Symflower

Symflower offers a unique approach to finding the best LLM (Large Language Model) for your project by evaluating and benchmarking hundreds of models across various programming languages, frameworks, and use cases. It compares approximately 80 popular models using over 50 functional and non-functional metrics, helping developers choose the best fit for their environment.

Symflower significantly enhances LLM-generated code quality through automatic pre- and post-processing. By addressing issues such as linting problems, Symflower boosts the functional score of code by an average of 26%, improving the usefulness and reliability of generated code.

The software applies Retrieval-Augmented Generation (RAG) to suppress hallucinations and provide the right context for tasks. By supplying LLMs with structured information, Symflower improves accuracy and minimizes errors, ensuring better quality results from LLMs.

Symflower runs real-time benchmarks on real-world use cases to ensure that your LLMs continue to work optimally with the latest models. This feature helps developers stay updated as new models are introduced and older ones are deprecated, ensuring long-term reliability and performance.

Symflower provides tools for refining the training process, including curated high-quality data for model fine-tuning. It offers deep-dive reports to pinpoint and solve issues within your models, along with automated fixes for rapid feedback and post-processing.

Symflower optimizes function calling to ensure models operate faster and more efficiently. The software includes a binary that configures and invokes tooling for various environments and actions, improving test execution times and reducing delays—on average, test times are 29% shorter.

Symflower integrates seamlessly with popular development environments such as IntelliJ IDEA, VS Code, and Android Studio, as well as with CLI/CI tools. This broad integration ensures developers can use Symflower’s features within their existing workflows.

Compare 80+ LLMs across 10 categories, with more than 50 metrics to evaluate each model's performance in specific use cases.

Apply automatic fixes for code quality issues such as linting errors, improving the functional score of generated code by +26% on average.

Enhance LLM performance by providing the necessary context and structure for tasks, improving result accuracy and reducing hallucinations.

Keep LLMs up-to-date by running continuous benchmarking on real-world use cases, ensuring that the latest models are always available for your projects.

Curate high-quality training data and streamline fine-tuning for your models to improve performance and speed up the pre-release phase.

Optimize function calls and reduce delays by using a single binary to configure tooling for all environments, resulting in a 29% reduction in test execution times.

Supports popular development environments like IntelliJ IDEA, VS Code, and Android Studio, as well as CLI/CI integrations, making it easy to incorporate into existing workflows.

Use post-processing to apply fixes and enhancements automatically to improve LLM-generated code and enhance its usefulness.

Symflower provides detailed reports on model performance, identifying areas of improvement and offering solutions to optimize code generation.

Track the performance of LLMs continuously, ensuring that model results are always relevant, accurate, and functional across all stages of development.