Closed SourceOpenAIReleased 2026-09

GPT-6.1 Sol

Near-Astra intelligence for a fifth of the price

Context window

1,050,000

Input / 1M tokens

$2.00

Output / 1M tokens

$10.00

Provider

OpenAI

Data verified 2026-09-30

GPT-6.1 Sol is an upgrade to GPT-6 Sol that nearly matches GPT-6 Astra's intelligence on agentic coding, computer use, and professional work at one-fifth of Astra's standard input and output token prices. The model delivers significant improvements over GPT-6 Sol in complex professional tasks including code writing, debugging, document understanding, and multistep business workflows.

Capability index

Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.

Coding
92
Reasoning
85
Math
80
Multimodal
70
Long context
95
Speed
70
Cost efficiency
95

How to access it

Available in the OpenAI API (gpt-6.1-sol), ChatGPT Work, and Codex for Plus, Pro, Business, Enterprise, and Edu users. Access starts with a basic API tier.

Strengths

  • ✓Matches GPT-6 Astra on DeepSWE v1.1 coding tasks
  • ✓Cost-efficient alternative to GPT-6 Astra at 1/5 the price
  • ✓Improved factual accuracy with 32% reduction in error rate vs GPT-6 Sol
  • ✓Strong performance on long-horizon computer use tasks (OSWorld 2.0)
  • ✓Better alignment with user intent and safety constraints

Best for developers who...

Agentic coding workflows and computer use automationLong-running software engineering tasksDocument-heavy professional work and analysisMulti-step business workflow automationCost-sensitive applications requiring near-flagship performance

When to choose it (and when not to)

Reach for GPT-6.1 Sol when...

  • →When you need Astra-level intelligence but have a 1/5 budget constraint
  • →For repeated and long-running workloads where cost per task matters
  • →When coding performance and computer use are primary requirements

Look elsewhere if...

  • ✕If you require the absolute highest safety standards (GPT-6 Astra has 2.4% vs 4.3% stress-test failure rate)
  • ✕When maximum reasoning on scientific research tasks is critical (Astra scores 68.1% vs Sol's lower score)

How to use it

  • ›Leverage prompt caching for repeated context to maximize the $0.10 cached input rate
  • ›Structure complex tasks into distinct steps for better multi-step workflow handling
  • ›Use higher reasoning effort (High/Max) for scientific or complex analysis work

Quickstart

Python
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
  model="gpt-6.1-sol",
  messages=[{"role": "user", "content": "Write a Python function to sort a list"}]
)
print(response.choices[0].message.content)

API key required; available via OpenAI API with proper authentication.

API model id: gpt-6.1-sol

Benchmarks

BenchmarkScoreNotes
DeepSWE v1.168.8%Matches GPT-6 Astra on long-running software engineering tasks
OSWorld 2.060.5%Computer use benchmark; within 2.1 points of Astra
Terminal-Bench Science 0.1 (Max Effort)Cost: $5.47/taskTask-based pricing; Astra costs $23.80, Opus 5.5 costs $23.21
AutomationBench (Medium Effort)2.2 points above Claude Opus 5.5Professional automation tasks
Factual Accuracy (Low Reasoning Effort)7.7% error rate32% reduction vs GPT-6 Sol's 11.4% error rate

Source: OpenAI Official Announcement

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