Real head-to-head · same prompt, one shot
Opus 4.8 vs LongCat-2.0
The reasoning king — deepest thinking, premium price. vs The open 1.6T MoE that builds — a frontier coder trained on non-Nvidia ASIC superpods.
Head-to-head verdict: LongCat-2.0 wins 3–1.
What I tested — same prompt, two models
I run the same fixed prompt set through every new model the day it drops — same string, one shot, single HTML file out — and I score the result 0–10 on whether it ran, how close it hit the brief, and how good it looked. Below is what came out when I gave the exact same prompts to Opus 4.8 and LongCat-2.0, side by side, on 4 shared tasks inside the Agent Operating System.
Both models were given identical prompts inside the Agent Operating System — no help, no iteration, no "best of N" tricks. I run each prompt once, save the HTML file the model produces, and score it 0–10 on whether it ran, how close it hit the brief, and how good it looked. The scoring is mine. The verdicts below are pulled from my source comparison guides at agentos.guide where I publish every score and the reasoning behind it.
Opus 4.8 · The default when the build has to ship on the first prompt — Opus is the safety net inside Agent OS for hard one-shots.
LongCat-2.0 · Run through the free longcat.chat web chat (the API key had no token quota), driven with the local-model-tester GoldieBench prompts; every build render-verified + playtested (verify-move.js: walks + looks + zero errors) before scoring. Slots into the Agent OS as an open frontier coder via its OpenAI-compatible API or the Claude Code / OpenClaw / Hermes harnesses.
Side-by-side on 42 shared tasks
Click any cell to play that model's actual one-shot attempt. Medals are derived from my 0–10 scores per task (highest = 🥇, second = 🥈, third = 🥉).
Task ↓
Opus 4.8
LongCat-2.0
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Where Opus 4.8 beat LongCat-2.0
The tasks where I gave Opus 4.8 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Voxelcraft
Game
Opus 4.8 8.0
·
LongCat-2.0 7.5
(+0.5)
What I saw: 7KB · plays clean · webgl, input, rAF
Where LongCat-2.0 beat Opus 4.8
The tasks where I gave LongCat-2.0 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Crypt
Game
LongCat-2.0 8.0
·
Opus 4.8 6.0
(+2.0)
What I saw: 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).
Dragonrealm
Game
LongCat-2.0 8.5
·
Opus 4.8 7.0
(+1.5)
What I saw: 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.
Skyrim
Game
LongCat-2.0 8.5
·
Opus 4.8 7.0
(+1.5)
What I saw: 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.
Strengths & weaknesses I logged
Opus 4.8
Strengths
- Most consistent across the Goldie Bench bench — no weak build, 8.46/10 average
- Deepest one-shot reasoning, especially on game-feel and physics
- Extended thinking mode handles up to 1M tokens of context
Trade-offs
- 5–10× the per-token cost of every other model on the bench
- Less flair on cinematic visuals than GLM-5.2 — playing it safer wins on accuracy, costs you on showpiece moments
LongCat-2.0
Strengths
- One-shot GoldieBench: 3 of 4 flawless playable 3D builds (Dragon Realm 8.5, Skyrim 8.5, Crypt 8.0); Voxel Craft built one-shot but needed a 1-line camera fix (7.5) — avg 8.1
- 1.6T-param MoE (~48B active/token) with LongCat Sparse Attention + a 1M-token window — built for long-horizon agentic + coding tasks
- Open weights, deeply integrated with Claude Code, OpenClaw and Hermes — a free frontier-class coder to slot into the Agent OS
Trade-offs
- The direct API key we were given had near-zero token quota, so we ran it through the free web chat rather than the API
- One camera-framing miss: Voxel Craft loaded facing away from the world (sky-only) until a one-line yaw/pitch patch pointed it at the terrain
Pricing & context — the spec sheet
| Spec | Opus 4.8 | LongCat-2.0 |
|---|---|---|
| Vendor | Anthropic | Meituan |
| Context window | 200,000 tokens (1M with extended thinking) | 1,000,000 tokens (LongCat Sparse Attention) |
| Price | $15 / $75 per M tokens | Open weights · free web chat · API |
| Pricing detail | Premium pricing via the Anthropic API: $15 per million input tokens, $75 per million output tokens. Extended thinking is included but adds latency. | 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 through the free web chat. Vendor: Meituan. |
| Release | 2026-05 | 2026-06 |
| Bench coverage | 42/42 scored · avg 7.49/10 | 4/4 scored · avg 8.12/10 |
The verdict — which should you pick?
