What is the IQ of gemini-3.5-flash?

gemini-3.5-flash has an estimated IQ of 120, which ranks #47 out of 176 public models on the AI IQ leaderboard. The score combines seven equally weighted reasoning dimensions using measured benchmark results and conservative estimates for missing coverage.

What is gemini-3.5-flash best at?

Among the dimensions with published benchmark results, gemini-3.5-flash ranks highest in Abstract Reasoning (IQ 112, #25 of 201) and Academic Reasoning (IQ 130, #39 of 203). Its lowest-ranked measured dimension is Programmatic Reasoning (IQ 113, #83 of 177).

What is the EQ of gemini-3.5-flash?

gemini-3.5-flash has an estimated EQ of 95. EQ measures emotional reasoning — how well a model reads and responds to emotional context — and is scored separately from IQ.

Which benchmarks has gemini-3.5-flash been tested on?

gemini-3.5-flash has published results on 26 of the 39 benchmarks behind AI IQ's Composite IQ, including EnterpriseOps-Gym-AA, Chess Puzzles, MMMU-Pro, SimpleQA Verified and AA Omniscience.

How much does gemini-3.5-flash cost?

gemini-3.5-flash costs $1.50 per million input tokens and $9.00 per million output tokens. Adjusted for how many tokens it actually consumes on real tasks, its effective cost works out to $9.07 per million input-plus-output tokens.

Is gemini-3.5-flash open source?

No — gemini-3.5-flash is a proprietary model. Its weights are not publicly available, so it is used through hosted APIs and products.

Who developed gemini-3.5-flash?

gemini-3.5-flash was developed by Google, based in the United States.

When was gemini-3.5-flash released?

gemini-3.5-flash was publicly released on May 18, 2026.

How large is the context window of gemini-3.5-flash?

gemini-3.5-flash supports a context window of 1,000,000 tokens — the amount of text it can consider in a single request.

How fast is gemini-3.5-flash?

In independent measurements, gemini-3.5-flash generates about 204 tokens per second, with a typical response time of about 16 seconds.

GoogleReleased May 18, 2026

Proprietary

gemini-3.5-flash

Strongest in Abstract Reasoning (#25 of 201) and Academic Reasoning (#39 of 203); weakest in Programmatic Reasoning (#83 of 177). All seven dimensions are measured.

Composite IQ ?

120

#47 of 176 ranked models · ahead of 74%

Google's #7 model, behind Gemini 4 Argon (140)

Effective cost

$9.07/1M

$1.50 in · $9 out

Speed

204tok/s

16s typical response

Context

1Mtokens

1,000,000 tokens in

Evidence

7of 7 dims

26 of 39 benchmarks

Compare with gpt-6.1-solAPI pricing

IQ profile

Seven dimensions, averaged equally into Composite IQ

Compare shape with

Measured95% range

#1 in dimensionTop-10 median

IQ dimensions

Ranks are among models scored in each dimension. Ranges are model-based estimates, not calibrated confidence intervals.

DimensionIQPosition · 70–160RankEvidencevs Top 10

Abstract ReasoningAbstract

112measured

#25 / 201

4/5

-14.5

Mathematical ReasoningMath

124measured

#51 / 176

4/5

-23

Academic ReasoningAcademic

130measured

#39 / 203

5/6

-5.5

Programmatic ReasoningCoding

113measured

#83 / 177

2/6

-36.5

Computer UseComputer Use

122measured

#42 / 176

2/5

-21

ReliabilityReliability

121measured

#40 / 177

6/6

-13

Knowledge WorkKnowledge Work

123measured

#47 / 176

3/6

-8.5

gemini-3.5-flash95% range (model-based, uncalibrated)Top-10 median#1 in dimension100 = average human

Benchmark results

26 of 39 Composite IQ benchmarks have a published result for gemini-3.5-flash. Benchmarks without a result stay listed so gaps are visible.

Abstract Reasoning IQ 112 · measuredBest: #8 of 76 on Chess Puzzles4/5

BenchmarkScorePercentileRankLeader · source

Chess Puzzles

Epoch AI vEpoch 100-position leaderboard (task implementation versions recorded per run) · Oct 14

50.0%

#8 / 76

Leader gpt-6-astra · 72.0%
Epoch AI vEpoch 100-position leaderboard (task implementation versions recorded per run) · Oct 14

ARC-AGI-2

Full source leaderboard reconciliation · Oct 23

72.1%

#20 / 68

Leader gpt-6-astra · 95.0%
Full source leaderboard reconciliation · Oct 23

ARC-AGI-1

Full source leaderboard reconciliation · Oct 23

92.5%

#22 / 68

Leader fable-5 · 98.5%
Full source leaderboard reconciliation · Oct 23

Mystery Game Puzzles

Full source leaderboard reconciliation vEpoch 100-position leaderboard (task implementation versions recorded per run) · Oct 23

