Get the Agent OS + join 4,000+ founders inside the AI Profit Boardroom → Join AIPB ($59/mo)
Real head-to-head · same prompt, one shot

MiniMax M3 vs DeepSeek V4 Pro

1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench. vs DeepSeek's flagship tier — benched head-to-head against its own cheap Flash.

MiniMax M3 · context1M tokens
DeepSeek V4 Pro · context1M tokens
MiniMax M3 · price$0.30 / 1M input tokens, $1.50 / 1M output
DeepSeek V4 Pro · priceAPI · pro tier
MiniMax M3 · vendorMiniMax
DeepSeek V4 Pro · vendorDeepSeek

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 DeepSeek V4 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.

MiniMax M3 · Bench prompts dispatched via OpenRouter. Scored by Claude judge against the same 42 prompts every other model ran.

DeepSeek V4 Pro · Benched on all 50 GoldieBench tasks via api.deepseek.com with the same pipeline as the Flash 0731 run, then published as a live side-by-side: goldiebench.com/vs-live/deepseek-flash-vs-pro.html loads both builds of every task in twin panes.

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 = 🥉).

Task ↓
MiniMax M3
DeepSeek V4 Pro
Game
MiniMax M3 on Arcade
DeepSeek V4 Pro on Arcade
Game
🥉MiniMax M3 on Crypt
DeepSeek V4 Pro on Crypt
Game
MiniMax M3 on Dogfight
DeepSeek V4 Pro on Dogfight
Game
MiniMax M3 on Doom
DeepSeek V4 Pro on Doom
🥉MiniMax M3 on Dragonflight
DeepSeek V4 Pro on Dragonflight
🥇MiniMax M3 on Dragonrealm
DeepSeek V4 Pro on Dragonrealm
Game
MiniMax M3 on Flightsim
DeepSeek V4 Pro on Flightsim
Game
MiniMax M3 on Game
DeepSeek V4 Pro on Game
Game
MiniMax M3 on Gtadrive
DeepSeek V4 Pro on Gtadrive
Game
MiniMax M3 on Gtafoot
DeepSeek V4 Pro on Gtafoot
MiniMax M3 on Neonblaster
DeepSeek V4 Pro on Neonblaster
Game
MiniMax M3 on Neoncity
DeepSeek V4 Pro on Neoncity
Game
MiniMax M3 on Neonracer
DeepSeek V4 Pro on Neonracer
🥈MiniMax M3 on Nordiccrypt
DeepSeek V4 Pro on Nordiccrypt
Game
MiniMax M3 on Outrun
DeepSeek V4 Pro on Outrun
Game
MiniMax M3 on Parachute
DeepSeek V4 Pro on Parachute
Game
MiniMax M3 on Pool
DeepSeek V4 Pro on Pool
Game
🥇MiniMax M3 on Racing
DeepSeek V4 Pro on Racing
Game
MiniMax M3 on Raycaster
DeepSeek V4 Pro on Raycaster
Game
MiniMax M3 on Rpg
DeepSeek V4 Pro on Rpg
Game
MiniMax M3 on Skyrim
DeepSeek V4 Pro on Skyrim
🥉MiniMax M3 on Twilightvale
DeepSeek V4 Pro on Twilightvale
Game
MiniMax M3 on Voxelcraft
DeepSeek V4 Pro on Voxelcraft
Page
MiniMax M3 on Aipbpromo
DeepSeek V4 Pro on Aipbpromo

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

DeepSeek V4 Pro

Strengths

  • Flagship reasoning tier on the same official API and 1M context as Flash
  • Ran the identical 50-prompt set as V4 Flash 0731 — a clean same-vendor A/B
  • Reasoning-first: thinks before writing every build

Trade-offs

  • Unranked — builds are on the bench but not yet scored by the Opus vision judge
  • Slower and pricier per build than Flash — the whole question is whether that buys quality

Pricing & context — the spec sheet

Spec MiniMax M3 DeepSeek V4 Pro
VendorMiniMaxDeepSeek
Context window1,048,576-token context — matches GLM-5.2 and Fable 51,000,000-token context window
Price$0.30 / 1M input tokens, $1.50 / 1M outputAPI · pro tier
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.DeepSeek's flagship tier, benched on `deepseek-v4-pro` via api.deepseek.com — the exact same 50 one-shot prompts, skill-infused game prompt and pipeline as the V4 Flash 0731 run, so the two runs are directly comparable side by side. DeepSeek's own line on the 0731 Flash refresh is that its post-training now beats the older V4-Pro-Preview — this run tests the current Pro against that claim.
Release2026-06-182026-07
Bench coverage47/47 scored · avg 7.97/100/50 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 DeepSeek V4 Pro 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, checking whether deepseek's pro tier is worth the premium over flash 0731 → DeepSeek V4 Pro. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — MiniMax M3 vs DeepSeek V4 Pro

Which is better, MiniMax M3 or DeepSeek V4 Pro?

On Goldie Bench, MiniMax M3 averages no scored verdicts yet across the shared tasks, with 2 gold, 1 silver, 4 bronze overall. DeepSeek V4 Pro 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 DeepSeek V4 Pro?

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. DeepSeek V4 Pro: DeepSeek's flagship tier, benched on `deepseek-v4-pro` via api.deepseek.com — the exact same 50 one-shot prompts, skill-infused game prompt and pipeline as the V4 Flash 0731 run, so the two runs are directly comparable side by side. DeepSeek's own line on the 0731 Flash refresh is that its post-training now beats the older V4-Pro-Preview — this run tests the current Pro against that claim.

What's the context window for MiniMax M3 vs DeepSeek V4 Pro?

MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window. DeepSeek V4 Pro has a 1,000,000-token context window context window.

When should I pick MiniMax M3 over DeepSeek V4 Pro?

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 DeepSeek V4 Pro over MiniMax M3?

Pick DeepSeek V4 Pro for: Checking whether DeepSeek's pro tier is worth the premium over Flash 0731; Hard single-shot builds where extra reasoning depth may pay off. The trade-off is the weaknesses we logged on the bench: Unranked — builds are on the bench but not yet scored by the Opus vision judge; Slower and pricier per build than Flash — the whole question is whether that buys quality.

How does Goldie Bench score MiniMax M3 vs DeepSeek V4 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.

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