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Solar Pro 3

upstage/solar-pro-3
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Solar Pro 3

upstage/solar-pro-3
Back

Upstage's Solar Pro 3 is a Mixture-of-Experts (MoE) language model with 102B total parameters and 12B active parameters per forward pass, optimized for Korean with strong English and Japanese support.

Added Mar 3, 2026

Context Window

128.0K

Max Output

128.0K

Input Price (Auto)

$0.15/1M

Output Price (Auto)

$0.60/1M

Cache Read (Auto)

$0.015/1M

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

7.8

Better than 28% of models compared

Coding Index

16.2

Better than 19% of models compared

Agentic Index

1.4

Better than 26% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

0.5%

Better than 9% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

100 Elo

Better than 10% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

251 Elo

Better than 16% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

1.6%

Better than 11% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

32.3%

Better than 32% of models compared

MLCR-AA

Medical long-context reasoning

0.6%

Better than 4% of models compared

Reasoning

HLE

Humanity's Last Exam

10.3%

Better than 51% of models compared

IFBench

Instruction-following benchmark

71.2%

Better than 87% of models compared

CritPt

Research-level physics reasoning

0.0%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

25.5%

Better than 6% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

18.5%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

88.2%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

72.4%

Better than 52% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

7.6%

Better than 40% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

86.3%

Better than 79% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

32.3%

Better than 32% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

0.0%

Last updated Oct 1, 2026

Artificial Analysis

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