Splicer · premium Nyquest feature

Multi-Model
Splicer.

Ask once. Compare multiple AI models. Get a consensus-backed answer.

Nyquest Splicer runs a single prompt across multiple AI models in parallel, streams each model's response, then generates a consensus answer showing where models agree, where they diverge, and which answer is most reliable.

How it works

One prompt → multiple models
→ consensus answer

Your prompt"Compare the risks of launching this AI product in healthcare and finance."01
Fan-outClaude · GPT · Gemini · DeepSeek · Qwen — in parallel, responses streamed02
Consensus EngineAgreement, dominant position, synthesized answer, and divergence points — in one card.03

Consensus-backed — a stronger signal across models, not a guarantee of truth.

Consensus Engine

87%agreement

Agreement, dominant position, synthesized answer, and divergence points — in one card.

Why Splicer

A serious edge for
high-stakes answers

Breadth

Multi-Model Intelligence

Run the same query across multiple AI models at once instead of trusting one model blindly.

Scoring

Consensus Scoring

Nyquest analyzes model agreement, the dominant position, a synthesized answer, and the divergence points.

Receipts

Side-by-Side Model Output

View each model's response alongside its token usage, latency, and cost.

Stakes

Better High-Stakes Answers

Ideal for research, legal review, technical planning, business strategy, compliance checks, and complex decisions.

Catalog

Full Model Catalog Support

Splice across models from the available Nyquest / OpenRouter catalog, with tier-based access controls.

Cost

Built on Nyquest Compression

Compression reduces prompt waste before fan-out, helping control costs when multiple models run at once.

See it in action

One question, four models,
one consensus

› "Compare the risks of launching this AI product in healthcare and finance."

Claude

Cautious regulatory analysis — emphasizes HIPAA, audit trails, and approval gates before scale.

1,240 tok · 2.1s · $0.004

GPT

Product-market and compliance framing — phased rollout with clear accountability owners.

1,090 tok · 1.8s · $0.005

Gemini

Broad market and operational risk — surfaces vendor, data-residency, and uptime concerns.

1,310 tok · 2.4s · $0.003

DeepSeek

Technical and cost-risk breakdown — inference cost, latency budgets, and failure modes.

980 tok · 1.6s · $0.001

Consensus Engine

84%agreement
Dominant position

Launch with controlled pilots

Divergence

Regulatory timing & compliance burden

Recommended answer

Start with low-risk workflows, human approval in the loop, and industry-specific guardrails. Run controlled pilots in both healthcare and finance before broad rollout, and stage compliance work to the regulatory timeline of each sector.

Illustrative example. Splicer surfaces a higher-confidence, consensus-backed answer — it does not guarantee correctness.

Availability

Scale the number of models
with your plan

PlanSplicer capacity
FreeUpgrade required to run Splicer
ProUp to 4 models per prompt
TeamsUp to 6 models per prompt
EnterpriseExpanded model workflows — options available
Compare plans →

Stop trusting
one model.

Splice your most important prompts across multiple models and let the Consensus Engine show you the strongest answer.