DataLLM LabAbout DataLLM Lab
DataLLM Lab is two things: a unified LLM gateway that lets you call 300+ models through one OpenAI- and Anthropic-compatible endpoint, and an evidence-based blog about what those models actually cost and can do. This page explains who we are, who writes the articles, and the standards we hold every number to - because advice about model selection is only as good as the trust behind it.
What DataLLM Lab is
DataLLM Lab is a unified LLM gateway: one OpenAI- and Anthropic-compatible API key that routes to 300+ models — Claude, GPT, Gemini, GLM, DeepSeek, Qwen and more — with live price comparison and automatic failover. Instead of juggling separate SDKs, keys, and billing dashboards per provider, you integrate once and switch models with a string. The same routing makes it easy to send routine work to a cheap model and escalate hard tasks to a flagship.
Why we run the blog
Choosing a model is mostly a cost-versus-capability decision, and the public information is poor: vendor benchmarks are marketing, pricing pages hide real per-task spend, and most "best LLM" articles recycle the same press releases. Because we operate a gateway, we call these models constantly and see what they actually cost and where they break. The blog exists to turn that into evidence other developers can use — hands-on tests, real numbers, and honest trade-offs.
Who writes it
The articles are written, and the benchmarks run, by Kevin Fan — the builder of the DataLLM Lab gateway. The reviews come from first-hand use and first-party testing: models are called through the same infrastructure we run in production, executed against real test cases, and measured for real billed cost and latency. You can follow the underlying work on GitHub at github.com/kevinwowo. When we test a model, we mean we ran it — our methodology page documents exactly how.
Editorial standards
First-party numbers
- Pass rates, costs and latencies are measured by us, not copied from vendor decks.
Sources labeled
- Vendor claims are marked "vendor-reported"; independent figures are attributed (e.g. Artificial Analysis).
No pay-for-ranking
- Rankings come only from measured results — including when a cheap model wins.
No fabrication
- Every figure is sourced or measured; if we can't verify it, we say so or leave it out.
Contact & corrections
Accuracy is the whole point, so corrections are welcome. If you think a number is wrong, tell us and we'll re-check and update it. The fastest way to evaluate our advice is to test it yourself: get an API key and run the same models on your own tasks. For the author's other work, see github.com/kevinwowo.
One key, 300+ models
Try the gateway the blog is built on — Claude, GPT, Gemini, GLM, DeepSeek and more behind one OpenAI-compatible endpoint, with live price comparison.
FAQ
What is DataLLM Lab?
A unified LLM gateway — one OpenAI- and Anthropic-compatible key routing to 300+ models with live price comparison and failover. The blog is its research arm.
Who writes the articles?
Kevin Fan, who builds the DataLLM Lab gateway — so reviews come from hands-on use and first-party testing, not rewritten press releases. Work: github.com/kevinwowo.
Is the content independent?
Yes — our numbers are measured first-party, vendor figures are labeled, no pay-for-ranking, and we report honestly even when a cheap model beats an expensive one. Methodology is public.
How does DataLLM Lab make money?
It's a commercial gateway — it earns when developers route traffic through it, which aligns our incentive with honest, accurate model-selection advice.
How can I contact you?
Through the DataLLM Lab site, by signing up for an API key, or via the author on GitHub (github.com/kevinwowo). Corrections welcome.
What do you write about?
Hands-on model reviews with executed benchmarks, real cost-per-task and pricing, comparisons, and practical guides (routing, failover, cheaper coding agents).