GLM-5.2 vs MiMo-V2.6 Pro

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 GLM-5.2 and MiMo-V2.6 Pro, side by side, on 47 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.

GLM-5.2 · Default model inside Agent OS for any task that touches a long context — codebase Q&A, multi-file refactors, agent memory replay.

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.

Where GLM-5.2 beat MiMo-V2.6 Pro

The tasks where I gave GLM-5.2 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Where MiMo-V2.6 Pro beat GLM-5.2

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.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Across 47 scored shared tasks, GLM-5.2 averaged 7.77/10, beating MiMo-V2.6 Pro's 6.33/10 by 1.44 points. Pick GLM-5.2 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 GLM-5.2 and MiMo-V2.6 Pro both into the Agent Operating System and dispatch each from the kanban by task type — long-context agent loops — pasting a whole codebase into one prompt → GLM-5.2, simulations, shaders and visual scenes in one shot, at a fraction of frontier prices → MiMo-V2.6 Pro. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — GLM-5.2 vs MiMo-V2.6 Pro

Which is better, GLM-5.2 or MiMo-V2.6 Pro?

On Goldie Bench, GLM-5.2 averages 7.77/10 across the shared tasks, with 5 gold, 0 silver, 0 bronze overall. MiMo-V2.6 Pro averages 6.33/10, with 5 gold, 8 silver, 4 bronze. GLM-5.2 wins the head-to-head 24–23.

How much does GLM-5.2 cost vs MiMo-V2.6 Pro?

GLM-5.2: Open-weights release: weights downloadable from Hugging Face for self-hosting, or runnable for free on z.ai for individuals (commercial use has separate licensing). 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.

What's the context window for GLM-5.2 vs MiMo-V2.6 Pro?

GLM-5.2 has a 1,000,000 tokens context window. MiMo-V2.6 Pro has a 1,000,000 tokens context window.

When should I pick GLM-5.2 over MiMo-V2.6 Pro?

Pick GLM-5.2 for: Long-context agent loops — pasting a whole codebase into one prompt; Cinematic visual builds — landing pages, voxel scenes, synthwave runners; Anyone who needs to run a frontier coder locally for $0. The trade-off is the weaknesses we logged on the bench: Faceplanted on the {{SITE_NAME}} raycaster — the engine was great but it spawned the player inside a wall; First-shot reliability lags Opus by a hair on consistency.

When should I pick MiMo-V2.6 Pro over GLM-5.2?

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.

How does Goldie Bench score GLM-5.2 vs MiMo-V2.6 Pro?

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: GLM-5.2 · MiMo-V2.6 Pro · back to the leaderboard