GoldieBench deep dive
LongCat-2.0's full benchmark breakdown.
The open 1.6T MoE that builds — a frontier coder trained on non-Nvidia ASIC superpods. Every number below is either one of my own judged one-shot builds - playable on this site - or an outside result with its source linked. Nothing display-only, nothing vibes.
01 · The headline numbers
Provisional flag. Part of this model's run hit infrastructure failures that are being re-run; its average is still settling. Scores shown are real judged builds only.
02 · Every benchmark, bar by bar
All 4 scored tasks, best first. Hover for the judge's comment; click through for every model's build on that task. Or overlay other models in the interactive graphs.
03 · Where it wins - with the judge's own words
“One-shot 15KB three.js snow open-world — snow-capped mountains + 30 low-poly pines, 3000-particle falling snow, first-person glowing sword, fog. Real WASD+mouse+sprint controls, terrain-follow. verify-move: walks+looks, canvas 1440x810, 0 errors. Flawless first try — no patch.”
“One-shot 23KB open-world explorer (the richest of the four) — rolling displaced terrain, snow mountains, a stone watchtower, 20+ conifers, boulders, grass, clouds, and terrain-height following. Real WASD+mouse. verify-move: walks+looks, 0 errors.”
“One-shot 9KB torch-lit stone dungeon corridor — pillars, barrels, a chest, 6+ flickering torch PointLights, fog. Real WASD+mouse controls. verify-move: walks+looks, 0 errors. Lit + atmospheric (a touch over-bright orange).”
04 · Where it struggles - quoted, not hidden
Boards that hide the weak rows are brochures. These are LongCat-2.0's lowest scored builds, verdicts unedited:
“One-shot 9KB Minecraft-style voxel world — 16x16 grass/dirt/stone cubes, voxel trees, day/night sky, raycast break+place, real WASD+mouse. verify-move: walks+looks, 0 errors. Built the full world one-shot but the initial camera yaw faced away (sky-only) — a one-line framing patch (yaw 0, pitch -0.5) pointed it at the terrain.”
05 · Category breakdown vs the whole field
Games (4 tasks)
Others (0 tasks)
Pages (0 tasks)
Sims (0 tasks)
Visuals (0 tasks)
06 · Outside signals - every row sourced
My bench measures one thing: judged one-shot builds. These outside rows measure other things - kept separate, never blended into the GoldieBench average, every value linked to where it comes from.
Vendor-published numbers
| Benchmark | Result | Source |
|---|---|---|
| SWE-bench Pro | 59.5 - edges GPT-5.5's 58.6 | www.marktechpost.com |
| Terminal-Bench 2.1 | 70.8 | www.marktechpost.com |
| SWE-bench Multilingual | 77.3 | www.marktechpost.com |
| BrowseComp | 79.9 | www.longcatai.org |
| Verification status | all vendor-reported from Meituan's internal suite; independent confirmation pending | www.longcatai.org |
From the source guides
| Measure | Result | Source guide |
|---|---|---|
| Terminal-Bench 2.1 | 70.8 | /longcat-2-0 |
| SWE-bench Multilingual | 77.3 | /longcat-2-0 |
| BrowseComp | 79.9 | /longcat-2-0 |
| GPQA-diamond | 88.9 | /longcat-2-0 |
| IFEval | 90.0 | /longcat-2-0 |
07 · Head-to-head records
Not enough shared scored tasks yet.
08 · Methodology + honest limits
Every GoldieBench score is a one-shot, single-file build from an identical prompt - no retries, no hand-fixing - rendered for real, screenshotted, and scored 0-10 by one judge model on one rubric across the whole field. Failures score as failures. What this bench does NOT measure: multi-turn agent work, long-context recall, or API latency - that is what the sourced outside rows are for. Full method: /methodology.
Frequently asked questions
01How does LongCat-2.0 perform on GoldieBench?
LongCat-2.0 averages 8.12/10 across 4 scored one-shot build tasks, ranking #4 of 17 ranked frontier models, with 0 task golds, 0 silvers and 1 bronzes. Every score is a real judged build you can open and play on this site.
02What is LongCat-2.0 best at?
Its strongest scored build is Dragonrealm at 8.5/10. By category it averages Game 8.1 on the bench.
03How much does LongCat-2.0 cost?
LongCat-2.0 is open-sourced (weights on Hugging Face + GitHub) and served via the longcat.chat web chat plus an OpenAI-compatible API (model id 'LongCat-2.0' at api.longcat.chat/openai/v1). It's a 1.6T-parameter MoE with ~48B activated per token, trained entirely on AI ASIC superpods (>50K accelerators, 35T+ tokens, no rollbacks). Note: the direct API key we were handed shipped with zero token quota ('Token 额度不足'), so every build here was run thr
Source ledger
- 01Official vendor site: longcat.chatlongcat.chat
- 02SWE-bench Prowww.marktechpost.com
- 03BrowseCompwww.longcatai.org
- 04Terminal-Bench 2.1agentos.guide
Every model's breakdown
Run this stack yourself.
Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.