AIREITER
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GPT-6 Sol

Run GPT-6 Sol at 30% of OpenAI's official rates: $0.60 per 1M input and $3.00 per 1M output. Compare it against GPT-6 Luna, GPT-5.6 Sol, Opus 5.5, and Gemini 3.8 Flash, then call it with copy-paste curl, Python, and Node.

Input tokensInputOutputCache readCache creation
≤ 271,999$0.60 per 1M tokens$3.00 per 1M tokens$0.06 per 1M tokens$0.75 per 1M tokens
> 271,999$1.20 per 1M tokens$4.50 per 1M tokens$0.12 per 1M tokens$1.50 per 1M tokens

Rates are per million tokens. The tier applies to the entire request based on total input tokens, including cached reads and writes.

Run with API

INPUT

OUTPUT

Example
Generated in
42.7 seconds
Input tokens
134
Output tokens
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Model details

Use the same model key in Playground, API requests, and internal workflows.

Model ID
gpt-6-sol
Provider
OpenAI
Protocol
OpenAI Chat Completions
Context window
1,050,000 tokens
Max output
128,000 tokens

GPT-6 Sol vs Luna, GPT-5.6, and the competition

GPT-6 Sol is the flagship tier of the GPT-6 family that OpenAI shipped on 22 September 2026. Against GPT-5.6 Sol it is exactly half the price per token in both directions at the same flagship capability tier, which is the single biggest reason to migrate. Luna is the cheap tier of the same generation and handles most routine traffic at a twentieth of Sol's input cost.

List prices below are per 1M tokens. The AIReiter column is what you actually pay here, which is 30% of the official rate.

ModelOfficial inputOfficial outputCached inputAIReiter inputAIReiter output
GPT-6 Sol$2.00$10.00$0.20$0.60$3.00
GPT-6 Luna$0.10$0.50$0.01--
GPT-5.6 Sol$4.00$20.00$0.40$1.20$6.00
Claude Opus 5.5$4.00$20.00---
Gemini 3.8 Flash$0.75$3.75---

Official list prices as published by each vendor in September 2026. Gemini 3.8 Flash is on introductory pricing through 31 December 2026 and rises to $1.50 / $7.50 on 1 January 2027. Anthropic cut Opus 5.5 to $4 / $20 from $5 / $25, which still leaves it at twice the list price of GPT-6 Sol.

One caveat worth knowing before you migrate: cheaper does not mean uniformly stronger. Independent comparisons published at launch found GPT-5.6 Sol still scoring higher than GPT-6 Sol on some coding and computer-use benchmarks. Run your own evaluations on your own traffic before you switch a production route.

Call GPT-6 Sol from your code

The endpoint is OpenAI-compatible, so any client that already speaks the Chat Completions protocol works by changing two lines: the base URL and the API key. The model ID is gpt-6-sol.

curl

curl https://aireiter.com/api/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AIREITER_API_KEY" \
  -d '{
    "model": "gpt-6-sol",
    "messages": [{"role": "user", "content": "Explain how a 429 response should be retried."}],
    "reasoning_effort": "medium",
    "stream": true
  }'

Python (openai SDK)

from openai import OpenAI

client = OpenAI(
    base_url="https://aireiter.com/api/v1",
    api_key="YOUR_AIREITER_API_KEY",
)

stream = client.chat.completions.create(
    model="gpt-6-sol",
    messages=[{"role": "user", "content": "Explain how a 429 response should be retried."}],
    reasoning_effort="medium",
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="")

Node (openai SDK)

import OpenAI from "openai"

const client = new OpenAI({
  baseURL: "https://aireiter.com/api/v1",
  apiKey: process.env.AIREITER_API_KEY,
})

const stream = await client.chat.completions.create({
  model: "gpt-6-sol",
  messages: [{ role: "user", content: "Explain how a 429 response should be retried." }],
  reasoning_effort: "medium",
  stream: true,
})

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "")
}

Agent CLIs use the same credentials. Codex CLI and other OpenAI-compatible clients point at https://aireiter.com/api/v1, and Claude Code points at https://aireiter.com/api. Grab a key on the API keys page and see the LLM API integration guide for per-client setup.

