Every AI Request Deserves the Right Model.
Intelligent routing for AI coding agents. Optimize every step, preserve prompt caches, reduce repeated context, and help stop runaway costs.
Agent-Aware Routing
Why use an expensive model for every step? Air Router can route routine agent operations to lower-cost models while reserving stronger models for demanding work.
User request“Build an Authentication System”
Illustrative example. Real routes depend on each request.
Pay for intelligence where it matters.
Autonomous Agent Protection
Detect repeated searches, edits, and tool calls. Air Router is designed to interrupt unproductive loops before they consume more tokens.
Stopped with clear feedback for the agent.
Identify repeated agent behavior.
Nudge agents away from unproductive repetition.
Can stop persistent loops with clear feedback.
Prompt Cache Intelligence
Air Router considers existing prompt caches before switching models, helping avoid expensive cache rebuilds.
Illustrative units, not real prices. 100K-token context. Model A: 1.0 per 1K tokens, cached input 0.1. Model B: 0.6 per 1K tokens, nothing cached yet.
Optimize the actual bill, not just the model price list.
Complexity-Aware Model Selection
A simple edit inside a massive repository shouldn’t automatically require your most expensive model.
Task
Rename a variable
Context150K tokens
ComplexityLow
Recommended TierFast
Task
Design distributed architecture
Context12K tokens
ComplexityHigh
Recommended TierAdvanced
Large context, simple changeFast
Route by task difficulty, not prompt length alone.
Quality-Aware Routing
Once an active task requires a stronger model tier, Air Router avoids moving that task to a weaker tier.
Passing on Advanced · lower tiers locked
Simple first steps start on a fast model.
Failed checks can move the task up a tier.
Lower tiers stay locked until the task ends.
Save on simple work while demanding tasks stay on stronger models.
Context Efficiency Engine
Reduce repeated file content, completed tool history, and unnecessary prompt overhead before model execution.
Before optimization
150K tokens
After optimization
65K tokens
Illustrative example150K → 65K tokens
Illustrative example — actual reduction depends on the request.
Send repeated files and search results once.
Condense completed tool calls into a short history.
Compact code context with its language in mind.
Send less redundant context. Preserve what matters.
Hybrid AI Infrastructure
Air Router can run eligible routine workloads on AirGPU, AirLLM’s own GPU infrastructure, with fallback to cloud model providers when needed.
Rerouted to an eligible cloud provider.
Only eligible routine requests run on AirGPU; everything else, and anything AirGPU can’t serve at that moment, goes to cloud model providers.
Combine efficient infrastructure with cloud-scale capability.
Effective Cost Routing
Air Router considers token accounting, cache state, model capabilities, and context requirements when evaluating model choices.
| Metric | Model A | Model BSelected | Model C |
|---|---|---|---|
| Advertised rate | Low | Medium | High |
| Cache state | Cold | Warm | Cold |
| Context fit | Yes | Yes | Yes |
| Effective cost | Medium | Lowest | High |
Model B Selected.Lowest estimated effective cost for this request.
Price each candidate on the tokens it would really process.
Count reusable cached context in the comparison.
Rank only models that fit the request’s context.
The best route is the one that makes economic sense for the actual request.
Agent Reliability
Repair eligible malformed tool calls, detect empty responses, and avoid repeatedly selecting unreliable models during a session.
Simulated example. Repairs apply only to eligible malformed calls.
Fix eligible malformed tool calls before the agent sees them.
Catch empty model responses instead of passing them on.
Avoid reselecting models that keep failing in a session.
Fewer avoidable failures. More productive agent execution.
Intelligent Tier Optimization
Air Router can use compressed context estimates when evaluating model tiers, with safeguards against inappropriate downgrades.
Large context
140K tokensSmaller eligible payload
60K tokensSuitable model tier
Size alone suggestsSmaller prompts shouldn’t mean weaker reasoning.
Built for AI Coding Agents
Traditional gateways connect applications to models. Air Router goes further by optimizing how coding agents use those models.
| Capability | Conventional Model Gateway | Air Router |
|---|---|---|
| Model Routing | Yes | Yes |
| Provider Fallback | Yes | Yes |
| Agent-Step Routing | Varies | Yes |
| Task Complexity Analysis | Varies | Yes |
| Cache-Aware Cost Decisions | Varies | Yes |
| Agent Loop Protection | Varies | Yes |
| Context Deduplication | Varies | Yes |
| Safe Tier Escalation | Varies | Yes |
| AirGPU + Cloud Routing | Varies | Yes |
Compared with a generic basic gateway. Capabilities of specific products vary.
Developer Compatibility
Connect compatible coding tools through familiar API formats without rebuilding your workflow.
/v1/messages/v1/chat/completions/v1/responsesSetup steps and supported features vary by tool and version.
View Integration GuidesIntelligent model selection. Efficient context. Cache-aware routing. Built for the next generation of AI coding agents.
One intelligent routing layer for your AI development workflow.Keep exploring
Why coding agents need per-step model routing, and what to look for in a router.
Read SetupRoute Claude Code through Air Router: base URL, key and routing model.
Read SetupPoint Codex CLI at Air Router with a custom Responses provider.
Read