Google's speed-focused Gemini Flash model for frontier multimodal intelligence across text, images, audio, video, PDFs, and code. Built for agentic coding, reliable tool use, structured outputs, and long-context workflows. Audio input costs $3.00 per million audio tokens.
Added May 19, 2026
Context Window
1.0M
Max Output
65.5K
Avg output tokens (7d)
3.2K tokens
Input Price (Auto)
$1.50/1M
Output Price (Auto)
$9.00/1M
Cache Read (Auto)
$0.15/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
32.6
Coding Index
70.1
Agentic Index
26.0
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
42.1%
Better than 59% of models compared
Harvey LAB-AA
Legal agentic work criterion pass rate
82.1%
Better than 27% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
872 Elo
Better than 38% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1185 Elo
Better than 60% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
19.8%
Better than 69% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
73.3%
Better than 69% of models compared
MLCR-AA
Medical long-context reasoning
18.3%
Better than 63% of models compared
Reasoning
HLE
Humanity's Last Exam
42.7%
Better than 91% of models compared
IFBench
Instruction-following benchmark
76.3%
Better than 95% of models compared
CritPt
Research-level physics reasoning
13.1%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
6.6%
Better than 58% of models compared
SciCode
Python programming for scientific computing
53.9%
Better than 70% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
51.4%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
62.2%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
92.2%
Better than 94% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
40.9%
Better than 87% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
95.3%
Better than 94% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
73.3%
Better than 69% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
34.2%
Last updated Oct 2, 2026
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