Split the beam. Read the spectrum. The convergences are the coastline. The divergences are the interesting water.
Spectro sends your prompt to N different AI models in parallel, then analyzes what they agree on (convergences) and where they diverge (divergences). The output isn't any single model's answer — it's the pattern across all of them.
This is not model routing (picking the best model). This is not ensemble voting (averaging answers). This is spectral analysis of model cognition — treating the differences between models as signal, not noise.
Every paradigm essay in the SuperInstance corpus points at the same truth: different models are different perspectives, not different quality. The convergences between independent models reveal high-confidence territory. The divergences reveal the interesting edges — the places where the question is genuinely uncertain, or where a model sees something the others miss.
- A question where all 5 models agree → high confidence, low exploration value
- A question where models disagree → genuinely uncertain, needs human judgment
- A question where one model is unique → either a blind spot or a breakthrough
Spectro makes this visible.
pip install spectro-spectrograph# Set your DeepInfra API key (or any OpenAI-compatible provider)
export DEEPINFRA_API_KEY=...
# Run a spectral analysis across 5 models
spectro "What is the most important quality in a senior engineer?"Your Prompt
│
▼
┌─────────────────────────────────┐
│ Spectro Engine (parallel) │
│ │
│ Model A ──→ Response A │
│ Model B ──→ Response B │
│ Model C ──→ Response C │
│ Model D ──→ Response D │
│ Model E ──→ Response E │
│ │
└─────────────┬───────────────────┘
│
▼
┌─────────────────────────────────┐
│ Analysis Layer │
│ │
│ 1. Extract key concepts │
│ 2. Find convergences (shared) │
│ 3. Find divergences (unique) │
│ 4. Map the agreement space │
│ │
└─────────────┬───────────────────┘
│
▼
┌─────────────────────────────────┐
│ Spectrum Report │
│ │
│ ████ CONVERGENCE (high conf) │
│ ██░░ PARTIAL AGREEMENT │
│ ░░░░ DIVERGENCE (explore here) │
│ █░█░ UNIQUE INSIGHTS │
│ │
└─────────────────────────────────┘
# Default ensemble (5 models)
spectro "Should we use microservices?"
# Choose specific models
spectro "What causes bad code?" \
--models deepseek,seed-pro,ornith,nemotron
# Focus on a specific analysis type
spectro "Is this architecture sound?" \
--focus divergence
# Output as JSON
spectro "Best practices for API design" \
--format json
# Verbose mode (show full responses + analysis)
spectro "What is consciousness?" \
--verbosefrom spectro import Spectrograph
spec = Spectrograph(api_key="...")
result = spec.analyze(
prompt="What's the biggest risk in microservices?",
models=[
"deepseek-ai/DeepSeek-V4-Flash",
"ByteDance/Seed-2.0-pro",
"deepreinforce-ai/Ornith-1.0-35B",
],
)
print(result.convergences) # concepts all models share
print(result.divergences) # where models disagree
print(result.unique_insights) # what each model saw alone
print(result.confidence) # 0.0-1.0 agreement scoreSpectro is built on the paradigm documented across 1,600+ essays in the SuperInstance/AI-Writings corpus:
- The Spectrograph: Every model output is a composite beam. Splitting it reveals composition.
- The Ensemble Is the Experiment: The intelligence is in the relationship, not the individual.
- Charts Not Maps: Each model is a different chart of the same territory.
- Two Charts Same Ocean: Convergences show the ocean floor. Divergences show the interesting water.
- Cast Thin First: Cheap models discover the territory. Expensive models synthesize.
MIT