Real head-to-head · same prompt, one shot
MiniMax M3 vs Kilo Code
1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench. vs Fable 5-class intelligence at ~59% less. The split-the-cost play.
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 Kilo Code, 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.
MiniMax M3 · Bench prompts dispatched via OpenRouter. Scored by Claude judge against the same 42 prompts every other model ran.
Kilo Code · Used inside Agent OS as a routing layer: Fable 5 generates the plan, cheaper models execute. Bench scoring pending a head-to-head comparison.
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 = 🥉).
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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
Kilo Code
Strengths
- Kilo's own rubric: Fable 5 plan = 9.1/10, GPT-5.5 plan = 8.3/10 — Kilo isolates where the intelligence actually lives
- Plan quality stays high while execution cost drops
- Drop-in for Agent OS — Kilo Split framework already wired
Trade-offs
- Adds routing complexity — two model providers in one workflow
- No per-task goldiebench head-to-heads yet
Pricing & context — the spec sheet
| Spec | MiniMax M3 | Kilo Code |
|---|---|---|
| Vendor | MiniMax | Kilo |
| Context window | 1,048,576-token context — matches GLM-5.2 and Fable 5 | Varies — Kilo splits planning from execution across multiple models |
| Price | $0.30 / 1M input tokens, $1.50 / 1M output | ~59% less than Fable 5 solo |
| 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. | Kilo Code is a routing layer that splits planning (heavy model) from execution (cheaper model) so you get Fable-5-class plans driving GPT-5.5-class builds. Total spend lands at ~59% less than running Fable 5 end-to-end. |
| Release | 2026-06-18 | 2026-06-16 |
| Bench coverage | 42/42 scored · avg 7.96/10 | 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 MiniMax M3 and Kilo Code 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, cost-conscious operators who run high-volume agent loops → Kilo Code. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — MiniMax M3 vs Kilo Code
Which is better, MiniMax M3 or Kilo Code?
On Goldie Bench, MiniMax M3 averages no scored verdicts yet across the shared tasks, with 12 gold, 11 silver, 8 bronze overall. Kilo Code 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 MiniMax M3 cost vs Kilo Code?
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. Kilo Code: Kilo Code is a routing layer that splits planning (heavy model) from execution (cheaper model) so you get Fable-5-class plans driving GPT-5.5-class builds. Total spend lands at ~59% less than running Fable 5 end-to-end.
What's the context window for MiniMax M3 vs Kilo Code?
MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window. Kilo Code has a Varies — Kilo splits planning from execution across multiple models context window.
When should I pick MiniMax M3 over Kilo Code?
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 Kilo Code over MiniMax M3?
Pick Kilo Code for: Cost-conscious operators who run high-volume agent loops; Multi-step workflows where the plan is the expensive part; Teams already paying for Fable 5 who want to keep the plan but drop the execution bill. The trade-off is the weaknesses we logged on the bench: Adds routing complexity — two model providers in one workflow; No per-task goldiebench head-to-heads yet.
How does Goldie Bench score MiniMax M3 vs Kilo Code?
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 Kilo Code vs Opus 4.8 MiniMax M3 vs GLM-5.2 Kilo Code vs GLM-5.2 MiniMax M3 vs Grok Kilo Code vs Grok MiniMax M3 vs Fusion Kilo Code vs FusionFull model pages: MiniMax M3 · Kilo Code · 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