LongCat-2.0 vs Kimi K2.7 · Quality

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 LongCat-2.0 and Kimi K2.7 · Quality, 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.

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.

Kimi K2.7 · Quality · Reserved for one-shot builds where the output is the deliverable — polish over speed.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Not enough scored shared tasks yet for a head-to-head average. The live demos for both are on the matrix above — play them and form your own opinion.

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 LongCat-2.0 and Kimi K2.7 · Quality both into the Agent Operating System and dispatch each from the kanban by task type — one-shot single-file 3d / html / game builds inside the agent os → LongCat-2.0, one-shot games and sims where polish matters → Kimi K2.7 · Quality. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — LongCat-2.0 vs Kimi K2.7 · Quality

Which is better, LongCat-2.0 or Kimi K2.7 · Quality?

On Goldie Bench, LongCat-2.0 averages no scored verdicts yet across the shared tasks, with 0 gold, 1 silver, 2 bronze overall. Kimi K2.7 · Quality averages no scored verdicts yet, with 0 gold, 0 silver, 0 bronze. Not enough scored shared tasks yet to call a winner.

How much does LongCat-2.0 cost vs Kimi K2.7 · Quality?

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. Kimi K2.7 · Quality: Same flat-rate plan as standard Kimi K2.7 — Quality mode runs the deepest reasoning path. Vendor: Moonshot AI (moonshot.ai).

What's the context window for LongCat-2.0 vs Kimi K2.7 · Quality?

LongCat-2.0 has a 1,000,000 tokens (LongCat Sparse Attention) context window. Kimi K2.7 · Quality has a 256,000 tokens context window.

When should I pick LongCat-2.0 over Kimi K2.7 · Quality?

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.

When should I pick Kimi K2.7 · Quality over LongCat-2.0?

Pick Kimi K2.7 · Quality for: One-shot games and sims where polish matters; Creative writing where you want the model to slow down; Final-pass refinement of an earlier draft. The trade-off is the weaknesses we logged on the bench: Slower than Fast and No-Think — not for snappy loops; Not scored on the standalone bench — see methodology.

How does Goldie Bench score LongCat-2.0 vs Kimi K2.7 · Quality?

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:

Full model pages: LongCat-2.0 · Kimi K2.7 · Quality · back to the leaderboard