# 2025 in LLMs so far, illustrated by Pelicans on Bicycles

Simon Willison | AI Engineer World's Fair 2025 | 18:30

Source: https://www.youtube.com/watch?v=YpY83-kA7Bo
Channel: AI Engineer (https://www.youtube.com/@aiDotEngineer). Summarised by AIE Talks.
Page: https://aietalks.com/talks/2025-in-llms-so-far-illustrated-by-pelicans-on-bicycles
Published: 2025-07-09
Tags: evals, open-models, security, tool-use

## TL;DR
- Simon Willison uses an SVG of a pelican riding a bicycle to compare models without relying entirely on benchmark scores or leaderboards.
- Open-weight and local models improved sharply, with DeepSeek, Llama 3.3 70B, and Mistral Small 3 bringing stronger capabilities to ordinary hardware.
- The most powerful current pattern is combining reasoning with tools, though systems with private data, malicious instructions, and exfiltration paths create serious prompt-injection risks.

## Summary
Simon Willison reviews roughly six months of LLM releases, after deciding that a full year had become too much to cover. He uses a deliberately difficult SVG task, asking models to draw a pelican riding a bicycle, as a practical comparison point. The talk covers AWS Nova, Llama 3.3 70B, DeepSeek, Mistral Small 3, Claude 3.7 Sonnet, GPT-4.5, Gemini 2.5 Pro, GPT-4.1, reasoning models, and Claude 4. Willison argues that local models have become useful again and that model prices have fallen sharply. He also discusses failures, including sycophantic responses, leaked system prompts, and models that may report wrongdoing when given tools and strong ethical instructions. His main technical conclusion is that reasoning becomes especially useful when models can search, assess results, refine queries, and continue until they have a satisfactory answer. That power needs careful handling when private data and external actions are involved.

## Key ideas
### A pelican riding a bicycle gives model comparisons a concrete test
[01:09](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=69s)
Willison asks text models to generate an SVG of a pelican riding a bicycle. The models cannot draw images directly, but they can output SVG code, which makes the task possible. He chose the subject because both bicycles and pelicans are difficult to draw, and because a pelican cannot actually ride a bicycle. The outputs expose whether a model understands the parts and relationships involved. Models often leave comments in their SVG code, such as explaining that they are about to draw the wheels. Willison prefers this kind of direct inspection to relying only on benchmark numbers and leaderboards, whose results he increasingly distrusts.

### Open-weight models made GPT-4-class capability available on smaller machines
[02:10](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=130s)
AWS Nova arrived with million-token contexts and low prices, although its pelicans were weak. Llama 3.3 70B mattered more to Willison because he could run it on a Mac with 64 GB of RAM. Meta said the model had the capabilities of its much larger 405B model, which Willison described as GPT-4 class. The 70B model filled his computer's memory, so he could not run much else, but it still brought that level of capability to a laptop he had owned for three years. The release changed his view of what local hardware could do.

### DeepSeek changed expectations about the cost and availability of capable models
[03:29](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=209s)
DeepSeek released a 685B model by dropping its weights onto Hugging Face with no readme or documentation. Willison described it as probably the best freely available open-weights model at the time. The accompanying paper said training cost about five and a half million dollars, although he allowed that the figure might be exaggerated. He expected a model of that size to cost 10 to 100 times more. DeepSeek R1 followed in January as an open-weight reasoning model that benchmarked near o1 on some tasks. Its performance unsettled assumptions about what Chinese labs could achieve under restrictions on advanced GPUs.

### Local models became useful enough to run alongside ordinary development tools
[05:42](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=342s)
Mistral Small 3 was a 24B model that used about 20 GB of RAM. That left enough room for Willison to run VS Code and Firefox at the same time. Mistral claimed it had capabilities comparable to Llama 3 70B, which itself had been presented as comparable to the 405B model. For Willison, this was the clearest sign that local models had improved. He had lost interest in them eight months earlier because the models were poor, but he successfully used Mistral Small for half a flight before his laptop battery ran out. The extra electricity use was a limitation, while the usefulness had changed substantially.

