How to Route LLM Requests to the Right Model for Each Task

Short answer: route requests in your application, then use one CometAPI key and the OpenAI-compatible base URL https://api.cometapi.com/v1 to call the selected model. Send repetitive, easy-to-check work to a low-cost tier; latency-sensitive customer interactions to a fast tier; and ambiguous or high-impact work to a high-accuracy tier. Keep the labels as your own policy—not as a universal model ranking—and measure every tier on the same test set.

This guide builds that three-tier router with a compact Python example, bounded fallback, and a cost model that counts retries and rejected outputs. The example uses current model IDs from the CometAPI catalog, but the routing logic stays separate so models can be replaced without rewriting the application.

What is LLM routing?

LLM routing is the process of sending each request to the model or service tier that best fits its task, latency target, quality requirement, and budget.

How Should You Route LLM Requests by Task?

As of August 20, 2026, the following model IDs and catalog pricing fields were available through the public CometAPI Models API. The estimated consumer rates below apply the catalog's current ratio value to its input and output baseline prices, following the CometAPI pricing guide. Confirm the final rate shown for your account before production use.

“Fast” means the route has a latency objective; “high accuracy” means it has a stricter quality objective. Neither label proves that one model is always fastest or most accurate. Benchmark p50 and p95 latency, task pass rate, and cost per accepted output on your own traffic before making the mapping permanent.

How Do You Set Up CometAPI for an LLM Router?

You need a CometAPI API key, Python 3.10 or later, and the OpenAI Python package. Store the key server-side rather than in source code.

pip install openaiexport COMETAPI_KEY="your-key-here"

The example uses POST /v1/chat/completions. CometAPI documents this as a shared interface for multiple providers, but parameter behavior can still vary by model. Check the current model entry and Chat Completions reference before adding provider-specific fields.

What do you need to build an LLM router?

Map stable tasks to service tiers. Do not ask another LLM to classify every request unless simple application signals are insufficient. A support tag is predictably cheap-tier work; a live reply is latency-sensitive; a policy review deserves the strictest quality gate.

Validate the output. Successful HTTP status does not mean the result is usable. Pass a task-specific validator to the router. A classification validator can check an allowed label; a customer-reply validator can enforce length and prohibited claims; a structured workflow can validate a JSON schema.

Fallback narrowly. Try the next approved route after a timeout, 408, 429, temporary 5xx, or a bounded quality-gate failure. Do not use another model to hide malformed input, an invalid key, or unsupported parameters.

How Do You Build an LLM Router in Python?

import osimport time​from openai import APIError, OpenAI​client = OpenAI(    api_key=os.environ["COMETAPI_KEY"],    base_url="https://api.cometapi.com/v1",    max_retries=0,    timeout=20,)​MODELS = {    "cheap": "deepseek-v4-flash",    "fast": "gemini-3.7-flash",    "accurate": "claude-opus-5",}​# Put the preferred tier first; later tiers are fallbacks.ROUTES = {    "tag": ["cheap", "fast", "accurate"],    "reply": ["fast", "cheap", "accurate"],    "policy_review": ["accurate", "fast", "cheap"],}​​def retryable(error):    status = getattr(error, "status_code", None)    return status is None or status in {408, 429} or (status and status >= 500)​​def route(task, prompt, validate=lambda text: True):    attempts = []    for tier in ROUTES.get(task, ROUTES["reply"]):        model = MODELS[tier]        started = time.perf_counter()        try:            response = client.chat.completions.create(                model=model,                messages=[{"role": "user", "content": prompt}],                max_tokens=400,            )            text = response.choices[0].message.content or ""            attempts.append({                "tier": tier,                "model": model,                "latency_ms": round((time.perf_counter() - started) * 1000),                "accepted": validate(text),            })            if attempts[-1]["accepted"]:                return {                    "text": text,                    "route": tier,                    "model": model,                    "usage": response.usage.model_dump() if response.usage else None,                    "attempts": attempts,                }        except APIError as error:            attempts.append({"tier": tier, "model": model, "status": error.status_code})            if not retryable(error):                raise​    raise RuntimeError(f"No route passed: {attempts}")​​if __name__ == "__main__":    result = route(        "reply",        "Reply to a customer asking when their refund will arrive. Do not promise a date.",        validate=lambda text: 30 <= len(text) <= 600 and "guarantee" not in text.lower(),    )    print(result)

How do you bound retries before fallback?

Keep SDK retries at zero and wrap each model call with an explicit limit. The helper below retries only retryable API failures once, then raises so the outer route can move to the next approved tier.

MAX_ATTEMPTS_PER_MODEL = 2​def call_model(model, prompt):    for attempt in range(1, MAX_ATTEMPTS_PER_MODEL + 1):        try:            return client.chat.completions.create(                model=model,                messages=[{"role": "user", "content": prompt}],                max_tokens=400,            )        except APIError as error:            if not retryable(error) or attempt == MAX_ATTEMPTS_PER_MODEL:                raise            time.sleep(min(0.5 * (2 ** (attempt - 1)), 2.0))

In route(), replace the direct client.chat.completions.create(...) call with call_model(model, prompt). With three tiers, one request stops after at most six provider calls; validation failures still escalate once per tier instead of retrying the same output.

