Better LLMs for software development
Symflower helps you build better software by pairing static, dynamic and symbolic analyses with LLMs. The robustness of deterministic analyses combined with the creativity of LLMs allows for higher quality and rapid software development.
Find the right LLM for your project
The “best model” is not necessarily the best for your programming language, framework or use case.
We create evaluations for your scenarios to benchmark hundreds of LLMs to find the best-fitting model for your environments, workflows and requirements.
Our leaderboard compares ~80 popular models across 10 categories with 50 functional and non-functional metrics.
Fix disadvantages of LLMs
Apply automatic pre- and post-processing to make a 27B LLM beat a 405B model.
Investing a few milliseconds in automatic code repair and fixing of linting problems improves the usefulness and quality of LLM-generated code dramatically: on average +26% in functional score.
Give LLMs the right context
Suppress hallucinations through optimal RAG (Retrieval-Augmented Generation).
Some tasks are unsolvable without context. Providing the right information and structure for a task allows LLMs to avoid silly mistakes and improves their results dramatically.
Continuously benchmark
Make sure your use cases work forever and with the latest models.
We continuously run benchmarks on real-world use cases to make sure that you can always upgrade when old models are removed.
Refine training and fine-tuning
Curate high-quality data and accelerate fine-tuning for your pre-release models.
We provide deep dive reports to identify problems. Including automated fixes for these problems to improve your feedback loop or as quick-to-deploy post-processing.
Make LLMs work faster and smarter
Function calling needs to be exact and fast to let models rethink their results.
We provide a single binary to configure and invoke tooling for every environment and action. For example, test execution times for changes are 29% shorter on average.
