Drafted with AI tools and edited by a human. The figures and conclusions were checked by a GPUniq editor.
TL;DR
GPT-6.1 Sol costs $2.00 per 1M input tokens and $10.00 per 1M output tokens for standard requests. Payloads over 272K tokens cost $4.00 per 1M input tokens and $20.00 per 1M output tokens. The model features a 1,050,000-token context window, 128,000 max output tokens, and native support for text and vision. It achieves a 44.8% success rate on long Chain-of-Thought controllability tests. Custom fine-tuning is unsupported. Fast mode is unavailable under EU data residency, while Ultrafast supports only US or global endpoints.
What Does the API Cost?
Pricing for GPT-6.1 Sol depends on your request context length. OpenAI charges standard rates for contexts up to 272K tokens. Crossing that 272K threshold doubles both input and output rates.
| Context Tier | Input Rate ($/1M tokens) | Output Rate ($/1M tokens) |
|---|---|---|
| Standard (<=272K tokens) | $2.00 | $10.00 |
| Long-Context (>272K tokens) | $4.00 | $20.00 |
Compute costs scale as you increase the reasoning effort parameter. The API accepts five effort levels: low, medium, high, xhigh, and max.
Do not send "none" or "minimal" effort parameters from older endpoints. GPT-6.1 Sol rejects both choices. Set the minimum level to "low" instead.
{
"model": "gpt-6.1-sol",
"reasoning_effort": "high",
"messages": [
{"role": "user", "content": "Analyze the attached codebase for concurrency bugs."}
]
}
How Large Is the Context Window?
GPT-6.1 Sol offers a 1,050,000-token context window. Max output capacity sits at 128,000 tokens per call.
If your request reaches 273,000 tokens, OpenAI bills the entire payload at the $4.00/$20.00 long-context rate. Watch your context accumulation. Keep dynamic message histories under the 272K mark to avoid the higher billing tier.
How Much Better Is Its Reasoning?
OpenAI benchmark data indicates notable progress in tracking multi-step inference chains. For Chain-of-Thought (CoT) outputs between 750 and 1,250 tokens, GPT-6.1 Sol hits a 44.8% controllability success rate. That beats GPT-6 Sol at 23.2% and GPT-5.6 Sol at 16.1%.
| Model | CoT Controllability Success (750-1,250 tokens) | Safety Benchmarks (Challenging Prompts) |
|---|---|---|
| GPT-5.6 Sol | ~16.1% | - |
| GPT-6 Sol | ~23.2% | Baseline |
| GPT-6.1 Sol | ~44.8% | Higher in 5 of 8 categories |
The safety architecture shows measurable changes too. On challenging production prompt benchmarks, GPT-6.1 Sol scored higher than GPT-6 Sol in 5 of 8 core safety categories.
Configuring high reasoning parameters pushes overall output quality close to OpenAI's Astra model on complex coding and shell tasks.
What Modality and Fine-Tuning Limits Exist?
The model handles text inputs, text outputs, and image inputs. Native tool integrations include web search, file search, image generation, code interpreter, and hosted shell execution environments.
Audio and video inputs are explicitly unsupported. Keep video and audio pipelines on dedicated multimodal infrastructure.
Custom fine-tuning is also unsupported. Guide model outputs using prompt construction, strict system constraints, or the reasoning effort toggles.
import openai
client = openai.OpenAI()
# Fine-tuning is unsupported; use reasoning_effort and strict system prompts
response = client.chat.completions.create(
model="gpt-6.1-sol",
reasoning_effort="medium",
messages=[
{"role": "system", "content": "You are a specialized code auditor enforcing strict compliance rules."},
{"role": "user", "content": "Review this deployment manifest."}
]
)
Does It Support EU Data Residency?
Standard endpoints support EU data residency. OpenAI turns the model off by default in Enterprise and Edu workspaces until an administrator enables it.
Routing choices change residency guarantees. Fast mode is completely unavailable under EU data residency rules. Ultrafast mode routes exclusively through US or global endpoints, skipping EU-only storage guarantees.
Configure your API clients carefully to avoid breaking cross-border data constraints.
# Call the standard completion endpoint without Fast or Ultrafast flags
curl https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6.1-sol",
"reasoning_effort": "low",
"messages": [{"role": "user", "content": "Ping test"}]
}'
Is Upgrading from GPT-6 Sol Worth It?
Agents requiring complex reasoning or heavy tool usage gain clear accuracy improvements. Moving CoT controllability from 23.2% to 44.8% cuts down execution failures in step-by-step logic chains.
Standard workloads under 272K tokens run at $2.00 per 1M input tokens and $10.00 per 1M output tokens.
Watch out for payloads over 272K tokens. Rates jump to $4.00 input and $20.00 output per million tokens, doubling your costs if prompts run unmonitored.
Upgrading requires no new SDKs. Change your model string to gpt-6.1-sol, replace unsupported "none" or "minimal" reasoning flags with "low", and adjust your application limits.
Related on the GPUniq blog
Methodology
Facts are compiled from the vendor's own announcement and documentation, linked below, at the time of writing; where the vendor hasn't published a number it is marked as such. GPUniq prices, when the model is already available here, come from our live catalog.
Sources
- 1.GPT-6.1 Sol Model | OpenAI API — OpenAI (accessed )
- 2.Addendum to GPT-6 Astra System Card: GPT-6.1 Sol — OpenAI (accessed )
- 3.Compare models | OpenAI API — OpenAI (accessed )
- 4.Pricing | OpenAI API — OpenAI (accessed )
- 5.OpenAI launches GPT-6 Sol and Luna, boasting lower cost and fewer mistakes — TechCrunch (accessed )
- 6.The 5 biggest announcements from OpenAI's blockbuster AI conference — Axios (accessed )
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