GPT-5.6 Sol

GPT-5.6 Sol (openai/gpt-5.6-sol) is the flagship of OpenAI’s GPT-5.6 series: complex reasoning, coding and agentic workflows, especially strong at command-line and multi-step coding. It takes text, images and files, with a 1M-token context. Requests go through the usual POST /v1/chat/completions in OpenAI format.

Parameters and capabilities

Capability	Value
model	openai/gpt-5.6-sol (or the short gpt-5.6-sol)
Context	1M tokens
Image input	Yes — image_url with a data: URL in the last message
File input	Yes — PDF, DOCX, TXT, CSV, XLSX, PPTX, up to 5 per request
Reasoning	reasoning_effort: low, medium, high, xhigh, max; disabled with off
Cache read	21.40 ₽ ($0.24) per 1M tokens
Cache write	267.40 ₽ ($3) per 1M tokens

Reasoning is billed as output tokens and eats into the max_tokens budget — leave headroom for long answers. The cache lives 5 minutes with sliding renewal, and repeated context is billed at the read price — see Context caching.

Examples

A request with a reasoning depth

Python

from openai import OpenAI

client = OpenAI(base_url="https://api.mixen.ai/v1", api_key="mxn-...")

resp = client.chat.completions.create(
    model="openai/gpt-5.6-sol",
    messages=[{"role": "user", "content": "Find the race condition in this code and propose a fix"}],
    reasoning_effort="high",
)
print(resp.choices[0].message.content)

cURL

curl https://api.mixen.ai/v1/chat/completions \
  -H "Authorization: Bearer $MIXEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-5.6-sol",
    "messages": [{"role": "user", "content": "Find the race condition in this code and propose a fix"}],
    "reasoning_effort": "high"
  }'

extra_body sends the parameter into the request body as is — the example works on any SDK version.

Streaming with usage

stream = client.chat.completions.create(
    model="openai/gpt-5.6-sol",
    messages=[{"role": "user", "content": "Design a database schema for a subscription service, explain the choices"}],
    reasoning_effort="high",
    stream=True,
    stream_options={"include_usage": True},
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

With include_usage a chunk carrying usage arrives right before [DONE] — handy for logging spend.

Price

Per 1M tokens: input — 213.90 ₽ ($2.40), output — 1069.50 ₽ ($12). Context cache: read — 21.40 ₽ ($0.24), write — 267.40 ₽ ($3). Catalog prices track the exchange rate — take current values from the catalog.

Strengths and limits

• Complex reasoning and multi-step code are the model’s core profile; the command line is its declared strong suit.
• Agentic workflows: tool descriptions go in tools, calls come back in tool_calls, as with OpenAI.
• Images and files in the same /v1/chat/completions endpoint; each file contributes up to 150,000 characters, no OCR.
• 1M-token context — long repositories and documentation fit whole.
• Reasoning depth can be dialed down to low or switched off with off when step-by-step deliberation is not needed.
• Flagship pricing: 1069.50 ₽ per 1M output tokens. For high-volume routine the smaller GPT-5.6 Luna is cheaper — 21.40 ₽ input / 128.30 ₽ output.

All models — in the catalog. The general text workflow — chat guide.