Fusion vs GPT-5.6 Sol

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 Fusion and GPT-5.6 Sol, 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.

Fusion · Dispatched from Agent OS for research-heavy prompts where ensemble accuracy outweighs single-model speed.

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%.

Where Fusion beat GPT-5.6 Sol

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

Where GPT-5.6 Sol beat Fusion

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.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Across 47 scored shared tasks, Fusion averaged 8.59/10, beating GPT-5.6 Sol's 8.16/10 by 0.43 points. Pick Fusion 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 Fusion and GPT-5.6 Sol both into the Agent Operating System and dispatch each from the kanban by task type — deep-research workflows where panel consensus beats single-model answers → Fusion, the hardest reasoning and code where being right beats being cheap → GPT-5.6 Sol. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Fusion vs GPT-5.6 Sol

Which is better, Fusion or GPT-5.6 Sol?

On Goldie Bench, Fusion averages 8.59/10 across the shared tasks, with 23 gold, 5 silver, 9 bronze overall. GPT-5.6 Sol averages 8.16/10, with 11 gold, 11 silver, 7 bronze. Fusion wins the head-to-head 30–17.

How much does Fusion cost vs GPT-5.6 Sol?

Fusion: OpenRouter's Fusion API dispatches a single prompt to multiple frontier models and ensembles the answers. Premium panel: Fable 5 + GPT-5.5. Budget panel: cheaper open-weights models. Roughly half the per-token cost of a Fable 5 solo call. 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.

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

Fusion has a Varies — depends on which panel models are dispatched context window. GPT-5.6 Sol has a 1,050,000 tokens context window.

When should I pick Fusion over GPT-5.6 Sol?

Pick Fusion for: Deep-research workflows where panel consensus beats single-model answers; Cost-sensitive operators who want Fable-5-class output at ~half the bill; Production agents that benefit from vendor-redundancy on every call. The trade-off is the weaknesses we logged on the bench: Ensemble latency higher than any single model (panel calls run in parallel but the slowest still gates the response); No per-task goldiebench scoring yet — bench rank pending.

When should I pick GPT-5.6 Sol over Fusion?

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).

How does Goldie Bench score Fusion vs GPT-5.6 Sol?

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: Fusion · GPT-5.6 Sol · back to the leaderboard