What GPT-6 Sol actually costs you

Per-token rates are hard to reason about, so here is the arithmetic on three realistic workloads at AIReiter's rate of $0.60 input and $3.00 output per 1M tokens.

WorkloadPer requestCost per 1,000 requests
Support reply2K in / 500 out$2.70
Code review on a diff20K in / 2K out$18.00
Agent step with tool results50K in / 4K out$42.00

Two levers move these numbers more than anything else:

  • Cached input reads cost $0.06 per 1M, a tenth of a fresh read. A stable system prompt and a stable context prefix are the cheapest optimization available. OpenAI raised default cache hit rates for this generation and lets you set an explicit breakpoint for where the cached prefix ends, and you can change reasoning effort or toggle tools without losing the cached context.
  • Requests above 272K input tokens are surcharged, at 2x on input and 1.5x on output, following OpenAI's own tiering. Crossing that line roughly doubles your input bill, so trimming a 300K-token context back under the threshold is usually worth more than any prompt tuning.

Output tokens cost 5x what input tokens cost. If responses are running long, capping max completion tokens or lowering verbosity moves the bill more than shortening the prompt does.

When to use GPT-6 Sol, and when not to

Sol is the escalation tier. Routing everything to it is the most common and most expensive mistake.
01

Use it: multi-file debugging

Bugs whose cause sits in the interaction between modules rather than in any single function, where a cheaper model keeps fixing the symptom.

02

Use it: long-horizon agents

Plans that must survive a dozen tool calls, partial failures, and revisions without losing the thread. The 1M-token window holds the whole trajectory.

03

Use it: decisions with tradeoffs

Architecture and migration calls where the useful output is an honest comparison, not a confident recommendation.

04

Do not use it: routine traffic

Classification, extraction, summarization, and first-line support replies. GPT-6 Luna is a twentieth of the input cost and finishes these reliably. Route to Sol only after a cheaper model measurably fails.

Try GPT-6 Sol in three steps

No install and no setup. The playground above runs against the same endpoint your code will call.

01

Set reasoning effort

Start at medium. Raise it for problems that need the model to plan before answering, lower it when latency matters more than depth.

02

Send a prompt

Paste your real task rather than a toy one. Token usage and credits consumed are reported under every response, so you can price the workload before committing.

03

Copy the API call

Move the same request into your code with model ID gpt-6-sol against the OpenAI-compatible endpoint. Nothing else in your client changes.

GPT-6 Sol FAQ

Pricing, capability, and migration questions.

/ 01

How much does GPT-6 Sol cost on AIReiter?

$0.60 per 1M input tokens and $3.00 per 1M output tokens, which is 30% of OpenAI's official $2.00 and $10.00. Cached input reads are $0.06 per 1M against the official $0.20.

/ 02

Is GPT-6 Sol better than GPT-5.6 Sol?

It is half the price at the same flagship tier, which is the clear win. On capability the picture is mixed: comparisons published at launch found GPT-5.6 Sol still ahead on some coding and computer-use benchmarks. Evaluate on your own traffic before switching a production route.

/ 03

Should I use Sol or Luna?

Luna for anything routine, at $0.10 per 1M input against Sol's $2.00. Sol for hard code, long reasoning chains, and agents that have to recover from failed steps. A routed stack that escalates to Sol only on failure costs a fraction of sending everything to Sol.

/ 04

What is the context window?

Roughly 1M input tokens with up to 128K output tokens. Requests above 272K input tokens carry OpenAI's surcharge of 2x on input and 1.5x on output, so staying under that threshold materially changes the bill.

/ 05

Does it support prompt caching?

Yes, and it is the largest cost lever available. Cached input reads carry a 90% discount, and this generation lets you set an explicit breakpoint for where the cached prefix ends and change reasoning effort or toggle tools without invalidating the cache.

/ 06

How do I call it from Claude Code or Codex CLI?

Both work unchanged with an AIReiter key. Codex CLI and other OpenAI-compatible clients use https://aireiter.com/api/v1, Claude Code uses https://aireiter.com/api. Model ID is gpt-6-sol.

/ 07

When was GPT-6 Sol released?

OpenAI released GPT-6 Sol and GPT-6 Luna on 22 September 2026, 19 days after GPT-6 Astra, alongside a roughly 50% cut to per-token API prices across the family.