Gemini 3.1 Pro (Preview Custom Tools)
Gemini 3.1 Pro preview variant tuned for better tool selection behavior in coding agents and multi-tool workflows. It reduces overuse of generic bash tools and improves function-calling reliability while retaining Gemini 3.1 Pro's multimodal reasoning and 1M-token context. NOTE: Inputs > 200k tokens are charged at 2x input and 1.5x output rates.
- Reasoning
- Vision
- Audio Input
- Video Input
- Tool Calling
- Structured Output
Added Feb 27, 2026
Pricing
Auto routing · per 1M tokens- Input
- $2.00
- Output
- $12.00
- Cache read
- $0.20
Specifications
- Context window
- 1M
- Max output
- 65.5K
- Avg output (7d)
- 530 tokens
- Longer than 50% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
29.7
Coding Index
68.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
35.4%
Better than 52% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
9.4%
Better than 51% of models compared
Harvey LAB-AA v1.1
Legal tasks fully passed without material hallucinations
0.0%
Better than 9% of models compared
AA-Briefcase v1.1
Agentic knowledge work (Elo)
456 Elo
Better than 19% of models compared
GDPval-AA v2.1
Economically valuable tasks (Elo)
789 Elo
Better than 35% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
17.8%
Better than 65% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
82.0%
Better than 92% of models compared
MLCR-AA
Medical long-context reasoning
11.1%
Better than 38% of models compared
Reasoning
HLE
Humanity's Last Exam
47.0%
Better than 94% of models compared
IFBench
Instruction-following benchmark
77.1%
Better than 96% of models compared
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
4.0%
Better than 53% of models compared
SciCode
Python programming for scientific computing
58.7%
Better than 91% 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
53.8%
Better than 96% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
95.6%
Better than 95% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
82.0%
Better than 92% of models compared
Last updated Oct 22, 2026
Artificial AnalysisProviders
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Reliability
Last 3 days