32.0%

#24 / 55

Leader gpt-6-astra · 84.0%
Full source leaderboard reconciliation vEpoch 100-position leaderboard (task implementation versions recorded per run) · Oct 23

Not yet reported

ARC-AGI-3

Mathematical Reasoning IQ 124 · measuredBest: #36 of 84 on FrontierMath Tier 1-34/5

BenchmarkScorePercentileRankLeader · source

FrontierMath Tier 1-3

Full source leaderboard reconciliation · Oct 23

62.8%

#36 / 84

Leader gpt-6-astra · 93.7%
Full source leaderboard reconciliation · Oct 23

FrontierMath Tier 4

Full source leaderboard reconciliation · Oct 23

26.8%

#36 / 72

Leader gpt-6.1-sol · 100%
Full source leaderboard reconciliation · Oct 23

MathArena

Full source leaderboard reconciliation · Oct 28

24.5%

#33 / 64

Leader gpt-6.1-sol · 84.0%
Full source leaderboard reconciliation · Oct 28

ProofBench 1.1

Full source leaderboard reconciliation v1.1 · Oct 27

31.0%

#34 / 48

Leader fable-5.1 · 100%
Full source leaderboard reconciliation v1.1 · Oct 27

Not yet reported

AIME

Academic Reasoning IQ 130 · measuredBest: #11 of 89 on MMMU-Pro5/6

BenchmarkScorePercentileRankLeader · source

MMMU-Pro

Artificial Analysis v4.3.2 · Oct 27

83.9%

#11 / 89

Leader fable-5.1 · 90.6%
Artificial Analysis v4.3.2 · Oct 27

GPQA Diamond

Artificial Analysis v4.3.2 · Oct 27

92.1%

#27 / 147

Leader gpt-6-astra · 96.1%
Artificial Analysis v4.3.2 · Oct 27

Humanity's Last Exam

Artificial Analysis v4.3.2 · Oct 27

41.3%

#41 / 167

Leader opus-5.5 · 61.4%
Artificial Analysis v4.3.2 · Oct 27

SciCode

Artificial Analysis model leaderboard · Sep 10

53.0%

#48 / 167

Leader opus-5.5 · 66.9%
Artificial Analysis model leaderboard · Sep 10

CritPt

Artificial Analysis v4.3.2 · Oct 27

10.9%

#57 / 159

Leader Muse Wonder Full · 32.9%
Artificial Analysis v4.3.2 · Oct 27

Not yet reported

MMLU-Pro

Programmatic Reasoning IQ 113 · measuredBest: #14 of 25 on SWE-rebench2/6

BenchmarkScorePercentileRankLeader · source

SWE-rebench

SWE-rebench · Sep 22

49.5%

#14 / 25

Leader fable-5 · 64.5%
SWE-rebench · Sep 22

ProgramBench

Full source leaderboard reconciliation · Oct 23

1.5%

#43 / 61

Leader opus-5.5 · 65.0%
Full source leaderboard reconciliation · Oct 23

Not yet reported

LiveCodeBenchIOI V2Terminal-Bench 4.0MirrorCode

Computer Use IQ 122 · measuredBest: #6 of 32 on MCP Atlas2/5

BenchmarkScorePercentileRankLeader · source

MCP Atlas

Full source leaderboard reconciliation · Oct 23

83.6%

#6 / 32

Leader muse-spark-1.1 · 88.1%
Full source leaderboard reconciliation · Oct 23

OSWorld-Verified

Full source leaderboard reconciliation · Oct 23

78.4%

#11 / 24

Leader fable-5 · 85.0%
Full source leaderboard reconciliation · Oct 23

Not yet reported

BrowseCompArena.ai Agent ArenaAgents' Last Exam

Reliability IQ 121 · measuredBest: #14 of 80 on SimpleQA Verified6/6

BenchmarkScorePercentileRankLeader · source

SimpleQA Verified

Full source leaderboard reconciliation · Oct 23

66.2%

#14 / 80

Leader Gemini 4 Argon · 76.0%
Full source leaderboard reconciliation · Oct 23

AA Omniscience

Artificial Analysis v4.3.2 · Oct 27

20.8

#28 / 158

Leader opus-5.5 · 46.4
Artificial Analysis v4.3.2 · Oct 27

IFBench

Artificial Analysis v4.3.2 · Oct 27

74.6%

#27 / 96

Leader minimax-m3 · 82.9%
Artificial Analysis v4.3.2 · Oct 27

FACTS Grounding

Full source leaderboard reconciliation · Oct 23

73.2%

#12 / 36

Leader gpt-6-sol · 85.2%
Full source leaderboard reconciliation · Oct 23

AA Long Context Reasoning v1.1

Artificial Analysis v4.3.2 · Oct 27

74.3%

#90 / 154

Leader kimi-k3 · 88.7%
Artificial Analysis v4.3.2 · Oct 27

BullshitBench v2

Full source leaderboard reconciliation · Oct 23

20.0%

#66 / 83

Leader opus-4.8 · 94.0%
Full source leaderboard reconciliation · Oct 23

Knowledge Work IQ 123 · measuredBest: #3 of 31 on EnterpriseOps-Gym-AA3/6

BenchmarkScorePercentileRankLeader · source

EnterpriseOps-Gym-AA

Full source leaderboard reconciliation v1117-task-strict-2026-09-29 · Oct 27

50.1%

#3 / 31

Leader fable-5 · 51.1%
Full source leaderboard reconciliation v1117-task-strict-2026-09-29 · Oct 27

