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Home/Models/Gemini 3.1 Flash-Lite Preview
Gemini 3.1 Flash-Lite Preview
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Gemini 3.1 Flash-Lite Preview

Google · Closed weights · mid · registry tag 2026 preview
textvisiondocumentaudiocode3 aliases6 official receipts
Open compare
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.
75.4% percentile inside its comparable group
34Raw 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.
18.8% percentile inside its comparable group
5.46sRaw 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.
85.1% percentile inside its comparable group
1,423Raw 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.
16.7% percentile inside its comparable group
1,238Raw 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.
84.2% percentile inside its comparable group
1,239Raw 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.
16.7% percentile inside its comparable group
1,238Raw benchmark value
TutorBench
SL · Reasoning / math / science · Rubric
It is one of the cleaner reads on deliberate reasoning strength rather than style or popularity.
10% percentile inside its comparable group
51.5%Raw benchmark value
MASK
SL · Safety · Rubric
Whether a model stays honest instead of covertly optimizing against the user.
15.4% percentile inside its comparable group
48.4%Raw benchmark value

Receipts and registry checks

official
Google Gemini models docs

May 1, 2026

source →
official
Artificial Analysis

May 1, 2026

source →
official
Arena

May 1, 2026

source →
official
Arena

May 1, 2026

source →
official
Arena

May 1, 2026

source →
official
Arena

May 1, 2026

source →