GPT-5.6 Luna Pricing vs GPT-4o Mini: Real Cost per Request

GPT-5.6 Luna vs GPT-4o Mini

Luna costs a third more than GPT-4o Mini on input and twice as much on output. So the gap grows with every token a feature writes. Here is what that looks like on three common request shapes at 100,000 requests a month.

On price alone Mini wins every row. Luna only pays for itself if it does the job in fewer attempts: for the chat reply above, Mini would need about 1.7 tries per good answer before the two cost the same.

If you move a feature from Mini to Luna, expect that feature's cost to rise by roughly 1.5x to 2x depending on how much it writes. Measure it per feature before and after the switch, because a blended monthly total will hide it.

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.

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.

Copyimport { 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.

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