Speed
Output tokens / second
GPT-6 Sol overview
GPT-6 Sol specs
- Model ID
openai/gpt-6-sol- Developer
- OpenAI
- Context window
- 1,050,000 tokens
- Max output
- 128,000 tokens
- Input
- Text, Image
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
What Is GPT-6 Sol?
GPT-6 Sol is the middle tier of OpenAI's GPT-6 family: below the flagship GPT-6 Astra and above the fast, cheap GPT-6 Luna. It is aimed at complex coding and agentic workflows, the work where a model has to plan, call tools, read results and keep going, rather than answer once and stop.
All three share the same 1,050,000-token context window. What separates them is depth and price: Sol costs a fifth of Astra and roughly twenty times Luna, which makes the choice between them a question about how hard the task actually is.
Technical Reference
- Release date: 22 September 2026
- Endpoint: POST /v1/chat/completions
- Context window: 1,050,000 tokens
- Modalities: Image, Text → Text
- Reasoning effort: none to max, medium by default
- Capabilities: function calling, parallel tool calls, structured outputs, vision, web search, prompt caching
Reasoning Effort Is A Dial, Not A Switch
Sol runs from none to max, defaulting to medium. That control matters more than it sounds: reasoning tokens bill at the output rate, so effort is the main lever on both latency and cost. Routine edits, classification and formatting rarely need more than low; architecture decisions, tricky debugging and multi-file refactors are where high or max pays for itself. Leaving everything at max is the most common way to overspend on a model like this.
The 1.05M Context Window
1,050,000 tokens is on the order of 3,000 pages, or a substantial codebase with its history and documentation. For agents, the practical win is that a long transcript does not need to be summarised away every few steps, the point where long runs usually start contradicting themselves. For code work, it means the model can hold the actual call sites rather than a retrieved guess at them.
Output is capped at 128,000 tokens, which is enough for a large diff or a long structured document in a single reply.
Where GPT-6 Sol Makes The Most Sense
Complex coding. Multi-file changes, migrations and debugging where the model needs both the code and the surrounding context to be right rather than plausible.
Agentic pipelines. Planning, tool calls, parallel tool calls and structured outputs, with web search available when the answer is not in the prompt.
Vision-adjacent work. Screenshots, diagrams and design mockups alongside the prompt, UI work that would otherwise have to be described in words first.
Long-context analysis. Research, due diligence and audits across document sets that would need a retrieval layer elsewhere.
Use cases
Complex coding. Multi-file refactors, migrations and debugging where the model needs the real call sites, not a retrieved approximation. The 1,050,000-token window holds the code and its history; the 128,000-token output ceiling leaves room for a large diff in one reply.
Agentic workflows. Planning loops with function calling, parallel tool calls and structured outputs, plus web search when the answer is not in the prompt. Prompt caching at a tenth of input price is what keeps a twenty-step loop affordable.
Multimodal engineering work. Screenshots, diagrams and mockups in the same request as the prompt, UI fixes and design-to-code work without describing the picture first.
Long-context research. Audits, due diligence and literature review across document sets large enough that a retrieval pipeline would otherwise be mandatory.
Capabilities
- Reasoning
- Function calling
- Vision
- Structured outputs
- Streaming
GPT-6 Sol benchmarks by 
Artificial Analysis
See how GPT-6 Sol compares in generation speed. Data retrieved Nov 3, 2026.
Benchmark results
Compare 5 models available on AI/ML API across 4 independent benchmarks.
Humanity's Last Exam
Expert-written questions across academic subjects.
Terminal-Bench 4.0
Agentic tasks completed in a command-line environment.
AA-LCR
Reasoning across long documents of about 100k tokens.
SciCode
Python coding tasks drawn from scientific research.
GPT-6 Sol API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $2 | 1M tokens |
| Output | $10 | 1M tokens |
| Cached input | $0.2 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
GPT-6 Sol vs other models: token prices
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
 GPT-6 Sol | $2 | $10 |
| GPT-6 Luna | $0.1 | $0.5 |
| GPT-6.1 Terra | $1 | $5 |
| GPT-6.1 Sol | $2 | $10 |
| GPT-6.1 Terra Pro | $2.12 | $10.58 |
How to use the GPT-6 Sol API
View docs
Build with your agent
Give your agent the model details, code and documentation.
Paste it into your coding agent to build the integration.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "openai/gpt-6-sol",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "openai/gpt-6-sol",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "openai/gpt-6-sol",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to GPT-6 Sol
GPT-6 Sol FAQ
Is GPT-6 Sol available on AI/ML API?
Yes. GPT-6 Sol is available on AI/ML API through the OpenAI-compatible chat completions endpoint under the model ID openai/gpt-6-sol.
How much does GPT-6 Sol cost?
GPT-6 Sol costs $2 input and $10 output per 1M tokens on AI/ML API, billed as you go with no subscription.
How do I call GPT-6 Sol?
Send a request to https://api.aimlapi.com/v1/chat/completions with openai/gpt-6-sol as the model id. It is the same OpenAI-compatible endpoint every other GPT model on AI/ML API uses.
What is the GPT-6 Sol model ID?
Use openai/gpt-6-sol as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.
What is the context window of GPT-6 Sol?
GPT-6 Sol accepts up to 1,050,000 tokens of context and returns up to 128,000 output tokens.