AA-AnalystAgent

Full source leaderboard reconciliation vpass^5-2026-09-29 · Oct 27

45.0%

#16 / 35

Leader gemini-3.7-flash · 60.0%
Full source leaderboard reconciliation vpass^5-2026-09-29 · Oct 27

AA-Briefcase v1.1 Elo

Artificial Analysis v4.3.2 · Oct 27

876

#63 / 111

Leader sonnet-5.5 · 1,812
Artificial Analysis v4.3.2 · Oct 27

Not yet reported

GDPval-AA v2.1AutomationBench-AAGDP.pdf

Release & availability

Release details on record for this model.

Released: May 18, 2026
Developer: Google
Weights: ProprietaryHosted access only
Context: 1,000,000 tokens
API price: $1.50 in · $9 outper 1M tokens · full pricing

About this score

How much of gemini-3.5-flash's IQ is measured, and how the rest is estimated.

26/ 39benchmark results

7/ 7dimensions measured

0estimated

4/5Abstract

4/5Math

5/6Academic

2/6Coding

2/5Computer Use

6/6Reliability

3/6Knowledge Work

Bar height = share of benchmarks with resultsEstimatedNo score

Measured results are shown as published

Every score in the benchmark tables comes from its source unchanged. Composite IQ averages all seven dimensions equally.

How gaps are handled

Every dimension has at least one published result. Missing benchmarks can also draw on its predecessor, Gemini 3 Flash; a predecessor is an estimation input, not evidence that this model has the predecessor's measured results.

A missing result is not a zero

Benchmark versions and evaluation harnesses can differ between sources, so small gaps between models may not be meaningful.

Read the scoring methodology →

Compare gemini-3.5-flash

Side-by-side pages for the models it is most often weighed against.

#1 overall

gpt-6.1-sol

IQ141 21 higherEffective cost$6.30 1.4× cheaper

Predecessor

gemini-3-flash

IQ111 9 lowerEffective cost$2.60 3.5× cheaper

IQ120 same IQEffective cost$1.42 6.4× cheaper

Most similar profile

gpt-5.4

IQ122 2 higherEffective cost$20.47 2.3× pricier

Similar price

grok-4.6

IQ126 6 higherEffective cost$8.88 about the same

gemini-3.5-flashComparison model

Compare with any modelBrowse all comparisons →

Frequently asked questions

Quick answers about gemini-3.5-flash, drawn from the same data as the rest of this page.

How AI IQ scores models →Get this data from the API →

What is the IQ of gemini-3.5-flash?: gemini-3.5-flash has an estimated IQ of 120, which ranks #47 out of 176 public models on the AI IQ leaderboard. The score combines seven equally weighted reasoning dimensions using measured benchmark results and conservative estimates for missing coverage.
What is gemini-3.5-flash best at?: Among the dimensions with published benchmark results, gemini-3.5-flash ranks highest in Abstract Reasoning (IQ 112, #25 of 201) and Academic Reasoning (IQ 130, #39 of 203). Its lowest-ranked measured dimension is Programmatic Reasoning (IQ 113, #83 of 177).
What is the EQ of gemini-3.5-flash?: gemini-3.5-flash has an estimated EQ of 95. EQ measures emotional reasoning — how well a model reads and responds to emotional context — and is scored separately from IQ.
Which benchmarks has gemini-3.5-flash been tested on?: gemini-3.5-flash has published results on 26 of the 39 benchmarks behind AI IQ's Composite IQ, including EnterpriseOps-Gym-AA, Chess Puzzles, MMMU-Pro, SimpleQA Verified and AA Omniscience.
How much does gemini-3.5-flash cost?: gemini-3.5-flash costs $1.50 per million input tokens and $9.00 per million output tokens. Adjusted for how many tokens it actually consumes on real tasks, its effective cost works out to $9.07 per million input-plus-output tokens.
Is gemini-3.5-flash open source?: No — gemini-3.5-flash is a proprietary model. Its weights are not publicly available, so it is used through hosted APIs and products.
Who developed gemini-3.5-flash?: gemini-3.5-flash was developed by Google, based in the United States.
When was gemini-3.5-flash released?: gemini-3.5-flash was publicly released on May 18, 2026.
How large is the context window of gemini-3.5-flash?: gemini-3.5-flash supports a context window of 1,000,000 tokens — the amount of text it can consider in a single request.
How fast is gemini-3.5-flash?: In independent measurements, gemini-3.5-flash generates about 204 tokens per second, with a typical response time of about 16 seconds.