Real head-to-head · same prompt, one shot
MiniMax M3 vs Kimi K2.7
1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench. vs The heavy lifter — frontier coder at flat-rate.
Head-to-head verdict: MiniMax M3 wins 10–7 with 3 ties.
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 MiniMax M3 and Kimi K2.7, side by side, on 42 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.
MiniMax M3 · Bench prompts dispatched via OpenRouter. Scored by Claude judge against the same 42 prompts every other model ran.
Kimi K2.7 · Wired into the Agent OS as the heavy-lifter for game/sim prototypes and Kanban-dispatched code work. Mode toggled per task: Quality for one-shot games, Fast for short bursts.
Side-by-side on 42 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 = 🥉).
Task ↓
MiniMax M3
Kimi K2.7
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Page
Page
Sim
Sim
Sim
Where MiniMax M3 beat Kimi K2.7
The tasks where I gave MiniMax M3 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Fluid
Sim
MiniMax M3 7.5
·
Kimi K2.7 5.0
(+2.5)
What I saw: 2D fluid sim with click-drag injection.
Orbit
Sim
MiniMax M3 8.5
·
Kimi K2.7 6.0
(+2.5)
What I saw: 44KB top-down orbit map — Mercury through Mars with accurate relative speeds, hover info cards.
Voxel
Visual
MiniMax M3 8.0
·
Kimi K2.7 6.0
(+2.0)
What I saw: 29KB Temple-Run-style voxel runner on three.js — lane switching, jump + slide, coins.
Landing
Page
MiniMax M3 8.0
·
Kimi K2.7 6.5
(+1.5)
What I saw: Nova-1 landing with animated gradient hero, three feature cards, footer CTA.
Blackhole
Sim
MiniMax M3 7.0
·
Kimi K2.7 6.0
(+1.0)
What I saw: Minimal gravitational-lens shader.
Where Kimi K2.7 beat MiniMax M3
The tasks where I gave Kimi K2.7 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Plasma
Visual
Kimi K2.7 7.5
·
MiniMax M3 6.0
(+1.5)
What I saw: Plasma effect with palette switcher + click ripple. Cleaner than M3's stub.
Raycaster
Game
Kimi K2.7 8.5
·
MiniMax M3 7.0
(+1.5)
· winner · cleanest
What I saw: Kimi nailed it — brick walls, a checkered floor, a clean minimap, textbook Wolfenstein, runs clean out of the box. Opus's is close and more atmospheric: warm fog and a vignette down a stone corridor (A/D to turn, W/S to move). GLM's engine is genuinely good — brick and mossy-ston…
Doom
Game
Kimi K2.7 8.5
·
MiniMax M3 8.0
(+0.5)
What I saw: All three are real, playable shooters. Opus drops you in a corridor with an imp dead ahead — gun, crosshair and HUD framed like a screenshot. Kimi matches it: a monster down a textured hall, health, ammo, minimap. GLM ships a gorgeous 'HAZARD PROTOCOL' title screen with a working…
Fractal
Sim
Kimi K2.7 9.0
·
MiniMax M3 8.5
(+0.5)
· winner · pure wow
What I saw: All three are genuinely good. Kimi's is the jaw-dropper — a deep rainbow plunge into a seahorse spiral, dense with self-similar detail. Opus zooms smoothly into the seahorse valley with a tasteful cycling palette. GLM frames the whole iconic set in a fire palette with a live coor…
Galaxy
Sim
Kimi K2.7 8.0
·
MiniMax M3 7.5
(+0.5)
What I saw: Opus built a proper interactive 3D galaxy — drag to orbit a 7,000-star cloud around a glowing core. Kimi's is the prettiest single frame: a clean tilted spiral disk with rainbow arms. GLM's runs on a canvas with a slick NGC-style HUD and zoom, just less dramatic at a glance. Thre…
Strengths & weaknesses I logged
MiniMax M3
Strengths
- 1M token context — full repo / full deep-research corpus fits in one call
- $0.30/M input is roughly 1/30th of Opus 4.8 — built for high-volume agent loops
- Solid one-shot HTML output — clean structure on game and visual prompts
Trade-offs
- Less polished than Fusion's panel-ensembled output on the toughest deep builds
- Newer model — less community calibration vs Fable 5 / Opus 4.8
Kimi K2.7
Strengths
- Best-of-three on interactive games — raycaster, DOOM, monster AI
- Three speed modes (Fast / No-Think / Quality) you can swap per task
- Flat-rate plan eliminates the per-token meter, so iteration is free
Trade-offs
