GPT-5.6 Sol is the right call when your agent lives in a terminal: running commands, reading what comes back and patching code across several files. OpenAI shipped it in July 2026 at the head of the GPT-5.6 line. That release also brought in the naming OpenAI still uses: Sol for the strongest tier, Terra for the balanced one, Luna for cheap and quick work.
According to OpenAI, Sol handles hard reasoning, code and agent loops well, and shell tasks plus coding jobs with many steps are where it shines most.
What you can use it for
- Coding agents and scripted automation running in a shell
- Bug hunts that take several rounds and touch more than one file
- Hard reasoning problems that older GPT releases could not solve
- Apps whose prompts and evals were written for GPT-5.6 Sol
Settings and limits
- Context window: 1,050,000 tokens; output up to 128K tokens.
- The rate is fixed per model and shown before you send; there is no monthly fee.
- Cursor, Cline and similar editors connect through the OpenAI-style endpoint: base URL
https://kadraris.com/v1, ansk-kd-key andgpt-5.6-solas the model. - Give long-running agents their own key with a spending cap.
Example request
from openai import OpenAI
client = OpenAI(
base_url="https://kadraris.com/v1",
api_key="sk-kd-...",
)
resp = client.chat.completions.create(
model="gpt-5.6-sol",
messages=[{"role": "user",
"content": "Write a bash script that rotates "
"logs older than 7 days."}],
)
print(resp.choices[0].message.content)Swap in your own task and tell it which shell and OS the script must run on.
Questions
What will GPT-5.6 Sol cost me?
$4.32 / $21.60 per 1M tokens on Kadraris. A request that fails is not charged.
Should a new project start on GPT-6 Sol instead?
Usually yes, since GPT-6 Sol is the next generation of this tier. Where production prompts were tuned on 5.6 Sol, keep it running and move only after a side-by-side run on real inputs.
Where do I find editor setup steps?
Each tool's settings are listed in the Claude Code guide.
GPT-5.6 Sol in your code
from openai import OpenAI
client = OpenAI(base_url="https://kadraris.com/v1", api_key="sk-kd-...")
resp = client.chat.completions.create(
model="gpt-5.6-sol",
messages=[{"role": "user", "content": "Suggest three opening shots for a short film about a cafe."}],
)
print(resp.choices[0].message.content)curl https://kadraris.com/v1/chat/completions \
-H "Authorization: Bearer $KADRARIS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "gpt-5.6-sol", "messages": [{"role": "user", "content": "Suggest three opening shots for a short film about a cafe."}]}'import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://kadraris.com/v1", apiKey: process.env.KADRARIS_API_KEY });
const resp = await client.chat.completions.create({
model: "gpt-5.6-sol",
messages: [{ role: "user", content: "Suggest three opening shots for a short film about a cafe." }],
});
console.log(resp.choices[0].message.content);
























