ai, llm, cerebras, pelican-riding-a-bicycle