Claude Fable 5 vs LongCat-2.0

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 Fable 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 Fable 5 · Selected from Agent OS for the highest-stakes work — it replaced Opus 4.8 as the safety net on hard prompts. Its four core 3D games were rebuilt to showcase quality with the threejs-game-director skill, lifting the full 42-task bench to 8.14 avg — the #1 solo model, behind only the Fusion and MoA ensembles.

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.

Where Claude Fable 5 beat LongCat-2.0

The tasks where I gave Claude Fable 5 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Where LongCat-2.0 beat Claude Fable 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.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Across 4 scored shared tasks, Claude Fable 5 averaged 8.65/10, beating LongCat-2.0's 8.12/10 by 0.53 points. Pick Claude Fable 5 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 Fable 5 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 you want anthropic's newest reasoning → Claude Fable 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 4,000+ founders inside the AI Profit Boardroom.

FAQ — Claude Fable 5 vs LongCat-2.0

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

On Goldie Bench, Claude Fable 5 averages 8.65/10 across the shared tasks, with 4 gold, 2 silver, 1 bronze overall. LongCat-2.0 averages 8.12/10, with 0 gold, 0 silver, 0 bronze. Claude Fable 5 wins the head-to-head 3–1.

How much does Claude Fable 5 cost vs LongCat-2.0?

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. 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 Fable 5 vs LongCat-2.0?

Claude Fable 5 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 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: Its hardest one-shots (crypt, twilightvale) black-screened on three.js r128 API drift — the scored builds are agentic rebuilds, not the raw first pass, and crypt's AAA rebuild needed a one-line emissive patch; The showcase ceiling shown here needs the threejs-game-director scaffolding baked into the prompt — a bare one-shot lands lower (7.72 avg); Premium $10/$50 per-M pricing — you're paying for reasoning depth; cheaper models stay competitive on pure one-shot visuals.

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.

How does Goldie Bench score Claude Fable 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:

Full model pages: Claude Fable 5 · LongCat-2.0 · back to the leaderboard