LLMopenrouter

OpenRouter: Fusion

Fusion turns your prompt into a small multi-model deliberation. A panel of expert models (see below) analyzes your prompt in parallel with web search and web fetch enabled, then a...

Anyone in the Space can @-mention OpenRouter: Fusion with the team's shared context — pooled credits, one chat, one memory.

All models

Starter is free forever — 1 Space, 100 credits/month, 1 MCP. No card.

Verdict

OpenRouter Fusion is a meta-model that routes requests across multiple underlying LLMs, selecting the best provider for each query. It trades predictable behavior for potential cost savings and uptime resilience — you get whichever model OpenRouter deems optimal at that moment. Best for teams who prioritize availability and cost over consistent model behavior, or who want to experiment without committing to a single vendor. Skip it if you need reproducible outputs or fine-grained control over which model handles your prompts.

Best for

Cost-optimized general-purpose queries
High-availability production systems
Prototyping without vendor lock-in
Workloads tolerant of response variance

Strengths

Fusion automatically routes to the best-performing available model, which means you get uptime guarantees even when individual providers face outages. The dynamic selection can reduce costs by steering cheaper models toward simpler queries while reserving premium models for complex tasks. The 128k context window matches mid-tier models, making it viable for document analysis and multi-turn conversations. You avoid vendor lock-in since the underlying provider can shift transparently.

Trade-offs

You sacrifice consistency — the same prompt may hit different models on different days, producing varied outputs and making debugging harder. Pricing is opaque until after the call, complicating budget forecasting. Without public benchmarks, you cannot predict performance on specific task types like code generation or reasoning. The routing logic is a black box, so you lose the ability to tune for latency, style, or domain expertise. Teams needing reproducible results or strict compliance trails should look elsewhere.

Specifications

Provider: openrouter
Category: llm
Context length: 128,000 tokens
Max output: —
Modalities: text
License: proprietary
Released: 2026-05-12

Pricing

Input: —
Output: —
Model ID: openrouter/fusion

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool — one plan, one balance for everyone.

Providers

Provider	Context	Input	Output	P50 latency	Throughput	30d uptime
openrouter	128k	$0.00/Mtok	$0.00/Mtok	—	—	—

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Summarize Meeting Notes

Read the following meeting transcript and extract a bulleted list of action items, including who is responsible and any deadlines mentioned. Keep each item to one sentence.

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Draft Customer Email

Write a polite, professional email responding to this customer inquiry. Acknowledge their concern, provide a clear answer, and offer next steps if needed.

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Explain Technical Concept

Explain the following technical concept in plain language for someone without a technical background. Use an analogy if it helps, and keep it under 150 words.

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Brainstorm Product Features

Given this product description, brainstorm five new features that would appeal to small business owners. For each feature, write one sentence explaining the benefit.

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Parse Structured Data

Extract the following fields from this invoice text and return them as JSON: invoice_number, date, vendor_name, total_amount, line_items (array of item and price).

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