Real head-to-head · same prompt, one shot
Claude Sonnet 5 vs LongCat-2.0
The agentic SWE frontier — 82% SWE-bench Verified, Dev Team mode. 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 4–0.
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 Claude Sonnet 5 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.
Claude Sonnet 5 · Reach for it in Agent OS when the job is iterative, tool-using software engineering. For one-shot visual builds, GLM 5.2 (free) beat it 4-1 here.
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 = 🥉).
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Claude Sonnet 5
LongCat-2.0
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Where LongCat-2.0 beat Claude Sonnet 5
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
·
Claude Sonnet 5 6.5
(+1.5)
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).
Skyrim
Game
LongCat-2.0 8.5
·
Claude Sonnet 5 7.4
(+1.1)
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.
Dragonrealm
Game
LongCat-2.0 8.5
·
Claude Sonnet 5 8.0
(+0.5)
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.
Voxelcraft
Game
LongCat-2.0 7.5
·
Claude Sonnet 5 7.2
(+0.3)
What I saw: 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…
Strengths & weaknesses I logged
Claude Sonnet 5
Strengths
- 82.1% SWE-bench Verified — first model past 80% on real GitHub-issue repair
- Dev Team multi-agent mode + 1M context for repo-level agentic work
- Precision on hard logic — won the raycaster the open-weight field kept botching
Trade-offs
- One-shot creative-visual builds trail GLM 5.2 here (lost 4 of 5) — no iteration to catch its own bugs
- A temporal-dead-zone bug blanked its N-body orbit sim on the first shot
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 | Claude Sonnet 5 | LongCat-2.0 |
|---|---|---|
| Vendor | Anthropic | Meituan |
| Context window | 1,000,000 tokens | 1,000,000 tokens (LongCat Sparse Attention) |
| Price | $3 / $15 per M ($2/$10 intro) | Open weights · free web chat · API |
| Pricing detail | $3.00 input / $15.00 output per million tokens; introductory $2.00/$10.00 through 2026-08-31. | 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 | 2026-06-30 | 2026-06 |
| Bench coverage | 42/42 scored · avg 7.18/10 | 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 Claude Sonnet 5's 7.28/10 by 0.85 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 Claude Sonnet 5 and LongCat-2.0 both into the Agent Operating System and dispatch each from the kanban by task type — agentic software engineering — write / run / test / fix loops on real repos → Claude Sonnet 5, 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 — Claude Sonnet 5 vs LongCat-2.0
Which is better, Claude Sonnet 5 or LongCat-2.0?
On Goldie Bench, Claude Sonnet 5 averages 7.28/10 across the shared tasks, with 3 gold, 3 silver, 3 bronze overall. LongCat-2.0 averages 8.12/10, with 0 gold, 1 silver, 2 bronze. LongCat-2.0 wins the head-to-head 4–0.
How much does Claude Sonnet 5 cost vs LongCat-2.0?
Claude Sonnet 5: $3.00 input / $15.00 output per million tokens; introductory $2.00/$10.00 through 2026-08-31. 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 Claude Sonnet 5 vs LongCat-2.0?
Claude Sonnet 5 has a 1,000,000 tokens context window. LongCat-2.0 has a 1,000,000 tokens (LongCat Sparse Attention) context window.
When should I pick Claude Sonnet 5 over LongCat-2.0?
Pick Claude Sonnet 5 for: Agentic software engineering — write / run / test / fix loops on real repos; Repo-level reasoning across a 1M-token context (Dev Team multi-agent mode); Precise logic — raycasters, physics — where one-shot open models slip. The trade-off is the weaknesses we logged on the bench: One-shot creative-visual builds trail GLM 5.2 here (lost 4 of 5) — no iteration to catch its own bugs; A temporal-dead-zone bug blanked its N-body orbit sim on the first shot.
When should I pick LongCat-2.0 over Claude Sonnet 5?
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 Claude Sonnet 5 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:
Claude Sonnet 5 vs Fusion LongCat-2.0 vs Fusion Claude Sonnet 5 vs Hermes MoA LongCat-2.0 vs Hermes MoA Claude Sonnet 5 vs Grok LongCat-2.0 vs Grok Claude Sonnet 5 vs MiniMax M3 LongCat-2.0 vs MiniMax M3Full model pages: Claude Sonnet 5 · 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