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GPT 5.6 Sol Pro

openai/gpt-5.6-sol-pro
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GPT 5.6 Sol Pro

openai/gpt-5.6-sol-pro
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GPT-5.6 Sol with Pro reasoning mode. Pro mode performs more model work for difficult tasks; reasoning effort remains independently configurable and defaults to medium.

Added Jul 9, 2026

Context Window

1.1M

Max Output

128K

Input Price (Auto)

$2.00/1M

Output Price (Auto)

$10.00/1M

Cache Read (Auto)

$0.20/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

47.0

Better than 97% of models compared

Coding Index

77.4

Better than 98% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

60.1%

Better than 88% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

30.3%

Better than 56% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

87.2%

Better than 54% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

1487 Elo

Better than 86% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1588 Elo

Better than 90% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

27.2%

Better than 94% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

84.0%

Better than 98% of models compared

MLCR-AA

Medical long-context reasoning

26.1%

Better than 79% of models compared

Reasoning

HLE

Humanity's Last Exam

49.5%

Better than 97% of models compared

IFBench

Instruction-following benchmark

72.7%

Better than 90% of models compared

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

39.9%

Better than 89% of models compared

SciCode

Python programming for scientific computing

57.1%

Better than 87% of models compared

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

94.1%

Better than 99% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

65.9%

Better than 99% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

85.1%

Better than 77% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

84.0%

Better than 98% of models compared

Last updated Sep 28, 2026

Artificial Analysis

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