Free
$0Free plan available.
Make Real is a tool within tldraw that uses AI to transform UI drawings into functional websites. Users draw shapes, select them, and click 'Make Real' to generate a working website. It supports multiple AI model providers like OpenAI, Anthropic, and Google, allowing users to input their API keys for each provider. The tool enables iterative design, where users can continue drawing on top of existing websites to refine and expand their creations.
To use Make Real, first input your API keys for your preferred model providers (OpenAI, Anthropic, or Google). Next, draw your UI shapes within tldraw, select the elements, and click 'Make Real'. You can then continue drawing and refining the design to iterate on the generated website.
You must create an account with OpenAI or Anthropic and set up billing. Once registered, navigate to the API keys section within your account settings to generate a new key.
A standard ChatGPT subscription is separate from API access. To use Make Real, you must generate an API key through the OpenAI developer platform and ensure your account has sufficient credits.
tldraw is an infinite canvas SDK built for React. You can explore their collaborative whiteboard demo at tldraw.com.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Make Real.
Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and GPT 5.4 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Opus across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Sonnet across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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