A lightweight version of GPT-5 for cost-sensitive applications. Balances advanced capabilities with efficient resource usage.
Added Aug 7, 2025
Context Window
400.0K
Max Output
128.0K
Avg output tokens (7d)
1.1K tokens
Input Price (Auto)
$0.25/1M
Output Price (Auto)
$2.00/1M
Cache Read (Auto)
$0.025/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
16.8
Coding Index
15.6
Agentic Index
6.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
6.5%
Better than 31% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
428 Elo
Better than 20% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
754 Elo
Better than 36% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
8.4%
Better than 35% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
72.3%
Better than 68% of models compared
Reasoning
HLE
Humanity's Last Exam
21.5%
Better than 70% of models compared
IFBench
Instruction-following benchmark
75.4%
Better than 93% 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
39.0%
Better than 23% of models compared
LiveCodeBench
Contamination-free coding benchmark
83.8%
Better than 95% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
90.7%
Better than 91% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
83.7%
Better than 86% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
25.0%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
56.4%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
82.8%
Better than 72% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
33.3%
Better than 77% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
68.4%
Better than 61% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
72.3%
Better than 68% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
12.7%
Last updated Oct 1, 2026
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