Fusion: blending several models into one answer

Fusion (a “Mixture-of-Agents”) runs your prompt through several models in parallel, then a synthesizer merges their drafts into one answer — entirely in your browser, on your own keys. $0 markup. It isn't always better: it shines on hard, open-ended work and is wasteful on simple, factual asks.

How it works

Your prompt → several proposer models answer independently, in parallel → a synthesizer reads all the drafts and writes the single best response. No server step: it's just N parallel calls + one more, on your own API keys.

What it really costs

The honest number is the token multiplier: about 4–6× the tokens of a single model (≈4.4× for a long chat) — higher than the number of models because the synthesizer also reads every draft. You pay your providers directly for those tokens; Spanvero takes $0 and your keys never touch our servers.

The three presets

Self-Fusion ×3 — one strong model sampled 3× at different temperatures — the cheapest path, and research finds it often beats mixing different models (your model, ×3)
Best open trio — three similar-strength open models, merged — routes through your OpenRouter key (GLM + DeepSeek + Qwen)
Frontier panel — three top-tier models — real money, ~4–6× the tokens of one (frontier models)

When it's worth it — and when it isn't

Worth it: hard, open-ended problems where models make different mistakes a synthesizer can reconcile; reasoning, planning, tricky code; long-form drafting.

A waste: simple or factual asks (you pay 4–6× for a mushier answer); anything latency-sensitive (it's slower by design); mixing a weak model with a strong one (it can lower quality).

Fusion is a Spanvero Pro feature. The research behind it: Wang et al. 2024 (arXiv:2406.04692) and Li et al. 2025, Self-MoA (arXiv:2502.00674).

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