Vercel autofixer-01

autofixer-01 by Vercel excels in the Coding category, where it ranks #334 with a composite score of 32/100. Vercel's proprietary model for automatic error fixing in Next.js applications. Achieves 93.87% error-free rate on production deployments. It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

autofixer-01 is priced at $0.00 per million input tokens and $0.00 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, autofixer-01 holds rank #334 out of 341 models tracked. Its quality rank is #334 and adoption rank is #334. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

autofixer-01 has been evaluated across 5 different signals. Its strongest areas include Capabilities (17/100), Pricing (100/100), Context Window (0/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 32/100 reflects a weighted combination of all tracked signals.

autofixer-01 is available as a free, open-source model. You can run it locally or use it through various hosting providers. Some API providers may charge for hosted inference, so check individual provider pricing.

autofixer-01 supports streaming responses. These capabilities determine which workflows and integrations the model can handle natively.

autofixer-01 was developed by Vercel. It was released on October 1, 2025. You can access it through Vercel's API. Check our provider page for all models from Vercel and how they compare against each other.

Pick autofixer-01 when you need a budget-friendly option for high-volume, simpler tasks where you prioritize cost over peak performance. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access autofixer-01 through Vercel's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.