Real-world LLM evaluation
Evaluate models on your real prompts
No fixed benchmarks. Run any prompt instantly or in batch — LLM-as-Judge scoring plus user acceptance weighting produces reproducible, auditable cross-scenario rankings.
Prompt → parallel models → judge scores & acceptance
prompt
Refactor this React hook for SSR…
Model A
useEffect → useLayoutEffect…
8.4 / 10
Model B
useEffect → useLayoutEffect…
6.1 / 10
- Open algorithm docs
- Versioned rules
- Auditable prompts & acceptance
- Zero-code web UI
Who we are
BenchmarkFree is operated at benchmarkfree.dev. We build open, reproducible tools for comparing language models on real prompts — not static test sets — so developers and teams can see how models behave in practice.
Three steps to a real-world eval
From prompt to leaderboard — transparent end to end
- 01
Pick your prompt
Public library or your own prompts — real scenarios, not canned benchmarks.
- 02
Run models in parallel
Configure once, compare many models; streaming outputs with TTFT and latency.
- 03
Judge scores + your acceptance
Automated judge scoring; your acceptance feeds leaderboard weighting.
Why skip fixed benchmarks?
Static sets get gamed and drift from production. BenchmarkFree brings eval back to real use.
Prompt source
Fixed benchmarks
Static, closed test sets
BenchmarkFree
Your real prompts, open & reproducible
Real-world fit
Fixed benchmarks
Easy to overfit; misaligned with production
BenchmarkFree
Test what you use, not what was tested
Ranking transparency
Fixed benchmarks
Opaque ranking logic
BenchmarkFree
Full public algorithm & listing rules
Getting started
Fixed benchmarks
Scripts, frameworks, or paper reproduction
BenchmarkFree
Zero-code web — sign in and go
Core capabilities
- Real-world scenario evals: use your own prompts or production issues instantly, not bound to fixed benchmarks
- Quick Eval: single prompt, multi-model instant comparison
- Batch Eval: multi-prompt eval tasks, aggregated analysis, and result export
- LLM-as-Judge scoring: configurable dimensions; manual scoring disabled
- Public Leaderboard: cross-scenario percentile aggregation with transparency documentation
Built for
Indie developers
Compare candidate models in 30 seconds on real code prompts — not HumanEval scores.
Tech PMs & architects
Reproducible comparison data and public algorithm docs for team decisions.
AI tooling researchers
Custom prompts, batch tasks, exports — no eval framework from scratch.
Public quality leaderboard
Cross-scenario rankings from public quick evals and batch tasks — judge scores with acceptance weighting.
Early accumulation phase — low-sample models show as "observing".
Transparency is the product
Global ranking algorithm, constants, and listing rules are fully documented. Every rank change traces to an algorithm version — no hidden formulas or sample gates.
Read global leaderboard algorithmReady to pick models on real prompts?
Sign in to run quick evals, manage prompts and models, or publish tasks to the leaderboard.
Contact
Questions, feedback, privacy requests, or account deletion: [email protected]