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 OpenRouter's OpenAI-compatible endpoint - deliberately not through our own gateway, so the numbers do not depend on our infrastructure - executed against real test cases, and measured for token usage 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).