LongCat-2.0 vs Claude Fable 5

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 Claude Fable 5, side by side, on 0 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.

Claude Fable 5 · Selected from Agent OS for the highest-stakes one-shot work — replacing Opus 4.8 as the safety net on hard prompts. Bench scoring pending.

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 Claude Fable 5 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, mission-critical one-shot builds where you want anthropic's newest reasoning → Claude Fable 5. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — LongCat-2.0 vs Claude Fable 5

Which is better, LongCat-2.0 or Claude Fable 5?

On Goldie Bench, LongCat-2.0 averages no scored verdicts yet across the shared tasks, with 0 gold, 1 silver, 2 bronze overall. Claude Fable 5 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 Claude Fable 5?

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. Claude Fable 5: Released alongside Mythos 5 on June 9, 2026 as the publicly-available member of the new Mythos class. Premium per-token pricing on the Anthropic API; available everywhere Opus 4.8 ships.

What's the context window for LongCat-2.0 vs Claude Fable 5?

LongCat-2.0 has a 1,000,000 tokens (LongCat Sparse Attention) context window. Claude Fable 5 has a 200,000 tokens (1M with extended thinking) context window.

When should I pick LongCat-2.0 over Claude Fable 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.

When should I pick Claude Fable 5 over LongCat-2.0?

Pick Claude Fable 5 for: Mission-critical one-shot builds where you want Anthropic's newest reasoning; Long-context work using extended thinking up to 1M tokens; Plan-heavy multi-step tasks where intelligence in the plan matters more than the build. The trade-off is the weaknesses we logged on the bench: No goldiebench per-task scores yet — bench rank pending a published head-to-head guide; Premium pricing; Fusion premium panel reportedly out-scores it at half the API cost.

How does Goldie Bench score LongCat-2.0 vs Claude Fable 5?

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 · Claude Fable 5 · back to the leaderboard