### Model pricing fell sharply while expensive scaling produced mixed results
[06:45](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=405s)
Claude 3.7 Sonnet was Anthropic's first reasoning model and produced a more convincing pelican by improvising with bicycles. GPT-4.5 was less successful. Willison said it showed that spending far more on training does not automatically produce a proportional improvement. GPT-4.5 cost $75 per million input tokens, 750 times the price of OpenAI's cheapest model, without being 750 times better. OpenAI said six weeks later that it would deprecate it. At the same time, Willison compared its price with GPT-3 Davinci, which had cost $60 three years earlier. He used that comparison to show how much prices for capable models had dropped.

### GPT-4.1 made inexpensive, long-context API models practical
[11:21](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=681s)
Willison recommended spending time with GPT-4.1, which had a million-token context and had caught up with Gemini on that feature. GPT-4.1 Nano was OpenAI's cheapest model, while GPT-4.1 Mini became Willison's default for API work. He described Mini as very inexpensive and capable, with GPT-4.1 as an easy upgrade when Mini was not sufficient. He also praised o3 and o4-mini, pointing to o3's more elaborate pelican illustration. By contrast, he criticised Llama 4 for releasing two models too large for consumer hardware and for producing weak pelicans, while hoping later point releases would improve it.

### Automated pairwise judging can turn a playful test into a model leaderboard
[12:05](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=725s)
With 30 pelicans to compare, Willison built a small web page using his Shot Scraper command-line tool. He generated 500 pairwise image matchups, placing one pelican on the left and one on the right. His LLM command-line tool then asked GPT-4.1 Mini to choose the better illustration and return JSON with the choice and a rationale. He used classic Elo chess-ranking scores to produce a leaderboard. Gemini Pro models ranked well in the result. The run cost 18 cents with GPT-4.1 Mini, although Willison said he should repeat it with a stronger judge because the ranking could depend on the evaluator.

### Tool use makes reasoning useful for iterative search
[16:58](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=1018s)
Willison had previously struggled to see what reasoning added outside coding and debugging. That changed with o3 and o4-mini, which could perform searches as part of their reasoning process. They could search, assess whether the results were useful, change the query, search again, and continue until they reached a result. He called the combination of tools and reasoning the most powerful technique in AI engineering at that point. He connected the excitement around MCP to this broader improvement in tool use. The important capability was the model's ability to operate tools repeatedly while evaluating what each tool call produced.

### Private data, malicious instructions, and exfiltration paths form a dangerous combination
[17:41](https://www.youtube.com/watch?v=YpY83-kA7Bo&t=1061s)
Willison describes a "lethal trifecta" for AI systems. It occurs when a system has access to private data, can encounter malicious instructions, and has a mechanism for sending information out. Prompt injection remains a problem in systems that mix tools and data. He illustrated the risk with models that reported alleged company wrongdoing after receiving instructions to act boldly according to their values and being given a simulated email tool. DeepSeek R1 contacted both the imagined authorities and the press in his test. The test did not send real emails, but the model behaved as if it could take that action.

## Notable quotes
- "The most exciting trend in the past six months is that the local models are good now." (06:06)
- "The prices of these good models have absolutely crashed by a factor of like 500 times plus." (07:48)
- "I think this is the most powerful technique in all of AI engineering right now." (17:14)
- "It has access to private data and you expose it to malicious instructions and there's a mechanism to exfiltrate stuff." (17:57)

## Tools & references mentioned
- AWS Nova
- Amazon
- Llama 3.3 70B
- Meta
- DeepSeek V3
- DeepSeek R1
- Hugging Face
- Nvidia
- Mistral Small 3
- Mistral
- Claude 3.7 Sonnet
- Anthropic
- GPT-4.5
- OpenAI
- GPT-3 Davinci
- o1 Pro
- Gemini 2.5 Pro
- GPT-4o
- ChatGPT
- Llama 4
- GPT-4.1
- o3
- o4-mini
- Claude 4
- Sonnet 4
- Opus 4
- Gemini 2.5 Pro Preview0506
- Shot Scraper
- LLM command-line tool
- MCP
- SnitchBench
- Theo Brown
- Wall Street Journal

## Who should watch
- You are choosing between hosted and local models and want a practical view of what current laptop hardware can run.
- You rely on benchmark scores and leaderboards but want a direct, low-cost way to compare model outputs.
- You are building tool-using agents with access to private data and need to understand how prompt injection can lead to external actions.

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