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Home/Models/Gemini 3.1 Pro Preview
Gemini 3.1 Pro Preview
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Gemini 3.1 Pro Preview

Google · Closed weights · frontier · registry tag 2026 preview
textcodevisiondocumentaudio5 aliases7 official receipts
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Last verified · May 1, 2026
Visible coverage · 0%
Verified coverage · 0%
Benchmark fit · n/a
Benchmark spread · n/a
Build / data stamp

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Data snapshot May 1, 2026Registry verification passed9 providers · 826 tracked modelsPage refreshed May 7, 2026

Model pages expose the current registry snapshot and page stamp so stale deployments are visible without reading the code.

Score passport by benchmark

Each row keeps the benchmark receipt, source family, raw metric, and percentile inside its exact comparable group.

Thin verified coverageThis model currently reads as thin verified coverage across the resolved evidence surface.
Intelligence Index
AA · Chat / text · Composite
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
100% percentile inside its comparable group
57Raw benchmark value
Time to first token
AA · Chat / text · Speed / cost
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
7.1% percentile inside its comparable group
24.52sRaw benchmark value
Text Arena
AR · Chat / text · Human
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
99.7% percentile inside its comparable group
1,487Raw benchmark value
Code Arena
AR · Coding · Human
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
83.3% percentile inside its comparable group
1,453Raw benchmark value
Vision Arena
AR · Vision understanding · Human
It is useful when the model must read charts, UI, screenshots, or visual scenes rather than text alone.
96% percentile inside its comparable group
1,277Raw benchmark value
WebDev Arena
AR · Coding · Human
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
83.3% percentile inside its comparable group
1,453Raw benchmark value
Document Arena
AR · Document understanding · Human
It matters when the job is reading PDFs, tables, forms, or mixed-layout documents rather than plain chat.
50% percentile inside its comparable group
1,449Raw benchmark value
Debugging
BB · Coding · Rubric
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
50% percentile inside its comparable group
85.9%Raw benchmark value
Security
BB · Coding · Rubric
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
77.8% percentile inside its comparable group
85.2%Raw benchmark value
BS pushback
BB · Professional reasoning · Rubric
Resistance to confidently accepting bogus assumptions in expert-style prompts.
0% percentile inside its comparable group
66.5%Raw benchmark value
Speed throughput
BB · Coding · Speed / cost
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
50% percentile inside its comparable group
122.2 t/sRaw benchmark value
Speed TTFT
BB · Coding · Speed / cost
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
0% percentile inside its comparable group
7608.00msRaw benchmark value

Receipts and registry checks

official
Google Gemini models docs

May 1, 2026

source →
official
Artificial Analysis

May 1, 2026

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official
Arena

May 1, 2026

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official
Arena

May 1, 2026

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official
Arena

May 1, 2026

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official
Arena

May 1, 2026

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official
Arena

May 1, 2026

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