GPT-5.6 vs GPT-6: Routing Data-Heavy Prompts by Tier

For data-heavy prompts, use GPT-5.6 Luna ($0.20 / $1.20 per 1M tokens) for lookups and extraction, Terra ($2 / $12) as the default for mixed analysis, and Sol ($4 / $20) only for multi-step reasoning. Prices are as of October 2026. Before you commit, compare OpenAI's newer GPT-6 tiers, which cost less.

What are GPT-5.6 Sol, Terra and Luna?

OpenAI released GPT-5.6 publicly on July 9, 2026 as three tiers instead of one model. Engadget described Sol as OpenAI's most powerful model at the time, Terra as the everyday tier matching GPT-5.5 at half the cost, and Luna as the lowest-cost option. The launch prices in that article are out of date, so use the official numbers below.

These are standard prices per 1M tokens from OpenAI's pricing page, prices as of October 2026:

Sol's price is marked as promotional, available "at least through November 21, 2026". Luna is 20 times cheaper than Sol on input and about 17 times cheaper on output. That gap is why routing pays off.

Is GPT-5.6 still OpenAI's current lineup?

No. In September 2026 OpenAI shipped GPT-6 Astra and then GPT-6 Sol and GPT-6 Luna on September 22, with API prices 50% lower than GPT-5.6. The models page now recommends three flagship models:

GPT-5.6 is still priced and callable, so existing integrations do not break. For a new project, test the GPT-6 equivalents first, since they cost less per token. The routing approach in the rest of this post works for both generations: a cheap tier for simple reads, a middle tier as the default, and the top tier only when needed.

Which tier fits which data task?

The official TOON benchmark breaks accuracy down by question type, and the pattern is useful for routing. Field retrieval is easy (97.8% for TOON, 99.2% for JSON). Aggregation (48.4% for both) and filtering (38.0% TOON, 41.1% JSON) are hard regardless of format. Hard tasks need either a stronger model or less work for the model.

Small models need extra testing. In the benchmark, gpt-5.4-nano scored 57.0% with TOON and 57.4% with JSON, a statistical tie. A cheap tier is not automatically worse with compact formats, but it is also not automatically fine. Build a set of 50 to 100 real questions with known answers and run it on each tier.

How do you shrink payloads before routing?

Every tier bills per input token, so the same compression helps all three. Here are three log events as JSON:

{"events":[
  {"ts":"2026-09-30T08:00:00Z","service":"checkout","level":"error","latencyMs":1840},
  {"ts":"2026-09-30T08:00:05Z","service":"search","level":"warn","latencyMs":620},
  {"ts":"2026-09-30T08:00:09Z","service":"checkout","level":"info","latencyMs":95}
]}

And as TOON, encoded with @toon-format/toon 4.1.1:

events[3]{ts,service,level,latencyMs}:
  "2026-09-30T08:00:00Z",checkout,error,1840
  "2026-09-30T08:00:05Z",search,warn,620
  "2026-09-30T08:00:09Z",checkout,info,95

The encoder quotes the timestamps because they contain colons. That is expected, and the data round-trips exactly. Across the official benchmark, TOON used 42.6% fewer tokens than JSON overall (32.7% on the mixed/nested track, 58.7% on flat tables), with 72.2% accuracy versus 71.4%, effectively a tie. On real prompts the saving depends on how much of the prompt is data. Our TOON vs minified JSON comparison shows where compact JSON comes close.

A simple router looks like this:

import OpenAI from "openai";
import { encode } from "@toon-format/toon";

const client = new OpenAI();

function pickModel(task: "lookup" | "analysis" | "reasoning") {
  if (task === "lookup") return "gpt-5.6-luna";
  if (task === "analysis") return "gpt-5.6-terra";
  return "gpt-5.6-sol";
}

const data = encode({ events }); // stable prefix: same data, same bytes

const response = await client.responses.create({
  model: pickModel("lookup"),
  instructions: "Event data follows in TOON: header lists fields once, one event per line.\n" + data,
  input: "Which service logged an error, and what was its latency?",
});

console.log(response.output_text);

What does each tier cost per 1,000 requests?

Assume each request carries 6,000 input tokens of JSON data and returns 400 output tokens. Per 1,000 requests that is 6M input tokens and 0.4M output tokens. The TOON column assumes about 40% fewer input tokens. These are rough estimates; your own token counts will differ, so check a real payload in the TOON token calculator.

Routing matters more than format: moving a lookup from Sol to Luna saves about 95%. Caching comes second when the same data repeats. TOON trims what is left and also shrinks the cached prefix. For offline jobs, the Batch API halves everything again. See OpenAI Batch API with TOON.

What format should GPT-5.6 return?

JSON. Keep TOON on the input side and use OpenAI's Structured Outputs with a JSON Schema when your code needs to parse the answer. Asking a model to write a new notation is a generation task, and JSON is the format models know best. Our TOON with OpenAI GPT-5 guide shows the full request pattern, and LLM API pricing for October 2026 compares these tiers with Claude and Gemini. To measure your own payload, use the free JSON to TOON converter.

Frequently Asked Questions