Real head-to-head · same prompt, one shot
MiMo-V2.6 Pro vs LongCat-2.0
Open weights that score level with Opus 5 on agents, for cents. 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 3–1.
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 MiMo-V2.6 Pro 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.
MiMo-V2.6 Pro · Benched on all 50 GoldieBench tasks through OpenRouter at the model's default reasoning effort: one-shot build, real rendered poster, Opus 4.8 vision judge, game tasks skill-infused. No retries, no hand fixes; the broken builds are scored as they shipped.
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 50 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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Where MiMo-V2.6 Pro beat LongCat-2.0
The tasks where I gave MiMo-V2.6 Pro a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Voxelcraft
Game
MiMo-V2.6 Pro 8.6
·
LongCat-2.0 7.5
(+1.1)
· Polished voxel sandbox
What I saw: Strong render: clean voxel terrain with proper grass/dirt/stone textures, trees, first-person hand, full hotbar with 9 block types, and a polished HUD showing vitals, day/night clock, biome, and score badges. The cohesive dark-cyan monospace UI theme plus visible day/night cycle …
Where LongCat-2.0 beat MiMo-V2.6 Pro
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
·
MiMo-V2.6 Pro 3.2
(+4.8)
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
·
MiMo-V2.6 Pro 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
·
MiMo-V2.6 Pro 8.4
(+0.1)
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.
Strengths & weaknesses I logged
MiMo-V2.6 Pro
Strengths
- Simulations and visual pieces are top-tier one-shots: the black hole lensing scored 9.0 and the matrix rain, lava lamp, ocean waves, web desktop, boids and galaxy all landed 8.6 or higher
- Strong flight and driving output when the build holds together: a polished 3D dogfight (8.6), a flight sim (8.4) and the synthwave outrun (8.4)
- Big, complete files: builds ran 30 to 70 KB with full HUDs, control hints and settings panels
- The weights are MIT and on Hugging Face, so the same model can run on your own hardware
Trade-offs
- 18 of 50 builds scored under 5: long game files shipped with garbled tokens (a stray 'martin' or 'martial' identifier breaks the whole script), uninitialised references and bad canvas values, so the HUD paints but the 3D scene stays black
- Two hard crashes: the RPG threw an engine fault on load and the solar system rendered nothing at all
- It reasons for a long time at default effort: builds took 5 to 60 minutes each through OpenRouter
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 | MiMo-V2.6 Pro | LongCat-2.0 |
|---|---|---|
| Vendor | Xiaomi | Meituan |
| Context window | 1,000,000 tokens | 1,000,000 tokens (LongCat Sparse Attention) |
| Price | $0.435 in / $0.87 out per M tokens | Open weights · free web chat · API |
| Pricing detail | Xiaomi's September 2026 open-weight flagship (MIT licence, 1.02T total / 42B active parameters). $0.435 per million input tokens and $0.87 per million output on OpenRouter, with cache hits at a fraction of a cent, which is roughly a quarter of Grok 4.7 and a twentieth of the closed frontier models it scores level with on agent benchmarks. | 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 | 2026-09 | 2026-06 |
| Bench coverage | 50/50 scored · avg 6.35/10 | 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 MiMo-V2.6 Pro's 6.90/10 by 1.22 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 MiMo-V2.6 Pro and LongCat-2.0 both into the Agent Operating System and dispatch each from the kanban by task type — simulations, shaders and visual scenes in one shot, at a fraction of frontier prices → MiMo-V2.6 Pro, 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 — MiMo-V2.6 Pro vs LongCat-2.0
Which is better, MiMo-V2.6 Pro or LongCat-2.0?
On Goldie Bench, MiMo-V2.6 Pro averages 6.90/10 across the shared tasks, with 5 gold, 8 silver, 4 bronze overall. LongCat-2.0 averages 8.12/10, with 0 gold, 0 silver, 0 bronze. LongCat-2.0 wins the head-to-head 3–1.
How much does MiMo-V2.6 Pro cost vs LongCat-2.0?
MiMo-V2.6 Pro: Xiaomi's September 2026 open-weight flagship (MIT licence, 1.02T total / 42B active parameters). $0.435 per million input tokens and $0.87 per million output on OpenRouter, with cache hits at a fraction of a cent, which is roughly a quarter of Grok 4.7 and a twentieth of the closed frontier models it scores level with on agent benchmarks. 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 MiMo-V2.6 Pro vs LongCat-2.0?
MiMo-V2.6 Pro 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 MiMo-V2.6 Pro over LongCat-2.0?
Pick MiMo-V2.6 Pro for: Simulations, shaders and visual scenes in one shot, at a fraction of frontier prices; High-volume agent work where an open, MIT-licensed model matters; Pair it with a self-fix loop for games: the failures are single broken tokens, not missing ideas. The trade-off is the weaknesses we logged on the bench: 18 of 50 builds scored under 5: long game files shipped with garbled tokens (a stray 'martin' or 'martial' identifier breaks the whole script), uninitialised references and bad canvas values, so the HUD paints but the 3D scene stays black; Two hard crashes: the RPG threw an engine fault on load and the solar system rendered nothing at all; It reasons for a long time at default effort: builds took 5 to 60 minutes each through OpenRouter.
When should I pick LongCat-2.0 over MiMo-V2.6 Pro?
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 MiMo-V2.6 Pro 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:
MiMo-V2.6 Pro vs Fusion LongCat-2.0 vs Fusion MiMo-V2.6 Pro vs Claude Opus 5 LongCat-2.0 vs Claude Opus 5 MiMo-V2.6 Pro vs Hermes MoA LongCat-2.0 vs Hermes MoA MiMo-V2.6 Pro vs GPT-5.6 Sol LongCat-2.0 vs GPT-5.6 SolFull model pages: MiMo-V2.6 Pro · 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 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.
4,000+founders
258documented wins
38countries
$59/momonthly