Run it with python3 llm_task_router.py. To change providers or model generations later, update MODELS; the task policy and response contract stay in one place.

The example uses only parameters shared by the selected models. Add model-specific token controls through an adapter layer after checking model compatibility.

How Do You Test an LLM Routing Policy?

First verify that the deterministic policy selects the intended primary tier. These are routing expectations, not provider performance results:

A successful live smoke test returns the answer plus the selected tier, model ID, token usage, and every attempt. Actual token and latency values will vary:

{  "text": "...",  "route": "fast",  "model": "gemini-3.7-flash",  "usage": {    "prompt_tokens": "measured value",    "completion_tokens": "measured value"  },  "attempts": [    {      "tier": "fast",      "model": "gemini-3.7-flash",      "latency_ms": "measured value",      "accepted": true    }  ]}

For a real comparison, run the same labeled requests through all three models. Record task pass rate, p50 and p95 latency, error rate, input and output tokens, fallback rate, and human-review rate. The metric that matters is usually cost per accepted output, not cost per API call.

How much does multi-model routing cost?

Use one workload shape for a fair comparison. Assume 1 million total tokens: 800,000 input tokens and 200,000 output tokens. Using the catalog-derived rates checked on August 20, 2026:

If traffic is 60% cheap, 30% fast, and 10% high accuracy, the projected blended token cost is about $1.19 per 1 million total tokens. Sending the same mix entirely to the high-accuracy route would be about $7.20 under these assumptions. This is a pricing calculation, not proof that the mixed policy will meet your quality target.

Retries and rejections change the result. A 5% one-time retry rate raises the $1.19 projection to roughly $1.25. If a low-cost output fails validation and the entire request is repeated on the high-accuracy tier, count both calls. Track accepted outputs so an apparently cheap model does not hide review or regeneration costs.

What Are the Most Common LLM Routing Failures?

The error and retry guide recommends retrying rate limits and temporary platform failures with backoff, while malformed requests and authentication failures should be fixed. The fallback guide likewise keeps model fallback ordered and explicit.

Application Routing vs. CometAPI Auto: Which Should You Use?

Use application routing when control and reproducibility matter. Keep the decision in your code when tasks are stable and you need fixed model identities, per-tier budgets, custom validators, and an auditable fallback order. This approach also makes it easier to compare the same model map across releases.

Use CometAPI Auto when reducing routing maintenance matters more. Set model=auto for a balanced default or model=auto-high when quality has higher priority. CometAPI selects an eligible model dynamically from the request characteristics and current routing pool, so the underlying model can vary; that makes Auto less suitable when every run must use the same model or model-specific parameters.

How Do You Run LLM Routing in Production?

Refresh the model registry. Call GET https://api.cometapi.com/api/models during deployment or startup and fail the release if a configured ID or required endpoint is missing. Model IDs, prices, and capabilities can change.

Keep provider-specific options out of the router. A common Chat Completions surface does not make every parameter identical. For example, support for logprobs, reasoning controls, or multiple candidates can differ. Put those differences in tested adapters.

Cap traffic and output. Limit concurrency before requests leave the application, use exponential backoff with jitter for 429, and set an output-token ceiling. CometAPI's rate-limit guide recommends the same application-side controls.

Log the decision. Record task type, policy version, chosen tier, model ID, latency, token usage, validation result, retry count, fallback reason, and cost estimate. Avoid logging secrets or unnecessary customer content.

Promote routes with evidence. Keep a labeled evaluation set for each task. Roll out mapping changes gradually, compare them with the previous policy, and preserve a quick rollback path.

Frequently Asked Questions

Does CometAPI automatically decide which model is cheap, fast, or accurate?

This tutorial keeps that policy in application code. CometAPI provides the shared key, base URL, model catalog, Chat Completions interface, and documented fallback building blocks. Your team defines what each tier means and which model has passed its tests.

Can one CometAPI key call models from different providers?

Yes. For OpenAI-compatible text routes, use https://api.cometapi.com/v1 and change the model value. The current catalog should be checked before deployment.

Why not send every request to the cheapest model?

The lowest token rate can become expensive if outputs fail validation, require retries, or create human-review work. Compare cost per accepted result and keep high-impact tasks behind stricter quality gates.

Should quality failure trigger fallback?

Only when the failure is machine-detectable and the escalation is bounded. A schema error, missing required field, or prohibited promise can justify one escalation. Vague dissatisfaction should become evaluation data rather than an unlimited retry loop.

How often should the model map change?

Change it when current catalog data and a repeatable evaluation show a better trade-off. Do not rotate models only because a new name appears in the catalog.

Can I add an OpenAI model later?

Yes. Add a current OpenAI-compatible model ID to MODELS, test the same request and response contract, and place it in the route order. The client, key, and base URL remain unchanged.

How Do You Keep an LLM Routing Policy Maintainable?

The easiest multi-provider router is not an autonomous black box. It is a short, versioned task policy backed by shared API access, current model metadata, a quality validator, and a narrow fallback chain. CometAPI reduces the connection work to one key and one OpenAI-compatible base URL; your application keeps control of cost, latency, and quality decisions.