GPT-5.6 Sol vs MiniMax M3

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 GPT-5.6 Sol and MiniMax M3, 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.

GPT-5.6 Sol · Benched on GoldieBench as the flagship Sol at medium reasoning, one-shot, then headless-playtested. In the Agent OS it's the top tier of a routed stack — Sol on the hard calls, Terra for the bulk, Luna for the everyday 90%.

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

Where GPT-5.6 Sol beat MiniMax M3

The tasks where I gave GPT-5.6 Sol a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Where MiniMax M3 beat GPT-5.6 Sol

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

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Across 47 scored shared tasks, the averages are essentially tied — GPT-5.6 Sol 8.16 vs MiniMax M3 7.97. 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 GPT-5.6 Sol and MiniMax M3 both into the Agent Operating System and dispatch each from the kanban by task type — the hardest reasoning and code where being right beats being cheap → GPT-5.6 Sol, high-volume agent workflows where per-call cost dominates → MiniMax M3. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — GPT-5.6 Sol vs MiniMax M3

Which is better, GPT-5.6 Sol or MiniMax M3?

On Goldie Bench, GPT-5.6 Sol averages 8.16/10 across the shared tasks, with 11 gold, 11 silver, 7 bronze overall. MiniMax M3 averages 7.97/10, with 2 gold, 4 silver, 11 bronze. GPT-5.6 Sol wins the head-to-head 32–15.

How much does GPT-5.6 Sol cost vs MiniMax M3?

GPT-5.6 Sol: GPT-5.6 shipped as three models — Luna ($1/$6 per M), Terra ($2.50/$15) and Sol ($5/$30) — each with a same-price pro variant that ships a higher default reasoning effort. All share a ~1.05M-token context window and are rated High capability. Benched here on the flagship, Sol, at medium reasoning effort via OpenRouter. 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.

What's the context window for GPT-5.6 Sol vs MiniMax M3?

GPT-5.6 Sol has a 1,050,000 tokens context window. MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window.

When should I pick GPT-5.6 Sol over MiniMax M3?

Pick GPT-5.6 Sol for: The hardest reasoning and code where being right beats being cheap; One-shot game/sim prototypes you want shippable on the first prompt; The flagship slot in a routed Agent OS — Sol for the hard 10%, Luna/Terra for the rest. The trade-off is the weaknesses we logged on the bench: Priciest tier on the bench at $30/M output — only worth routing the hardest 10% of work to Sol; Reasoning can eat the token budget on big open-world briefs (one 0-byte failure until the budget was raised, then it built clean).

When should I pick MiniMax M3 over GPT-5.6 Sol?

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.

How does Goldie Bench score GPT-5.6 Sol vs MiniMax M3?

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:

Full model pages: GPT-5.6 Sol · MiniMax M3 · back to the leaderboard