Model Lineup & Pricing (OpenAI)

This is the single reference for OpenAI model IDs, prices and limits used across the OpenAI tracks. Everything here reflects the published API line-up as of 15 September 2026; prices are USD per million tokens (MTok) and are rollout-sensitive, so treat the table as a snapshot to reason with, not a live rate card. Re-verify against the API pricing and API models pages before you commit an architecture or a budget.

API line-up (September 2026)

All latest models take text and image input, emit text output, are multilingual and support vision. They are served through the Responses API and the client SDKs.

Previous generation, still available: gpt-5.5 at $5 / $30 and gpt-5.5-pro at $30 / $180. Legacy models remain callable; new work should target the GPT-5.6 line or Astra.

Reasoning effort

GPT-5.6 models accept efforts from none through max; Astra runs low, medium, high, xhigh, max. The rule straight from the docs is to use the lowest reasoning effort that gets the result — higher effort spends more output tokens (and therefore more money and latency) on internal reasoning. There is no exact mapping from GPT-5.5 efforts to GPT-5.6, so re-tune rather than porting an effort setting across a generation.

Model positioning

Selection guidance, straight from the docs:

                 capability ▲
                            │  GPT-6 Astra      frontier, judgment, multi-tool
                            │  GPT-5.6 Sol      complex, open-ended, high-value
                            │  GPT-5.6 Terra    pragmatic all-rounder
                            │  GPT-5.6 Luna     high-volume, structured, cheap
                            └───────────────────────────────► $ / MTok rises
   choose the lowest tier that clears the task, then the lowest effort that clears it

ChatGPT-side model names

The consumer/business product exposes different labels for the same underlying family. Do not assume an API ID exists for a ChatGPT label or vice versa.

Legacy models remain available in ChatGPT. The exact model roster a user sees is plan-dependent — see the ChatGPT feature and plan reference.

Specialised model families

These are purpose-built lines, several of them gated to authorised or approved organisations. Never route general traffic to them.

Selecting a model — decision table

Worked cost calculations

Prices are per MTok. These illustrate order-of-magnitude differences; run your own numbers against live pricing before committing.

Example 1 — a single 50k-token RAG answer

A retrieval-augmented answer with 50,000 input tokens (retrieved context + prompt) and 1,000 output tokens, one call.

The input side dominates a RAG answer because retrieved context is large relative to the reply. Astra costs ~49× Luna for the same answer — so the model choice is a retrieval-quality decision, not a reasoning-power one, unless the synthesis is genuinely hard.

Example 2 — a 1M-token batch job

Classify or transform one million input tokens with 200k tokens of output, run offline.

For structured high-volume work, Luna on Batch is the default; escalating a subset to Terra only where a validator flags low quality is a cascade, not a blanket upgrade.

Example 3 — an agent session

A single agent session that consumes 300k input tokens (accumulated context, tool results, compaction) and produces 40k output tokens across many turns.

Agent sessions accumulate context, so compaction and context summarisation (see the Agents cheat sheet) are the real cost levers: a session that lets context grow unbounded on Astra is where budgets disappear. Prompt caching a stable system prompt across turns also helps.

How to re-verify

1. Open developers.openai.com/api/docs/models for the current IDs, aliases, context windows and cutoffs.
2. Open developers.openai.com/api/docs/pricing for live input/output rates and any Batch/cached-input discounts.
3. Check the Codex models page for the Codex-specific roster and retirement dates.
4. When a fact here disagrees with the docs, the docs win — this page is a study aid, not an authority.

Key facts to memorise

• Four current API models: Astra ($10/$50), Sol ($4/$20), Terra ($2/$12), Luna ($0.20/$1.20); all 1.05M context, 128K max output.
• gpt-5.6 aliases to gpt-5.6-sol; pin explicit IDs in production.
• Use the lowest tier and lowest effort that clears the task; there is no GPT-5.5 → 5.6 effort mapping.
• Terra is the pragmatic replacement for GPT-5.5 workloads; Luna is for high-volume structured tasks.
• Specialised families (cyber, Daybreak, Rosalind) are gated and off-limits for general traffic.