GPT 6: Models, Credit Pricing and API Code (2026)

GPT 6 is the current generation of OpenAI’s large language models, and unlike most “what’s next” searches, this one has a concrete answer: the models are out, they have names, and you can call them right now. A large language model, if the term is new to you, is a neural network trained on a very large amount of text that predicts and generates text in response to what you give it. The GPT 6 line consists of three models at different capability and cost points, plus a point release, and the main decision you face is not whether to use it but which one of them your workload actually needs.

This article covers what each model in the family does, what the release timeline looks like, what the models cost per million tokens in credits, and how to call them from code. There is a worked example you can paste into a terminal, a cost comparison table, and a section on the mistakes that make people overpay by a factor of ten.

What is GPT 6 and when was it released?

GPT 6 is a family of large language models developed by OpenAI, released over roughly a month in autumn 2026. GPT 6 Astra came first, on 4 September 2026, as the frontier model of the line: the strongest option on coding, computer use and long reasoning chains. On 23 September 2026 OpenAI followed with two smaller models, GPT 6 Sol and GPT 6 Luna, which bring the same generation of capability to everyday work at very different price points. A point release, GPT 6.1 Sol, arrived on 5 October 2026.

The naming trips people up, so it is worth being blunt about it. “GPT 6” is not one model. It is a series, the same way GPT-4 was a series by the end of its life. When someone says they are “using GPT 6”, the useful follow-up question is which one, because Astra and Luna differ in price by roughly a factor of ninety and in latency by a lot more than that. The Wikipedia entry on GPT-6 tracks the family and its release history if you want the full public record, and there is a separate page for GPT-6.1 covering the point release.

All of these are generative pre-trained transformers, which is what the GPT in the name stands for: a transformer network pre-trained on general text, then tuned to follow instructions. The architecture has not changed category since GPT-2. What changes between generations is scale, training data, post-training technique and the amount of inference-time reasoning the model is allowed to do before it answers.

What about the search term “GPT 6 release date”?

A lot of search traffic for GPT 6 is still phrased as a question about the future: when is it coming out, will there ever be one. Those queries are now historical. The dates above are the answer. The live question has moved on to which variant to use and what it costs, which is the rest of this article.

What does GPT 6 do?

Every model in the GPT 6 family is a chat model. You send a list of messages with roles (system, user, assistant) and you get back an assistant message. That simple shape covers a surprising amount of ground:

• Writing and reviewing code. This is where the generational jump is most visible. Astra handles multi-file changes and long debugging sessions where the model has to keep a mental model of a codebase across dozens of turns.
• Tool calling. You describe your functions in the tools parameter, the model decides when to call them, and your code executes them and feeds results back. This is the foundation of every agent product.
• Structured extraction. With response_format set to a JSON schema, you get machine-readable output from unstructured input: invoices, transcripts, support tickets.
• Reasoning with adjustable effort. The reasoning_effort parameter lets you trade latency and cost against accuracy on hard problems. Low effort for classification, high effort for a maths proof or a tricky refactor.
• Everyday text work. Summarising, rewriting, translating, classifying. Luna is built for this and it is very cheap.

What GPT 6 does not do is generate images, video or audio. Those are separate model families with separate endpoints. If you need them alongside your text calls, they live in the same model catalog and bill from the same balance, which is the point of having one key rather than five.

GPT 6 Astra, Sol and Luna compared

Here is the whole family side by side, with prices in credits per million tokens. One credit is $0.01 on any monthly plan, so you can read the credit numbers as cents per million tokens if that is easier.

Model	Released	Input / 1M	Output / 1M	Cached input / 1M	Best for
GPT 6 Astra	2026-09-04	560 credits	2,800 credits	56 credits	Hard reasoning, long agent runs, complex code
GPT 6 Sol	2026-09-23	120 credits	600 credits	12 credits	General production work, good default
GPT 6.1 Sol	2026-10-05	120 credits	600 credits	6 credits	Same as Sol, cheaper cache reads
GPT 6 Luna	2026-09-23	6 credits	30 credits	0.6 credits	High-volume classification, extraction, routing

Three things in that table are worth sitting with.

First, the output-to-input ratio is five to one across the entire family. Output tokens always cost five times what input tokens cost. That means the cheapest reliable optimisation is almost never “send less context”, it is “ask for shorter answers”. A prompt that says “answer in at most two sentences” can halve a bill that no amount of context trimming would touch.

Second, cached input costs a tenth of fresh input on Astra, Sol and Luna, and GPT 6.1 Sol drops it to a twentieth. Caching applies when you send the same prefix repeatedly, which is exactly what happens with a long system prompt or a fixed document you keep asking questions about. If your application has a 20,000-token system prompt and you make a thousand calls a day, caching is the difference between a real bill and a rounding error.

