Layer 02 — Models
The best model in the world, per request
Adaptive intelligence scores every request and routes it to the best available model for the job: frontier reasoning for the hard problems, fast open-weight where it wins. One endpoint, the best answer either way.
Adaptive intelligence
Every request, scored and routed.
You don't pick a model per feature and hope. The router reads each request and sends it where it will be answered best, fastest, and cheapest, in that order.
Scored per requestEvery request is scored and routed to a tier: xfast, fast, balanced, or deep. The hard problems get frontier reasoning; the lookups never wait behind them.
Best available, not best knownRouting targets whatever wins the job today, frontier or open-weight. When a better model ships, your traffic finds it.
Mid-stream failoverProvider 500s and rate limits reroute in flight. The user never sees a retry, and quality never quietly degrades.
Adapt Router
“What’s our uptime?”xfast
“Draft a reply”fast
“Chart signup trends”balanced
“Root-cause the 401s”deep
TierModelBest forp50Cost
- xfastgpt-5.6-luna-lowlookups & FAQs121ms1×
- fastgemini-3.7-flashsummaries & drafts180ms1.3×
- balancedgpt-5.6-sol-mediumanalysis & synthesis388ms~11×
- deepfable-5-xhighhard reasoning412ms~17×
Output quality
The ceiling is always the frontier.
Routing isn't a cost trick. It's how you get the best possible answer to every request: frontier intelligence where it matters, and never a mismatched model anywhere.
Think outcomes, not models
Nobody on your team needs a model opinion to ship. You describe the job; the router worries about which model delivers the best result today.
The query picks the model
Every request is parsed for intent, difficulty, and latency budget, then sent to the model best at exactly that job. A lookup never burns deep reasoning; a hard problem never gets a fast guess.
Frontier and emerging frontier
Claude, GPT, and Gemini next to the best open-weight releases, the day they ship. The mix shifts as the field shifts, without you renegotiating your stack.
Cost
Cut token spend by up to 80%.
Most requests don't need frontier reasoning; they need a fast, cheap model that gets them right. Route the lookups and drafts to 1x-cost models, cache what repeats, and save the expensive tokens for the problems that earn them.
Token spend
everything on frontier1.0× baseline
adaptively routedup to 80% less
lookups at 1× · drafts at 1.3× · frontier only where it earns ~17×
Zero lock-in
The best model in 12 months hasn't shipped yet.
The leaderboard changes every quarter; your architecture shouldn't. With a neutral router in between, model choice stops being a bet you can lose and becomes a setting.
Neutral by designAdapt routes to models; it does not sell you one. There is no incentive anywhere in the stack to steer your traffic.
A model is a stringNot an architecture decision, not a migration. Switching providers is a config change, shipped in an afternoon.
Future-proof by defaultWhatever leads the field in 3, 6, or 12 months, your integration already speaks its language.
Best model right now
- 1fable-5-xhighhard reasoning
- 2gpt-5.6-sol-xhighanalysis & synthesis
- 3kimi-k3-maxopen-weight reasoning
- 4opus-5-highagentic coding
Never bet wrong
on a model.
The best available intelligence for every request, at the lowest cost that gets it right.