- Plays plainest on abstract visual prompts — synthwave grids, fluid sims, aurora — where GLM and Opus add more flair
- Bronze average on the Goldie Bench bench despite the gold-medal games — its visual builds are accurate but understated
Pricing & context — the spec sheet
| Spec | MiniMax M3 | Kimi K2.7 |
|---|---|---|
| Vendor | MiniMax | Moonshot AI |
| Context window | 1,048,576-token context — matches GLM-5.2 and Fable 5 | 256,000 tokens |
| Price | $0.30 / 1M input tokens, $1.50 / 1M output | Flat plan (no per-token bill) |
| Pricing detail | MiniMax M3 is the cheapest 1M-context frontier model on the bench — roughly 1/200th the per-call cost of OpenRouter Fusion and 1/30th of Claude Opus 4.8. Designed for high-volume agent workloads where context length matters but per-call budget is tight. | Available on Moonshot's flat-rate subscription plan — no per-token billing for individual builders. The plan covers all three speed modes (Fast, No-Think, Quality). |
| Release | 2026-06-18 | 2026-06 |
| Bench coverage | 42/42 scored · avg 7.96/10 | 20/42 scored · avg 7.42/10 |
The verdict — which should you pick?
Across 20 scored shared tasks, MiniMax M3 averaged 7.80/10, beating Kimi K2.7's 7.42/10 by 0.38 points. Pick MiniMax M3 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 MiniMax M3 and Kimi K2.7 both into the Agent Operating System and dispatch each from the kanban by task type — high-volume agent workflows where per-call cost dominates → MiniMax M3, interactive game prototypes you want shippable on the first prompt → Kimi K2.7. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — MiniMax M3 vs Kimi K2.7
Which is better, MiniMax M3 or Kimi K2.7?
On Goldie Bench, MiniMax M3 averages 7.80/10 across the shared tasks, with 12 gold, 11 silver, 8 bronze overall. Kimi K2.7 averages 7.42/10, with 3 gold, 2 silver, 4 bronze. MiniMax M3 wins the head-to-head 10–7.
How much does MiniMax M3 cost vs Kimi K2.7?
MiniMax M3: MiniMax M3 is the cheapest 1M-context frontier model on the bench — roughly 1/200th the per-call cost of OpenRouter Fusion and 1/30th of Claude Opus 4.8. Designed for high-volume agent workloads where context length matters but per-call budget is tight. Kimi K2.7: Available on Moonshot's flat-rate subscription plan — no per-token billing for individual builders. The plan covers all three speed modes (Fast, No-Think, Quality).
What's the context window for MiniMax M3 vs Kimi K2.7?
MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window. Kimi K2.7 has a 256,000 tokens context window.
When should I pick MiniMax M3 over Kimi K2.7?
Pick MiniMax M3 for: High-volume agent workflows where per-call cost dominates; 1M-context tasks (whole-repo refactors, deep-research synthesis); Drop-in cheaper alternative to GLM-5.2 with comparable 1M context. The trade-off is the weaknesses we logged on the bench: Less polished than Fusion's panel-ensembled output on the toughest deep builds; Newer model — less community calibration vs Fable 5 / Opus 4.8.
When should I pick Kimi K2.7 over MiniMax M3?
Pick Kimi K2.7 for: Interactive game prototypes you want shippable on the first prompt; High-iteration agent loops where per-token cost would dominate; Long-context refactors using the 256K window inside Agent OS. The trade-off is the weaknesses we logged on the bench: Plays plainest on abstract visual prompts — synthwave grids, fluid sims, aurora — where GLM and Opus add more flair; Bronze average on the {{SITE_NAME}} bench despite the gold-medal games — its visual builds are accurate but understated.
How does Goldie Bench score MiniMax M3 vs Kimi K2.7?
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:
MiniMax M3 vs Opus 4.8 Kimi K2.7 vs Opus 4.8 MiniMax M3 vs GLM-5.2 Kimi K2.7 vs GLM-5.2 MiniMax M3 vs Grok Kimi K2.7 vs Grok MiniMax M3 vs Fusion Kimi K2.7 vs FusionFull model pages: MiniMax M3 · Kimi K2.7 · 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 3,600+ founders shipping with it every day all live inside the AI Profit Boardroom.
3,600+founders
258documented wins
38countries
$100k+/mocommunity MRR