Gemini 3.5 Flash Thinking
Gemini 3.5 Flash with higher thinking enabled for harder reasoning, agentic coding, tool use, multimodal analysis, and long-context workflows. Audio input costs $3.00 per million audio tokens.
- Reasoning
- Vision
- Audio Input
- Video Input
Added May 19, 2026
Pricing
Auto routing · per 1M tokens- Input
- $1.50
- Output
- $9.00
- Cache read
- $0.15
Specifications
- Context window
- 1M
- Max output
- 65.5K
- Avg output (7d)
- 1.6K tokens
- Longer than 85% of models
Benchmarks
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)
873 Elo
Better than 39% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1205 Elo
Better than 61% 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
35.2%
Last updated Oct 8, 2026
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