LLM Stats vs OpenAI Evals

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

Tagline

LLM Stats

Live leaderboard and side-by-side comparison hub for 300+ frontier LLMs across reasoning, coding, and multimodal benchmarks.

OpenAI Evals

OpenAI's open-source framework for benchmarking LLMs against a shared registry of evaluations.

Pricing

LLM Stats

Free· Free to browse; underlying model usage billed by each provider

OpenAI Evals

Free· Free (MIT); you pay OpenAI API costs for eval runs

Free trial

LLM Stats

Yes

OpenAI Evals

Yes

API

LLM Stats

Yes

OpenAI Evals

Not listed

Platforms

LLM Stats

api

OpenAI Evals

cli

Open source

LLM Stats

Not listed

OpenAI Evals

Yes · NOASSERTION

GitHub stars

LLM Stats

—

OpenAI Evals

19,524

checked 2026-09-29

Last GitHub push

LLM Stats

—

OpenAI Evals

2026-04-14

First commit

LLM Stats

—

OpenAI Evals

2023-01

Company

LLM Stats

—

OpenAI Evals

OpenAI

Model used

LLM Stats

Multi-model

OpenAI Evals

OpenAI GPT models (extensible)

Best for

LLM Stats

Pick LLM Stats if you need a fast, opinion-free snapshot of where every major model lands on price, speed, and standard benchmarks before you commit to one.

OpenAI Evals

Pick OpenAI Evals if you want a free, code-first, reproducible eval harness for GPT-based systems with a large registry of ready-made benchmarks.

Not for

LLM Stats

Skip it if you need rigorous, task-specific evals on your own data or audit-grade methodology disclosure for procurement.

OpenAI Evals

Skip it if you want a polished hosted dashboard, non-OpenAI-first provider support, or a no-code eval workflow for PMs.

Editorial score

LLM Stats

7.9 / 10

OpenAI Evals

8.1 / 10

Use cases

LLM Stats

model-comparisonbenchmark-trackingcost-analysiscoding-arenamodel-selection

OpenAI Evals

llm-benchmarkingregression-testingmodel-graded-evalprompt-evaluationcustom-evals

Pros

LLM Stats

Covers 300+ models with both benchmark scores and live latency/throughput
Side-by-side price-per-million-token columns make cost comparison trivial
Task-specific leaderboards (coding, math, research) instead of one global rank
Interactive arenas let you sanity-check outputs before committing to a provider

OpenAI Evals

Large public registry of ready-to-run evals
MIT-licensed and fully open source
Supports basic, model-graded, and custom evals
Canonical format many published benchmarks adopt
W&B and Snowflake logging out of the box

Cons

LLM Stats

Relies on public benchmarks that frontier labs increasingly train against
Leaderboard itself is not open source and methodology is lightly documented
No first-party cost calculator or workload simulator for real traffic patterns

OpenAI Evals

Registry and defaults are OpenAI-centric
Model-graded evals can rack up API costs fast
UX is CLI + YAML, no hosted dashboard
Less actively iterated than commercial rivals

Website

LLM Stats

llm-stats.com

OpenAI Evals

github.com

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.

Pick LLM Stats if

✅ Covers 300+ models with both benchmark scores and live latency/throughput
✅ Side-by-side price-per-million-token columns make cost comparison trivial
✅ Task-specific leaderboards (coding, math, research) instead of one global rank
✅ Interactive arenas let you sanity-check outputs before committing to a provider

Pick OpenAI Evals if

✅ Large public registry of ready-to-run evals
✅ MIT-licensed and fully open source
✅ Supports basic, model-graded, and custom evals
✅ Canonical format many published benchmarks adopt

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