Third, the spread between Luna and Astra is enormous: 6 credits versus 560 credits per million input tokens. Nothing about your architecture has to change to move between them. It is one string.

How GPT 6 compares to the Anthropic models

You are rarely choosing GPT 6 in a vacuum, so for context, Anthropic’s current line sits in the same catalog at comparable tiers: Claude HaiKu 5.5 at 12 credits in and 60 out per million, Claude Sonnet 5.5 at 160 and 800, and Claude Opus 5.5 at 320 and 1,600. Luna is the cheapest small model of the group; Astra is the most expensive large one. In practice teams run evaluations across both families and pick per task rather than per vendor, which is much easier when the request shape and the bill are shared.

How to call GPT 6 from the API

The endpoint is OpenAI-compatible, so if you have written against the chat completions API before, this is the same code with a different base URL. Create a key at your keys page first and export it as UNICORN_API_KEY.

Start with curl to prove the connection works:

curl https://api.unicornapi.net/v1/chat/completions \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-luna",
    "messages": [
      {"role": "user", "content": "Write one sentence about unicorns."}
    ],
    "max_tokens": 200
  }'

The response is the standard chat completions object: the text is at choices[0].message.content and the token counts are under usage. Those usage numbers are what you are billed on, so log them from day one.

In Python, with the official OpenAI client pointed at the Unicorn base URL:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["UNICORN_API_KEY"],
    base_url="https://api.unicornapi.net/v1",
)

resp = client.chat.completions.create(
    model="gpt-6-sol",
    messages=[
        {"role": "system", "content": "You are a terse technical editor."},
        {"role": "user", "content": "Rewrite this headline to be under 60 characters: "
                                    "'An Introduction to the New Generation of OpenAI Language Models'"},
    ],
    max_tokens=200,
    temperature=0.3,
)

print(resp.choices[0].message.content)
print(resp.usage.prompt_tokens, resp.usage.completion_tokens)

And in JavaScript with plain fetch, no SDK required:

const res = await fetch("https://api.unicornapi.net/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.UNICORN_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-6-astra",
    messages: [
      { role: "user", content: "List three edge cases in parsing ISO 8601 durations." },
    ],
    max_tokens: 600,
    reasoning_effort: "high",
  }),
});

const data = await res.json();
console.log(data.choices[0].message.content);
console.log(data.usage);

Every model in the GPT 6 family accepts the same fields: model, messages, tools, tool_choice, stream, max_tokens, temperature, top_p, stop, response_format and reasoning_effort. Switching models is a one-line change, which is the behaviour you want when a new release lands. Full parameter reference is in the API documentation, and if you would rather try prompts before writing any code, the browser playground runs the same models against the same balance.

A worked example: routing between GPT 6 models

Here is the pattern that saves the most money in real systems. Most workloads are a mix: lots of easy requests and a few hard ones. Sending all of them to Astra is like taking a taxi to the end of the driveway. Instead, let a cheap model triage, and escalate only when it needs to.

import os, json
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["UNICORN_API_KEY"],
    base_url="https://api.unicornapi.net/v1",
)

TRIAGE = """Classify the user's request. Reply with JSON only:
{"difficulty": "simple" | "hard"}
"simple" means it can be answered from the text alone.
"hard" means it needs multi-step reasoning, maths, or code design."""

def answer(question: str) -> str:
    triage = client.chat.completions.create(
        model="gpt-6-luna",
        messages=[
            {"role": "system", "content": TRIAGE},
            {"role": "user", "content": question},
        ],
        max_tokens=20,
        temperature=0,
        response_format={"type": "json_object"},
    )
    difficulty = json.loads(triage.choices[0].message.content)["difficulty"]
    model = "gpt-6-astra" if difficulty == "hard" else "gpt-6-luna"

    final = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": question}],
        max_tokens=800,
    )
    return f"[{model}] {final.choices[0].message.content}"

print(answer("What is the capital of Portugal?"))
print(answer("Design a retry policy for a queue with poison messages."))

The triage call costs almost nothing. At 6 credits per million input tokens, a 100-token classification is 0.0006 credits, well under a hundredth of a cent. If that router sends 80 percent of traffic to Luna instead of Astra, the bill on that 80 percent drops by roughly ninety-nine percent, and the hard questions still get the strong model.

Two refinements worth adding once this works. Put your long system prompt in a stable prefix so cache pricing kicks in. And log the model chosen alongside a quality score, so you can see whether the router is being too eager to call things simple.

What does GPT 6 cost in practice?