Across 4 scored shared tasks, LongCat-2.0 averaged 8.12/10, beating Opus 4.8's 7.00/10 by 1.12 points. Pick LongCat-2.0 when the build has to ship on the first prompt and you can afford the trade-offs in the comparison below.
If you only run one of these inside your stack, the head-to-head average above is the call. If you can run both, my honest play is to wire Opus 4.8 and LongCat-2.0 both into the Agent Operating System and dispatch each from the kanban by task type — mission-critical one-shot builds where 'has to work the first time' matters → Opus 4.8, one-shot single-file 3d / html / game builds inside the agent os → LongCat-2.0. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Opus 4.8 vs LongCat-2.0
Which is better, Opus 4.8 or LongCat-2.0?
On Goldie Bench, Opus 4.8 averages 7.00/10 across the shared tasks, with 3 gold, 5 silver, 1 bronze overall. LongCat-2.0 averages 8.12/10, with 0 gold, 1 silver, 2 bronze. LongCat-2.0 wins the head-to-head 3–1.
How much does Opus 4.8 cost vs LongCat-2.0?
Opus 4.8: Premium pricing via the Anthropic API: $15 per million input tokens, $75 per million output tokens. Extended thinking is included but adds latency. LongCat-2.0: 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 through the free web chat. Vendor: Meituan.
What's the context window for Opus 4.8 vs LongCat-2.0?
Opus 4.8 has a 200,000 tokens (1M with extended thinking) context window. LongCat-2.0 has a 1,000,000 tokens (LongCat Sparse Attention) context window.
When should I pick Opus 4.8 over LongCat-2.0?
Pick Opus 4.8 for: Mission-critical one-shot builds where 'has to work the first time' matters; Hard reasoning tasks (planning, multi-step) where you'll pay for the depth; Anything where vendor reliability beats the per-token bill. The trade-off is the weaknesses we logged on the bench: 5–10× the per-token cost of every other model on the bench; Less flair on cinematic visuals than GLM-5.2 — playing it safer wins on accuracy, costs you on showpiece moments.
When should I pick LongCat-2.0 over Opus 4.8?
Pick LongCat-2.0 for: One-shot single-file 3D / HTML / game builds inside the Agent OS; Long-context, repo-level edits + automated agentic task execution; A free, open, frontier-class coder to drop into the Model-Proof System. The trade-off is the weaknesses we logged on the bench: The direct API key we were given had near-zero token quota, so we ran it through the free web chat rather than the API; One camera-framing miss: Voxel Craft loaded facing away from the world (sky-only) until a one-line yaw/pitch patch pointed it at the terrain.
How does Goldie Bench score Opus 4.8 vs LongCat-2.0?
Every demo on this page was built by Julian Goldie inside the Agent Operating System — same fixed prompt for both models, one shot, single HTML file out. Each result gets a 0–10 score on whether it ran, how close it hit the brief, and how good it looked. The highest score on each task gets gold; second gets silver; third gets bronze. See methodology for full provenance.
Related comparisons
Other head-to-heads using the same scoring system:
Opus 4.8 vs Fusion LongCat-2.0 vs Fusion Opus 4.8 vs Hermes MoA LongCat-2.0 vs Hermes MoA Opus 4.8 vs Grok LongCat-2.0 vs Grok Opus 4.8 vs MiniMax M3 LongCat-2.0 vs MiniMax M3Full model pages: Opus 4.8 · LongCat-2.0 · back to the leaderboard
The same stack Julian uses
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 3,600+ founders shipping with it every day all live inside the AI Profit Boardroom.
3,600+founders
258documented wins
38countries
$59/momonthly