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+# Evaluating Claude 3 at converting screenshots to code
+
+[video running both]
+
+Claude 3 dropped yesterday, claiming to rival GPT-4 on a wide variety of tasks. I maintain a very popular open source project (41k+ Github stars) called “screenshot-to-code” (this one!) that uses GPT-4 vision to convert screenshots/designs into clean code. Naturally, I was excited to see how good Claude 3 was at this task!
+
+## Evaluation Setup
+
+I don’t know of a public benchmark for the “screenshot to code” task so I created simple evaluation set up for the purposes of testing\*:
+
+- **Evaluation Dataset**: 16 screenshots with a mix of UI elements, landing pages, dashboards and popular websites.
+- **Evaluation Metric**: Replication accuracy, as in “How close does the generated code look to the screenshot?” There are other metrics that are important for sure but this is by far the #1 thing that most developers and users of the repo care about. Each output is subjectively rated by a human (me!) on a rating scale from 0 to 4. 4 = very close to an exact replica while 0 = nothing like the screenshot. With 16 screenshots, the maximum any model can score is 64. I like to compare the percentage of the maximum possible score for each run.
+
+\*I’ve used it mostly for testing prompt variations until now since no model has come close to GPT-4 vision yet.
+
+To make the evaluation process easy, I created [a Python script](https://github.com/abi/screenshot-to-code/blob/main/backend/run_evals.py) that runs code for all the inputs in parallel. I also have a simple UI to do a side-by-side comparison of the input and output, shown below.
+
+[video of input/output]
+
+## Results
+
+Quick note about what kind of code we’ll be generating: currently, screenshot-to-code supports generating code in HTML + Tailwind, React, Vue, and several other frameworks. Stacks can impact the replication accuracy quite a bit. For example, because Bootstrap uses a relatively restrictive set of user elements, generations using Bootstrap tend to have a distinct "Bootstrap" style.
+
+I only ran the evals on HTML/Tailwind here which is the stack where GPT-4 vision tends to perform the best.
+
+Here are the results (average of 3 runs for each model):
+
+- GPT-4 Vision obtains a score of **65.10%** - this is what we’re trying to beat
+- Claude 3 Sonnet receives a score of **70.31%**, which is ~7.5% better.
+- Surprisingly, Claude 3 Opus which is supposed to be the smarter and slower model scores worse than both GPT-4 vision and Claude 3 Sonnet, coming in at **61.46%**. Strange result.
+
+Overall, a very strong showing for Claude 3! Obviously, there's a lot of subjectivity involved in this evaluation but Claude 3 is definitely on par with GPT-4 Vision, if not better. It's also a lot less lazy than GPT-4 Vision.
+
+You can see the results for a run of Claude 3 Sonnet here. You can see the results for a run of GPT-4 Vision here.
+
+Some other notes:
+
+- The prompts used are optimized for GPT-4 vision. I played around with adjusting the prompts for Claude and that did yield a small improvement. Nothing game-changing and potentially not worth the trade-off of maintaining two sets of prompts.
+- All these models excel at code quality - the quality is usually comparable to a human or better
+- All the models seem to struggle with side-by-side layouts (the struggle is mostly in seeing, not in implementing) TODO: add iamge
+- Claude 3 is much less lazy than GPT-4 Vision. TODO: Hacker News example
+- Claude 3 Sonnet is faster and more steerable
+
+Overall, I'm very impressed with Claude 3 Sonnet as a multimodal model for this use case. I've added it as an alternative to GPT-4 Vision in the open source repo (hosted version update coming soon). I'm sure it's possible to get Opus to be quite good as well.
+
+If you’d like to contribute to this effort, I have some documentation on [running these evals yourself here](https://github.com/abi/screenshot-to-code/blob/main/Evaluation.md). If you know of a good tool for running evals like this (image input, code output), it would save me a lot of effort from having to build the tool from scratch.