COUNSEL VS ROUTING
LLM routers optimise which model answers your question. Counsel makes all of them answer — then deliberates. Two products, two problems.
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A router uses a small classifier model to pick the best single LLM for your prompt, then forwards the prompt to that one model. The output is whatever that one model produces. There is no second opinion, no cross-check, no confidence signal — if the router picks wrong, the answer is wrong and you have no way to tell.
A council sends the prompt to multiple models in parallel. Each responds independently. They then review each other's responses anonymously and rank them on accuracy, completeness, and reasoning. A designated chairman synthesises the final answer with confidence scores and per-model attribution.
| LLM Router | LLM Counsel | |
|---|---|---|
| Models per query | 1 (best match) | 3–12 (your choice) |
| Output | Single model response | Synthesised answer |
| Confidence signal | None | Agreement %, per-model attribution |
| Reasoning trace | None | Full peer review available |
| Hallucination check | None | Cross-model verification |
| Best for | Cost optimisation | Decision quality |
| Latency | ~30ms added | ~3–4s total |
| Cost | Lower per query | Higher per query, lower per mistake |
Routing saves money. Counsel saves decisions. Most production AI stacks need both.
No — they solve different problems. Use a router for cost-sensitive volume; use Counsel for decision-quality work. Many teams use both.
Stage 1 runs every member in parallel, so total wall-clock is dominated by the slowest member plus a synthesis pass. Typical end-to-end is 3–4 seconds for a 4-member council.
Yes. A common pattern: route the easy 95% of traffic to a router; send the high-stakes 5% to Counsel. The cost delta is negligible at that mix; the quality lift is large.