Teams that route every request to the same frontier model usually aren’t choosing it — they’re defaulting to it, because switching used to mean a separate account, a separate SDK, and a separate invoice per provider. With a unified API, model choice becomes a per-request decision instead of a one-time architectural commitment. That makes “which model should this call use” a question worth actually answering.
Start with the failure mode, not the model
Before picking a model, decide what a wrong answer costs you. A support ticket triage classifier that’s wrong 5% of the time gets corrected by a human downstream. A contract clause extractor that’s wrong 5% of the time creates legal risk. The second case justifies paying for a stronger model; the first usually doesn’t.
A three-tier framework
Tier 1 — high-volume, low-stakes. Classification, extraction, summarization of short text, routing decisions. Use the cheapest capable model — something like Llama 3.1 70B at $0.35/$0.40 per million tokens. At high volume, the cost difference versus a frontier model compounds fast: a million requests a month at even a few thousand tokens each is a meaningful bill.
Tier 2 — general-purpose production traffic. Customer-facing chat, content drafting, coding assistance. Use a mid-cost frontier model like GPT-4o ($2.50/$10 per million) or Claude Sonnet 4 ($3/$15 per million). The choice between them usually comes down to context window (Sonnet 4’s 200K vs GPT-4o’s 128K) and whether you need vision input (GPT-4o) or prefer Anthropic’s tuning.
Tier 3 — high-stakes reasoning. Legal analysis, financial modeling, multi-step agentic planning. Use the strongest available model regardless of cost, because the cost of a wrong answer exceeds the cost of the extra tokens by orders of magnitude.
Comparing the actual numbers
| Model | Input $/M | Output $/M | Context | Best for |
|---|---|---|---|---|
| Llama 3.1 70B | $0.35 | $0.40 | 128K | High-volume, low-stakes tasks |
| GPT-4o | $2.50 | $10.00 | 128K | General production traffic, vision |
| Claude Sonnet 4 | $3.00 | $15.00 | 200K | Long-context, complex reasoning |
Because Aniron uses one OpenAI-compatible request format across providers, testing this framework against your own traffic is a model string change, not a migration:
// Same code, three different cost/quality tradeoffs
const models = [
'meta-llama/llama-3.1-70b',
'openai/gpt-4o',
'anthropic/claude-sonnet-4',
];
for (const model of models) {
const response = await client.chat.completions.create({
model,
messages: [{ role: 'user', content: prompt }],
});
console.log(model, response.choices[0].message.content);
}Don’t over-optimize on day one
It’s tempting to build a routing layer that picks the “optimal” model per request from day one. In practice, most teams get more value from shipping with a sensible default (usually a Tier 2 model) and adding fallback chains for reliability, then splitting traffic into tiers once they have real usage data showing where cost or quality is actually the bottleneck.
Related resources
GPT-4o vs Claude Sonnet 4 — Detailed side-by-side comparison of the two Tier 2 options.
Model Fallbacks — Combine tiered model selection with automatic failover.
Prepaid vs Metered Billing — Why prepaid credits make multi-tier, multi-model spend easier to track.