Cheaper defaults
Don't burn premium models on simple requests like moving files or pushing to git — routine work lands on lower-cost models first.
More tokens. Lower spend.
Model Oracle AI runs every coding-agent request through one gateway, keeps cache-friendly conversations on stable models, and shifts routine work to cheaper defaults before it reaches a vendor.
Cache-aware intelligence
Warm conversations stay on stable models so prompt-cache locality is not reset unnecessarily.
Cross-provider fallback
Requests move around slow, expensive, rate-limited, or unavailable providers.
Team-wide tracking
Spend and token usage stay visible across every team, tool, provider, and API key.
modeloracle.ai
Every Request. The Right Model. Automatically.
Stop choosing between models for every request. Default to auto and let Model Oracle AI decide what runs based on cost, quality, latency, context, and your team's preferences — or pin a stable logical model when a workload needs a fixed model family.
Intent selected
auto
Selected model
GLM-5.2
Fallback
Claude Opus 5
AI Decision Engine
OpenCode -> intent policy -> best available model
Expose intent, not provider menus.
Teams can stop asking developers to choose between GPT-5, Claude, Kimi K3, Qwen, or the next breakthrough model for every request. Default to intent and let Model Oracle AI choose the provider and model behind the scenes — or pin a logical model when you need a fixed model family. Provider-specific model IDs and fallback choices stay behind one endpoint.
Developer-facing choices
auto
Model catalog behind the policy
GPT-5.6 Sol Claude Opus 5 DeepSeek V4 Pro Kimi K3 GLM-5.2
Swap models without swapping code.
The best model for coding work changes constantly as providers ship upgrades, prices
move, latency shifts, and older models fade. Keep your application code pointed at
auto,
update the model catalog and policy once, and every developer and coding agent picks up
the better choice immediately: zero config edits, zero PRs, zero redeploys.
const api = "https://model-oracle.ai/api/v1";
// Your code keeps using auto. Model Oracle AI can
// move to the best available model without a deploy.
const res = await fetch(`${api}/chat/completions`, {
method: "POST",
headers: {
"Authorization": "Bearer ${token}",
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "auto",
messages: [{ role: "user", content: "Refactor this module." }],
}),
});
Add or prioritize a model in the control plane once. Every agent using
auto
can use the new best choice on the very next request.
One control plane for every coding agent model decision.
Model Oracle AI still tracks every provider, model, prompt, completion, cost, and fallback. The difference is that developers use simple intent labels while teams govern the AI decision policy centrally.
Lower AI costs
Shift routine coding work to cheaper models without losing visibility.
Faster coding
Prefer low-latency models when speed matters more than maximum reasoning depth.
Intent-based selection
Default to auto and let Model Oracle AI choose behind the scenes, or pin a stable logical model.
Automatic failover
Move around provider errors, quota limits, and slow responses.
Full observability
Inspect prompts, completions, tools, cost, latency, errors, and provider behavior.
Team policies
Centralize model access, defaults, allowlists, and AI decision rules for teams.
Works with the tools teams already use
Cursor, Claude Code, OpenCode, Codex, Cline, Continue, Aider, and other coding agents that can talk to an OpenAI-compatible endpoint.
Built for the coding-agent wedge.
- OpenCode-compatible proxy
- Provider model registry and metrics dashboard
- Prompt and completion inspection
- Auto model selection
- Cost-aware model intelligence
- Provider fallback and automatic retries
- Stuck-session escalation
- Caching and cost optimization
- Team analytics
- Public benchmarks for coding-agent model selection
- Local setup in minutes
