Real head-to-head · same prompt, one shot

MiniMax M3 vs Hy3

1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench. vs Tencent's open-weights coder — Apache-2.0, cheap, beats GLM-5.1 on frontend in Tencent's blind eval.

Head-to-head verdict: MiniMax M3 wins 6–0 with 1 tie.

MiniMax M3 · context1M tokens
Hy3 · context262K tokens
MiniMax M3 · price$0.30 / 1M input tokens, $1.50 / 1M output
Hy3 · price$0.14 / 1M input · $0.58 / 1M output
MiniMax M3 · vendorMiniMax
Hy3 · vendorTencent Hunyuan

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 Hy3, side by side, on 7 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.

Hy3 · Wired into the Agent OS as the 'Hy3 Coder' tab (chat + live preview + workspace) via OpenRouter. Bench built one-shot on the same prompts as the field; weak builds iterated by Hy3 itself (the model fixes its own builds).

Side-by-side on 47 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
Hy3
Game
MiniMax M3 on Doom
Hy3 on Doom
🥇MiniMax M3 on Dragonrealm
Hy3 on Dragonrealm
Game
🥈MiniMax M3 on Flightsim
🥈Hy3 on Flightsim
Game
🥈MiniMax M3 on Gtadrive
Hy3 on Gtadrive
Game
🥈MiniMax M3 on Gtafoot
Hy3 on Gtafoot
Game
🥈MiniMax M3 on Parachute
Hy3 on Parachute
Page
🥇MiniMax M3 on Aipbpromo
Hy3 on Aipbpromo
Game
MiniMax M3 on Arcade
— not attempted —
Game
🥉MiniMax M3 on Crypt
— not attempted —
Game
🥉MiniMax M3 on Dogfight
— not attempted —
🥉MiniMax M3 on Dragonflight
— not attempted —
Game
MiniMax M3 on Game
— not attempted —
🥉MiniMax M3 on Neonblaster
— not attempted —
Game
MiniMax M3 on Neoncity
— not attempted —
Game
MiniMax M3 on Neonracer
— not attempted —
🥈MiniMax M3 on Nordiccrypt
— not attempted —
Game
MiniMax M3 on Outrun
— not attempted —
Game
MiniMax M3 on Pool
— not attempted —
Game
🥇MiniMax M3 on Racing
— not attempted —
Game
MiniMax M3 on Raycaster
— not attempted —
Game
MiniMax M3 on Rpg
— not attempted —
Game
🥉MiniMax M3 on Skyrim
— not attempted —
🥉MiniMax M3 on Twilightvale
— not attempted —
Game
🥉MiniMax M3 on Voxelcraft
— not attempted —

Where MiniMax M3 beat Hy3

The tasks where I gave MiniMax M3 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Doom Game
MiniMax M3 8.0 · Hy3 5.5 (+2.5)

What I saw: Raycaster + sprite enemies + gun + HUD. 21KB of game logic.

MiniMax M3 9.0 · Hy3 7.2 (+1.8) · winner · biggest Dragon Realm

What I saw: 34KB frozen open world — snowy mountains, pines, flying dragon, full HUD.

Gtafoot Game
MiniMax M3 8.0 · Hy3 7.2 (+0.8)

What I saw: 30KB · plays clean · three, webgl (re-rolled)

Parachute Game
MiniMax M3 8.0 · Hy3 7.2 (+0.8)

What I saw: 26KB · plays clean · three, webgl

Aipbpromo Page
MiniMax M3 8.0 · Hy3 7.4 (+0.6)

What I saw: 95KB · plays clean · rAF

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

Hy3

Strengths

  • Apache-2.0 open weights — self-host free, no lock-in
  • Tencent's 270-expert blind eval: 2.67/4 vs GLM-5.1's 2.51, strongest on frontend / data / CI-CD
  • Hallucination rate cut 12.5% → 5.4%; stable tool-calls across scaffoldings (<4% SWE-Bench variance)

Trade-offs

  • Slow upstream on OpenRouter (30-90s per build) — fine for one-shots, sluggish for tight loops
  • One-shot game builds can under-render (flat raycaster walls, unlit 3D) without an iterate pass

Pricing & context — the spec sheet

Spec MiniMax M3 Hy3
VendorMiniMaxTencent Hunyuan
Context window1,048,576-token context — matches GLM-5.2 and Fable 5262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter.
Price$0.30 / 1M input tokens, $1.50 / 1M output$0.14 / 1M input · $0.58 / 1M output
Pricing detailMiniMax 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.Tencent Hunyuan 3 — open-weights under Apache-2.0, so free to self-host. On OpenRouter it is one of the cheapest capable coders: ~$0.14/M in, $0.58/M out (1 RMB / 4 RMB). Upstream can be slow (30-90s to first token), but per-token cost is negligible.
Release2026-06-182026-07-06
Bench coverage47/47 scored · avg 7.97/107/7 scored · avg 7.13/10

The verdict — which should you pick?

Across 7 scored shared tasks, MiniMax M3 averaged 8.14/10, beating Hy3's 7.13/10 by 1.01 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 Hy3 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-sensitive coding + frontend design where open weights matter → Hy3. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — MiniMax M3 vs Hy3

Which is better, MiniMax M3 or Hy3?

On Goldie Bench, MiniMax M3 averages 8.14/10 across the shared tasks, with 3 gold, 7 silver, 10 bronze overall. Hy3 averages 7.13/10, with 0 gold, 1 silver, 0 bronze. MiniMax M3 wins the head-to-head 6–0.

How much does MiniMax M3 cost vs Hy3?

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. Hy3: Tencent Hunyuan 3 — open-weights under Apache-2.0, so free to self-host. On OpenRouter it is one of the cheapest capable coders: ~$0.14/M in, $0.58/M out (1 RMB / 4 RMB). Upstream can be slow (30-90s to first token), but per-token cost is negligible.

What's the context window for MiniMax M3 vs Hy3?

MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window. Hy3 has a 262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter. context window.

When should I pick MiniMax M3 over Hy3?

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 Hy3 over MiniMax M3?

Pick Hy3 for: Cost-sensitive coding + frontend design where open weights matter; Self-hosters who want an Apache-2.0 model they fully own; Anyone wiring a cheap capable coder into a live build panel (Agent OS Hy3 Coder tab). The trade-off is the weaknesses we logged on the bench: Slow upstream on OpenRouter (30-90s per build) — fine for one-shots, sluggish for tight loops; One-shot game builds can under-render (flat raycaster walls, unlit 3D) without an iterate pass.

How does Goldie Bench score MiniMax M3 vs Hy3?

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.

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
$59/momonthly