
LongCat-2.0 vs Claude Mythos 5
The open 1.6T MoE that builds — a frontier coder trained on non-Nvidia ASIC superpods. vs Restricted-access flagship — vetted partners only.
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 Mythos 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 Mythos 5 · Not currently dispatched from Agent OS — no public API. Tracked for news coverage only.
Side-by-side on 4 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 = 🥉).
Strengths & weaknesses I logged
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
Claude Mythos 5
Strengths
- Highest-class Anthropic model — sits above Fable 5 in the Mythos line
- Same Mythos-class capability profile as Fable 5 (with restrictions)
Trade-offs
- No public API access — can't be benched against the open field
- Restricted distribution makes it effectively unavailable to most builders
Pricing & context — the spec sheet
| Spec | LongCat-2.0 | Claude Mythos 5 |
|---|---|---|
| Vendor | Meituan | Anthropic |
| Context window | 1,000,000 tokens (LongCat Sparse Attention) | 200,000 tokens |
| Price | Open weights · free web chat · API | Restricted access — vetted partners only |
| Pricing detail | 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. | Mythos 5 is the un-released sibling of Fable 5. Same Mythos class, restricted to vetted partners. Made headlines mid-June 2026 when reports of a White-House-driven ban over China access dominated AI press. |
| Release | 2026-06 | 2026-06-09 |
| Bench coverage | 4/4 scored · avg 8.12/10 | 0/0 scored · avg — |
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 Mythos 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, vetted enterprise partners with mythos-class access → Claude Mythos 5. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — LongCat-2.0 vs Claude Mythos 5
Which is better, LongCat-2.0 or Claude Mythos 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 Mythos 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 Mythos 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 Mythos 5: Mythos 5 is the un-released sibling of Fable 5. Same Mythos class, restricted to vetted partners. Made headlines mid-June 2026 when reports of a White-House-driven ban over China access dominated AI press.
What's the context window for LongCat-2.0 vs Claude Mythos 5?
LongCat-2.0 has a 1,000,000 tokens (LongCat Sparse Attention) context window. Claude Mythos 5 has a 200,000 tokens context window.
When should I pick LongCat-2.0 over Claude Mythos 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 Mythos 5 over LongCat-2.0?
Pick Claude Mythos 5 for: Vetted enterprise partners with Mythos-class access; (For everyone else: read the Mythos ban coverage instead of waiting for access). The trade-off is the weaknesses we logged on the bench: No public API access — can't be benched against the open field; Restricted distribution makes it effectively unavailable to most builders.
How does Goldie Bench score LongCat-2.0 vs Claude Mythos 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:
LongCat-2.0 vs Fusion Claude Mythos 5 vs Fusion LongCat-2.0 vs Hermes MoA Claude Mythos 5 vs Hermes MoA LongCat-2.0 vs Grok Claude Mythos 5 vs Grok LongCat-2.0 vs MiniMax M3 Claude Mythos 5 vs MiniMax M3Full model pages: LongCat-2.0 · Claude Mythos 5 · back to the leaderboard
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.


