Antigravity supports both Claude Opus 4.6 and Gemini 3.1 Pro as of 2026. Instead of routing every task to a single model, a multi-AI strategy lets you assign each task to the model that handles it best — optimizing for quality, speed, and cost simultaneously.
This masterclass walks through building a production-grade multi-AI application in Antigravity, from routing architecture through monitoring and security.
Why a Single-Model Approach Falls Short
The intuition to always use the best model is understandable, but it creates real problems in production:
Cost: Claude Opus 4.6 and Gemini 3.1 Pro have different pricing profiles across different task types. Routing all traffic to one model ignores the efficiency available from using each where it excels.
Speed: Complex reasoning with Opus 4.6 takes longer than a fast completion with Gemini. When users are waiting on autocomplete, latency matters more than peak reasoning quality.
Rate limits: Depending entirely on one model means a single rate limit event can take down your entire application. Spreading load across models eliminates this single point of failure.
A multi-AI strategy treats model selection as an architectural decision, not an afterthought.
Designing the Task-Based Routing Logic
The first question is which tasks each model handles best.
Claude Opus 4.6 excels at:
- Complex multi-step code generation and refactoring
- Long-document analysis with nuanced follow-up questions
- Strict instruction following in automated workflows
- Safety-sensitive content generation
Gemini 3.1 Pro excels at:
- High-throughput text completion, summarization, and translation
- Multimodal tasks involving images, audio, and video
- Google ecosystem integrations (Drive, Docs, Workspace APIs)
- Large-scale batch processing
Implementation:
type ModelTask =
| "complex_reasoning"
| "code_generation"
| "fast_completion"
| "multimodal"
| "batch_processing";
function selectModel(task: ModelTask): "claude-opus-4-6" | "gemini-3-1-pro" {
const claudeOptimalTasks: ModelTask[] = [
"complex_reasoning",
"code_generation",
];
return claudeOptimalTasks.includes(task)
? "claude-opus-4-6"
: "gemini-3-1-pro";
}When prompting Antigravity to implement this, try: "Build a model router that selects between Claude Opus 4.6 and Gemini 3.1 Pro based on task type. Define task types as a TypeScript enum."
Implementing Multi-Model Calls in Antigravity
Antigravity's AgentKit 2.0 orchestration layer can manage model switching. A prompt that works well:
Implement a multi-AI backend with these requirements:
1. Router: Automatically select Claude Opus 4.6 or Gemini 3.1 Pro based on task type
(complex_reasoning / code_gen → Claude, fast_completion / multimodal / batch → Gemini)
2. Fallback: If the primary model fails, switch to the other automatically
3. Logging: Record model used, token count, latency, and cost estimate for every call
4. Cost cap: If daily spend exceeds a threshold, downgrade to a cheaper model automatically
Return TypeScript using Next.js App Router API routes.
This single prompt produces the routing logic, API call layer, and logging instrumentation together.
Fallback Chain Architecture
A simple "model A → model B" fallback isn't enough for production. Use a three-tier structure:
Tier 1 — Primary execution: Run on the optimal model for the task.
Tier 2 — Peer fallback: If the primary fails (rate limit, timeout, error), switch to the other premium model. Quality is maintained.
Tier 3 — Degraded execution: If both premium models are unavailable, fall back to a smaller, faster, cheaper model. Responses continue, at lower quality.
async function executeWithFallback(
prompt: string,
task: ModelTask
): Promise<AIResponse> {
const primary = selectModel(task);
const peer =
primary === "claude-opus-4-6" ? "gemini-3-1-pro" : "claude-opus-4-6";
try {
return await callModel(primary, prompt);
} catch (primaryError) {
console.warn(`Primary ${primary} failed, trying peer ${peer}`);
try {
return await callModel(peer, prompt);
} catch {
console.error("Both premium models failed — using minimal model");
return await callModel("gemini-flash", prompt);
}
}
}Antigravity generates this pattern including exponential backoff for retries.
Three Cost Optimization Techniques
These three approaches can reduce your AI spend by 30–40% without meaningful quality loss.
Semantic caching: Cache responses by semantic similarity rather than exact string match. When a new prompt is very close to a cached prompt (cosine similarity above ~0.95), return the cached response instead of making a new API call.
async function cachedGenerate(prompt: string): Promise<string> {
const embedding = await getEmbedding(prompt);
const cached = await vectorCache.findSimilar(embedding, { threshold: 0.95 });
if (cached) return cached.response;
const response = await generate(prompt);
await vectorCache.store(embedding, response);
return response;
}Use Supabase pgvector for the cache — Antigravity has strong support for this combination.
Batch processing: Queue non-real-time tasks (overnight reports, email drafts, scheduled summaries) and process them in batches. Most model providers offer batch APIs at lower cost per token.
Model tiering: Don't send every request to a premium model. Route simple requests to a smaller, faster model first. Only escalate to Claude Opus 4.6 or Gemini 3.1 Pro when complexity demands it. Antigravity can implement a complexity classifier that makes this decision automatically.
Production Monitoring
Multi-AI monitoring is more complex than single-model tracking. Track these metrics:
Per-model performance:
- Latency by percentile (P50, P95, P99) for each model
- Success and error rates, broken down by error type
- Actual token consumption and cost per model
Routing quality:
- Distribution of model selections by task type
- Fallback frequency (high fallback rates signal upstream problems)
Business metrics:
- Average AI cost per request
- Monthly spend trajectory vs. budget
async function monitoredAICall(
task: ModelTask,
prompt: string
): Promise<AIResponse> {
const model = selectModel(task);
const start = Date.now();
try {
const response = await callModel(model, prompt);
await metrics.record({
model, task,
duration: Date.now() - start,
tokens: response.usage.total_tokens,
cost: calculateCost(model, response.usage),
success: true,
});
return response;
} catch (error) {
await metrics.record({
model, task,
duration: Date.now() - start,
success: false,
errorType: (error as Error).name,
});
throw error;
}
}Prompt Antigravity to connect this logging to Google Cloud Monitoring or Datadog for dashboard visualization.
Security Essentials
API key management in a multi-model environment needs care:
Store all API keys in Google Secret Manager — never in source code or environment files checked into version control. Configure automatic key rotation. Avoid logging full prompt content if user data is present.
Antigravity generates Secret Manager integration when you include "fetch API keys securely via Secret Manager" in your prompt.
Putting It All Together
A structured prompt that gets Antigravity to produce the complete integrated implementation:
Build a production multi-AI backend with the following:
1. Task router: Select Claude Opus 4.6 or Gemini 3.1 Pro by task type
2. Three-tier fallback chain with exponential backoff
3. Semantic caching using Supabase pgvector (threshold 0.95)
4. Daily cost cap with automatic model downgrade when exceeded
5. Full observability: log model, latency, token count, cost estimate per call
6. API key management via Google Secret Manager
TypeScript, Next.js App Router. Include unit tests for the router and fallback logic.
Antigravity generates the full implementation from this, including test coverage.
Looking back
A multi-AI architecture using Claude Opus 4.6 and Gemini 3.1 Pro in Antigravity lets you optimize across quality, cost, and reliability simultaneously. Task routing assigns each model to its strengths. Fallback chains keep your application running through transient failures. Caching and tiering cut costs without hurting the user experience.
The implementation details — the router code, the fallback logic, the monitoring hooks — Antigravity handles for you. The decisions that matter most — which tasks justify Opus 4.6, what cost thresholds make sense for your usage — still require your judgment. This guide gives you the framework to make those calls confidently.