Free tool / Updated 2026-07-06

LLM Benchmark Decoder

Pick an AI benchmark and get a plain-English read on what it measures, where it saturates, and how to treat Claude or GPT vendor claims.

Citable baseline

Benchmark facts

Dated facts with primary-source links where the underlying source is available.

500 SWE-Bench Verified instances in the human-filtered subset. Primary source: SWE-Bench
1,865 Total SWE-Bench Pro tasks across public, private, and held-out subsets. Primary source: Scale Labs
System + model Terminal-Bench public rows should be read as agent-plus-model results. Primary source: arXiv

Interactive decoder

Pick the benchmark behind a claim

The output is intentionally conservative: it explains what the benchmark is allowed to prove, what it should not be used for, and what a Claude-vs-GPT citation must preserve.

Choose the exact benchmark family named in a release post or leaderboard before copying a score into a report.

How to use benchmark claims

Benchmarks are not interchangeable. SWE-Bench Pro is a software-repair signal; Terminal-Bench is closer to command-line agent behavior; MMLU is broad academic knowledge; ARC-AGI is a reasoning and generalization stress test. A responsible Claude-vs-GPT comparison names the benchmark before naming the winner.

The decoder also flags saturation. A saturated or heavily public benchmark can still be useful historically, but it should not carry a frontier procurement decision alone.

FAQ

Which benchmark should I trust for coding agents?

Start with SWE-Bench Pro for software-repair realism and Terminal-Bench for terminal-agent workflows. Neither replaces a private eval on your own repos.

Are academic benchmarks useless for procurement?

No. They are useful directional signals, but they transfer poorly when your workload depends on tools, documents, latency, or cost-per-accepted-result.

Keep reading

Related benchmark methods

Internal links are selected to keep benchmark, tool, and methodology context together.

Report

SWE-Bench Pro vs Verified

Use Verified for continuity and Pro for current frontier signal; when scores conflict, inspect dataset, scaffold, cost caps, and whether the result is model-only or agent-plus-model.

Report

Terminal-Bench Agents

Terminal-Bench is strongest when you care about the shipped agent experience, not when you want to isolate a base-model capability.

Tool

Model comparison

Filter Claude, GPT, and benchmark-reference models by provider, access, price, context window, and published coding benchmark evidence.

Method

Benchmark methodology

See how Claude Reports reads provenance, harness, effort, contamination, uncertainty, and cost.