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GPT-6.1 Sol

OpenAI · released Sep 29, 2026

ProprietaryReasoningTool useStructured outputVision

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional...

Specification

Context window
1.05M
Max output
128K
Knowledge cutoff
Not stated
Parameters
Undisclosed
Licence
Proprietary
Serving providers
3
Moderated
Yes
Uptime
100.0%

Intelligence

51.8

98th percentile

Coding

—

Coding Index

Agentic

—

Agentic Index

Output speed

133 t/s

Median across providers

Latency

1.77s

Time to first token

Cost per task

$0.90

Estimated

Benchmarks

Where the score comes from

The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.

Evaluation scores

Percentage correct · higher is better

  • AA-LCR (long context)
    83.0%
  • SciCode
    54.2%
  • Humanity's Last Exam
    52.9%

An evaluation missing from this list was not run for this model — it is not a zero.

View as table
EvaluationScore
AA-LCR (long context)83.0%
SciCode54.2%
Humanity's Last Exam52.9%

Against its peers

Intelligence Index · this model highlighted, nearest peers in grey

Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.

View as table
ModelIntelligence
Claude Fable 5.153.4
GPT-6 Astra52.7
Gemini 4 Argon52.6
Muse Wonder52.4
GPT-6.1 Sol51.8
Claude Opus 550.8
Claude Fable 549.6
Muse Spark 1.348.1

Percentile among all indexed models

Intelligence98th
Arena Elo98th

Pricing

What it costs to run

List prices per million tokens, plus what one representative task works out to.

List price

Input / 1M tokens
$2
Output / 1M tokens
$10
Cached input / 1M
$0.1
Blended 3:1
$4

One task, estimated

$0.90

Input tokens
50,000
Output tokens
80,000
Profile
Reasoning

Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).

Serving providers

Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.

Arena

Head-to-head generation quality

Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.

Overall Elo
1,464
Win rate
71.4%
Strongest at
3D scenes
Tournaments
315

Elo by category

Dot position on a 1,300–1,475 scale · Elo has no meaningful zero

Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.

View as table
CategoryEloWin rate
3D scenes146471.4%
Game development138061.9%
UI components134457.6%
Websites132255.8%