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Home › Guides › LLM Cost Optimization › GPT-4o, GPT-4o Mini, GPT-5.6 Luna and Gemini 3.6 Flash Costs
Model Selection · 8 min

GPT-4o, GPT-4o Mini, GPT-5.6 Luna and Gemini 3.6 Flash Costs

The cheapest model for your app depends on the input, output, and traffic patterns of each feature—not a generic benchmark score.

September 11, 20268 min readBy LLMtrack

This is part of our guide to managing AI API spend.

Quick Answer: GPT-4o remains the higher-priced option in this comparison. GPT-4o Mini and GPT-5.6 Luna are budget OpenAI tiers, while Gemini 3.6 Flash is Google’s current Flash model. The pricing table shows how their input and output rates differ, but only your actual token mix and quality checks can identify the best value for your feature.

Why Benchmarks Don't Predict Your Cost

Benchmarks like MMLU and HumanEval are useful for model labs, but they rarely match your feature token distribution. A classifier with 700 input tokens and a 40-token JSON answer has a completely different cost profile than a chat feature with long outputs. Your cheapest reliable model depends on the shape of your requests.

<1scost visibility per request
Featureattribution by product surface
Real datanot benchmark averages

Model Cost Calculator

Compare current rates using the same request shape. Prices are USD per million tokens.

Monthly Volume Comparison

Switch traffic volume to see how costs scale for a 1,000-input-token, 250-output-token request.

Comparing Models on Your Own Traffic

LLMtrack records the model, feature, token counts, latency, status, and computed cost after every LLM response. That turns optimization from a guessing exercise into a ranked list of actions based on your own production traffic.

Warning: Don't switch blind. Run changes on a sample of real requests before moving production traffic.
Tip: Check p95 token lengths and feature-level cost share before deciding where to optimize first.
import { LLMtrack } from 'llmtrack';

const llmtrack = new LLMtrack({ apiKey: process.env.LLMTRACK_API_KEY });

// after your LLM call — fire-and-forget, never throws, never blocks
llmtrack.track({
  provider: 'openai',
  model: response.model,
  feature: 'chat-completion',
  promptTokens: response.usage?.prompt_tokens,
  completionTokens: response.usage?.completion_tokens,
  latencyMs: Date.now() - startedAt,
  status: 'success',
});

Prefer raw HTTP? See the full API reference.

You cannot optimize what you cannot see.

Measure one feature today and compare the real cost across models, users, and workflows.

See which switch saves you the most →

FAQ

A model swap can change JSON formatting, tool-call behavior, context limits, safety refusals, and latency even when the prompt stays the same. Add schema validation and fallback handling, replay representative requests in staging, and use a reversible rollout before sending all traffic to the new model.

Assign a stable percentage of eligible requests to each model, keep prompts and decoding settings consistent, and log model, feature, token counts, latency, and a quality outcome. Exclude sensitive or high-risk flows at first, monitor errors, and keep a kill switch that sends traffic back to the control model.

There is no universal request count. Sample until the cost per successful outcome and quality/error rates are stable across your important traffic segments; low-variance classification may settle after hundreds of requests, while diverse chat traffic can require thousands. Include enough tokens to cover short, typical, and p95-length requests rather than trusting one aggregate average.

See your model savings — using your real token data

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