Fusion vs Hermes MoA

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 Hermes MoA, side by side, on 42 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.

Hermes MoA · Run from the Mixture tab in the Hermes Agent OS. On this bench the panel built each demo and the aggregator merged the best of every draft.

Where Fusion beat Hermes MoA

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

Where Hermes MoA beat Fusion

The tasks where I gave Hermes MoA 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 42 scored shared tasks, the averages are essentially tied — Fusion 8.60 vs Hermes MoA 8.38. 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 Fusion and Hermes MoA 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, high-stakes single prompts where ensemble quality beats single-model speed → Hermes MoA. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — Fusion vs Hermes MoA

Which is better, Fusion or Hermes MoA?

On Goldie Bench, Fusion averages 8.60/10 across the shared tasks, with 22 gold, 11 silver, 5 bronze overall. Hermes MoA averages 8.38/10, with 12 gold, 8 silver, 4 bronze. Fusion wins the head-to-head 24–18.

How much does Fusion cost vs Hermes MoA?

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. Hermes MoA: Hermes Mixture of Agents dispatches one prompt to a configurable panel of frontier models in parallel, then a named aggregator reads every draft and writes one better final answer. Default panel: Claude Opus 4.8 + GPT-5.5, aggregated by Opus 4.8 — all via the OpenRouter key. Unlike a black-box ensemble, every slot is yours to swap from the Mixture tab in the Agent OS.

What's the context window for Fusion vs Hermes MoA?

Fusion has a Varies — depends on which panel models are dispatched context window. Hermes MoA has a Varies — the sum of the panel models' contexts (Opus 4.8 + GPT-5.5) context window.

When should I pick Fusion over Hermes MoA?

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 Hermes MoA over Fusion?

Pick Hermes MoA for: High-stakes single prompts where ensemble quality beats single-model speed; Squeezing frontier-plus output from models you already have while Fable 5 / GPT-5.6 are still in preview; Production agents that want a configurable panel + vendor-redundancy on every call. The trade-off is the weaknesses we logged on the bench: Latency is the panel's slowest draft plus the aggregator pass — ~110–140s per single-file build vs a solo model's one call; Costs more per task than any single model (every panel slot + the aggregator are separate calls); Only 3 of 42 bench tasks run so far — a representative slice, not the full board.

How does Goldie Bench score Fusion vs Hermes MoA?

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 · Hermes MoA · back to the leaderboard