
Real head-to-head · same prompt, one shot
MiniMax M3 vs Kimi K3
1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench. vs Moonshot's 2.8T flagship — 1M context, tuned for long-horizon agent work.
Head-to-head verdict: Kimi K3 wins 30–16 with 1 tie.
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 K3, 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.
MiniMax M3 · Bench prompts dispatched via OpenRouter. Scored by Claude judge against the same 42 prompts every other model ran.
Kimi K3 · Wired into the Agent OS as the `kimi-k3` Hermes profile and a K3 speed-toggle in the Kimi Code tab — used for long unattended agent runs where a slow-but-right model beats a fast-but-forgetful one.
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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MiniMax M3
Kimi K3
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Where MiniMax M3 beat Kimi K3
The tasks where I gave MiniMax M3 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Orbit
Sim
MiniMax M3 8.5
·
Kimi K3 3.0
(+5.5)
What I saw: 44KB top-down orbit map — Mercury through Mars with accurate relative speeds, hover info cards.
Dogfight
Game
MiniMax M3 8.5
·
Kimi K3 3.5
(+5.0)
What I saw: 25KB 3D dogfight with enemy AI, missiles, guns.
Galaxy
Sim
MiniMax M3 7.5
·
Kimi K3 3.0
(+4.5)
What I saw: Spiral galaxy on three.js — particle stars, slow rotation.
Terrain
Visual
MiniMax M3 7.5
·
Kimi K3 4.5
(+3.0)
What I saw: Tron-style terrain flythrough.
Reactiondiff
Sim
MiniMax M3 8.5
·
Kimi K3 6.5
(+2.0)
What I saw: 31KB Gray-Scott shader with click-to-seed.
Where Kimi K3 beat MiniMax M3
The tasks where I gave Kimi K3 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Blackhole
Sim
Kimi K3 9.0
·
MiniMax M3 7.0
(+2.0)
· stunning geodesic lensing
What I saw: Gorgeous real geodesic raytracing with a properly lensed accretion disk wrapping over the top, convincing Doppler asymmetry, photon-ring glow, and a rich nebula starfield backdrop; polished typography and clean UI push it to the top of the field. Minor stat overlap at bottom-left…
Raycaster
Game
Kimi K3 8.6
·
MiniMax M3 7.0
(+1.6)
· textured raycaster polish
What I saw: Strong render: multiple distinct textured wall types (brick, blue stone, glowing tech strips, mossy hedge), textured floor/ceiling, minimap, ceiling glow lights and vignette all working with clean UI and controls. Very polished and clearly on-brief; edges out the field with textu…
Fluid
Sim
Kimi K3 9.0
·
MiniMax M3 7.5
(+1.5)
What I saw: Gorgeous, textbook-quality WebGL fluid sim with rich swirling dye, added particle sparkle, and polished UI (gradient title, hint pill, control buttons) — the vorticity/pressure-solve pipeline and half-float fallback handling are all correct and shippable; only minor knock is the …
Synthwave
Visual
Kimi K3 9.0
·
MiniMax M3 7.5
(+1.5)
· textbook synthwave sunset
What I saw: Nails every synthwave trope beautifully — banded sunset, receding neon perspective grid, layered mountains, glowing gradient title, floating wireframe solids, starfield, scanlines/vignette, and generative WebAudio music. Highly polished and cohesive; a clear task winner.
Plasma
Visual
Kimi K3 7.4
·
MiniMax M3 6.0
(+1.4)
What I saw: Clean UI with gradient PLASMA title, numbered palette swatches, and clear hint text render nicely; however the actual plasma field looks like soft amber blobs rather than the rich, banded hypnotic plasma the brief calls for, making it visually flat compared to the field's best. S…
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 K3
Strengths
- Launch-day benchmarks put it around the Fable/Sol tier, with Terminal Bench (agentic terminal-driving) the standout
- 1M-token context verified on this bench's needle test: exact recall from 162k tokens of noise in 18s
- One-shot builds run long but land complete — its first bench game (13.4 min of thinking, 30,880 tokens) playtested with zero JS errors
- Included in the Kimi coding plan — frontier tier without a new bill
Trade-offs
- Slow on hard tasks — early testers report up to ~35 minutes at max reasoning; this bench saw 13+ minute single builds
- Launch-day rate limits on OpenRouter (429s) — the coding-plan endpoint was the reliable route
- Self-reports as K2.7 if you ask it — verify the served model via the API response, not the model's word
Pricing & context — the spec sheet
| Spec | MiniMax M3 | Kimi K3 |
|---|---|---|
| Vendor | MiniMax | Moonshot AI |
| Context window | 1,048,576-token context — matches GLM-5.2 and Fable 5 | 1,048,576 tokens — a full codebase in working memory |
| Price | $0.30 / 1M input tokens, $1.50 / 1M output | $3 / M in |
| 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. | Launched July 16, 2026. 2.8T-param MoE (Moonshot's quickstart corrected the circulating 2.5T estimate). $3/M input on OpenRouter at launch; included at no extra cost in the Kimi coding plan (`k3` on the coding endpoint). |
| Release | 2026-06-18 | 2026-07-16 |
| Bench coverage | 47/47 scored · avg 7.97/10 | 50/50 scored · avg 7.89/10 |
The verdict — which should you pick?
Across 47 scored shared tasks, the averages are essentially tied — MiniMax M3 7.97 vs Kimi K3 7.86. This isn't the comparison where one wins; it's the comparison where you pick based on context, pricing, and what you're actually trying to ship.
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 K3 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, long-horizon agent runs → Kimi K3. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.
FAQ — MiniMax M3 vs Kimi K3
Which is better, MiniMax M3 or Kimi K3?
On Goldie Bench, MiniMax M3 averages 7.97/10 across the shared tasks, with 2 gold, 1 silver, 4 bronze overall. Kimi K3 averages 7.86/10, with 8 gold, 4 silver, 7 bronze. Kimi K3 wins the head-to-head 30–16.
How much does MiniMax M3 cost vs Kimi K3?
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 K3: Launched July 16, 2026. 2.8T-param MoE (Moonshot's quickstart corrected the circulating 2.5T estimate). $3/M input on OpenRouter at launch; included at no extra cost in the Kimi coding plan (`k3` on the coding endpoint).
What's the context window for MiniMax M3 vs Kimi K3?
MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window. Kimi K3 has a 1,048,576 tokens — a full codebase in working memory context window.
When should I pick MiniMax M3 over Kimi K3?
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 K3 over MiniMax M3?
Pick Kimi K3 for: long-horizon agent runs; whole-repo context work; terminal-driving agents. The trade-off is the weaknesses we logged on the bench: Slow on hard tasks — early testers report up to ~35 minutes at max reasoning; this bench saw 13+ minute single builds; Launch-day rate limits on OpenRouter (429s) — the coding-plan endpoint was the reliable route; Self-reports as K2.7 if you ask it — verify the served model via the API response, not the model's word.
How does Goldie Bench score MiniMax M3 vs Kimi K3?
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 Fusion Kimi K3 vs Fusion MiniMax M3 vs Claude Opus 5 Kimi K3 vs Claude Opus 5 MiniMax M3 vs Hermes MoA Kimi K3 vs Hermes MoA MiniMax M3 vs GPT-5.6 Sol Kimi K3 vs GPT-5.6 SolFull model pages: MiniMax M3 · Kimi K3 · 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














