Abstract per-million prices are hard to reason about, so here are three concrete shapes of work. Credits are $0.01 each on a monthly plan, so divide credits by 100 for dollars.

• A support classifier. 50,000 tickets a month, 400 input tokens and 30 output tokens each. That is 20M input and 1.5M output. On Luna: 120 + 45 = 165 credits, about $1.65. On Astra the same job would be 11,200 + 4,200 = 15,400 credits.
• A documentation assistant. 5,000 questions a month, an 8,000-token cached system prompt plus 200 fresh tokens in, 500 out. On Sol with caching: 40M cached input at 12 credits/M is 480 credits, 1M fresh input is 120, and 2.5M output is 1,500. Total about 2,100 credits, or $21.
• A coding agent. 200 sessions a month, 150,000 input and 25,000 output tokens per session. On Astra: 30M input at 560 is 16,800 credits and 5M output at 2,800 is 14,000. Around 30,800 credits, or $308, before caching. This is the workload where cache hits matter most.

Those are estimates from the published rates, not measurements of your traffic. Before you ship anything, call POST /v1/models/price with the request you intend to send and get the exact price back. That endpoint exists precisely so you never have to guess, and it is the one habit that separates teams who are surprised by their bill from teams who are not.

On plan structure: monthly plans buy credits at $0.01 each, from Starter at $25 through Creator at $50, Studio at $100 and Pro at $200. Top-up credits cost $0.015 each and never expire, which makes them useful for spiky workloads that occasionally run past the monthly allocation. The full breakdown is on the pricing page.

Mistakes people make with GPT 6

These come up repeatedly, and all of them are cheap to avoid.

1. Defaulting to the biggest model. Astra is excellent and most teams do not need it for most calls. Start at Luna, build an evaluation set of 50 real examples, and only move up when you can point at failures. The reverse order costs ninety times more and teaches you nothing.
2. Ignoring the cache. If your system prompt changes on every request (a timestamp, a shuffled example order, a user ID injected at the top) you get zero cache hits. Keep the variable parts at the end of the message list, not the start.
3. Leaving max_tokens unset. Output is five times the price of input. An unbounded generation on Astra that rambles for 4,000 tokens when you needed 200 is a bill you did not have to pay. Set a ceiling that matches the shape of the answer you want.
4. Turning reasoning_effort up everywhere. High effort generates more internal reasoning tokens, which cost money and add latency. It earns its keep on genuinely hard problems and wastes money on “summarise this paragraph”.
5. Vague prompts, then blaming the model. Most quality complaints about any frontier model trace back to an underspecified prompt. State the format, the length, the audience and the constraints. The prompt engineering overview is a reasonable grounding if you have never done this deliberately.
6. Hardcoding the model string everywhere. Put it in config. GPT 6.1 Sol arrived about a month after Astra, and the next point release will arrive too. You want that to be a deploy, not a refactor.

Should you use GPT 6 or wait for what comes next?

There is a reflex in this field of waiting for the next release. It is almost always the wrong call, because the cost of switching is a string in a config file. The code you write against GPT 6 Sol today runs against whatever replaces it with no structural change, and you get the value of the working system in the meantime.

What is worth building deliberately is the surrounding scaffolding: an evaluation set from your real traffic, logging of token usage per request, a model name you can change in one place, and a router that sends easy work to a cheap model. Teams with those four things in place treat every new release as an experiment that takes an afternoon. Teams without them treat it as a migration. If you drive models from a coding agent rather than your own service, the same key works through the MCP integration from Cursor, Claude Code and similar tools.

The broader context, if you want it: GPT 6 is one entry in a field that now includes dozens of serious options, and the public list of large language models gives a sense of the pace. Background on how these models work is useful if you are making architecture decisions rather than just calling an endpoint. For a look at the previous generation and how pricing moved between them, we covered GPT 5.6 Sol pricing and API access when it shipped.

Getting started with GPT 6 in ten minutes

A short plan that works:

1. Create a key at /keys and export it as UNICORN_API_KEY.
2. Run the curl example above against gpt-6-luna. Confirm you get a sentence back and that usage is populated.
3. Take ten real inputs from your product and run them through Luna. Score the outputs yourself.
4. Re-run the failures through gpt-6-sol, then gpt-6-astra. Note which tier is the first to pass.
5. Price the winning configuration with POST /v1/models/price at your expected volume.
6. Ship with the model name in config, usage logging on, and max_tokens set.

That sequence takes an afternoon at most and it answers the question that the GPT 6 release coverage cannot answer for you: not which model is best in general, but which one is good enough for your specific job at the lowest price. Browse the full catalog if you want to put Claude or a media model alongside it on the same key.