Openbenchmarks
Openbenchmarks is an independent benchmarking platform that rigorously evaluates AI search APIs and related tools through reproducible, open-source tests. It ranks vendors on metrics like precision, recall, F1, latency, and cost across use cases such as web search for coding agents, company enrichment, and document processing. The platform publishes live cohorts, like its 282-company enrichment test, with public data and code on GitHub.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Engineering and product teams considering whether to build or buy AI-related search capabilities lack independent, verifiable data on how different APIs actually perform in real-world scenarios. Vendor claims and anecdotal comparisons are unreliable, making it difficult to justify technical decisions and allocate engineering resources effectively.
Solution
Openbenchmarks provides independent, open-source benchmarks that rigorously evaluate AI search APIs and related tools for specific use cases. Each benchmark is designed to be verifiable and reproducible, running on a documented date and ranking options on metrics like precision, recall, F1, latency, and cost. The platform covers scenarios such as web search for coding agents, multi-turn company search, company news retrieval, and document processing with redlined contracts. Results are published openly, enabling teams to compare vendors directly based on concrete performance data.
Target Audience
Primary users are engineering leaders and technical evaluators at companies deciding between building in-house search solutions and purchasing commercial search APIs, particularly for AI applications.
Features
- Benchmark suite covering web search for coding agents, multi-turn company search, company news, document processing, company lookalikes, and company enrichment
- Reproducible test methodology with explicit run dates, documented evaluation criteria, and public data and code on GitHub
- Ranking metrics including precision, recall, F1, exact-set accuracy, enrichment success rate, field accuracy, field coverage, latency, and cost
- Live cohorts with human-verified ground truth, such as 282 companies across stable-large, long-tail, subsidiary, and verified-rebrand slices
- Workflow-specific recommendations, such as best API for CRM writeback, prospecting, complete profiles, or real-time product flows
- Tests that combine search APIs with agent workflows, such as grounding patches in retrieved URLs