Gemini 3.5 Flash-Lite vs GPT-5.2

Updated October 19, 2026. Rank says GPT-5.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.2 has the higher public score estimate, 59.88 versus 49.61, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemini 3.5 Flash-Lite

Google

49.61/100

Supported · Public rank #97

90% interval 37.0–62.3

Model B

GPT-5.2

OpenAI

59.88/100

Supported · Public rank #57

90% interval 52.8–67.0

Shared results: 4
Gemini 3.5 Flash-Lite only: 8
GPT-5.2 only: 27
Like-for-like categories: 3 / 8

Supported: Gemini 3.5 Flash-Lite and GPT-5.2How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.2
GPT-5.2 has the higher public coding point estimate, 36.9 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.2
GPT-5.2 has the higher public agentic point estimate, 44.6 to 35.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

35.8Gemini 3.5 Flash-Lite36.9GPT-5.2

Like-for-like · BenchAlign v5.8

GPT-5.2 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

FrontierMath v2 (Tiers 1-3)Math
Normalized gap 41.4
Gemini 3.5 Flash-Lite:25.965%
GPT-5.2:67.400%
Shared source
FrontierMath v2 (Tier 4)Math
Normalized gap 31.7
Gemini 3.5 Flash-Lite:0.000%
GPT-5.2:31.700%
Shared source
OSWorld-VerifiedAgentic
Normalized gap 26.7
Gemini 3.5 Flash-Lite:74%
GPT-5.2:47.3%
Gemini 3.5 Flash-Lite source GPT-5.2 source
SWE-bench ProCoding
Normalized gap 1.4
Gemini 3.5 Flash-Lite:54.2%
GPT-5.2:55.6%
Gemini 3.5 Flash-Lite source GPT-5.2 source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Like-for-like

Gemini 3.5 Flash-Lite: 35.6; Supported · #81/130
GPT-5.2: 44.6; Supported · #60/130
Basis: BenchAlign v5.8 lane · 3 vs 4 public rows
Reading: GPT-5.2 leads · intervals overlap

Coding

Like-for-like

Gemini 3.5 Flash-Lite: 35.8; Supported · #77/151
GPT-5.2: 36.9; Supported · #75/151
Basis: BenchAlign v5.8 lane · 4 vs 3 public rows
Reading: GPT-5.2 leads · intervals overlap

Knowledge

Like-for-like

Gemini 3.5 Flash-Lite: 49.1; Supported · #86/186
GPT-5.2: 59.8; Supported · #51/186
Basis: BenchAlign v5.8 lane · 2 vs 1 public rows
Reading: GPT-5.2 leads · intervals overlap

Reasoning

Not comparable

Gemini 3.5 Flash-Lite: 70.9; Unranked · 3 rankable rows
GPT-5.2: 64.3; Unranked · 4 rankable rows
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 3.5 Flash-Lite: 80.9; Unranked · 1 rankable row
GPT-5.2: 75.9; #22/55
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.5 Flash-Lite: Not ranked
GPT-5.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.5 Flash-Lite: Not ranked
GPT-5.2: 92.6; #19/148
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.5 Flash-Lite: 37.8; Unranked · 2 rankable rows
GPT-5.2: 71.1; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.5 Flash-Lite and GPT-5.2
Category	Gemini 3.5 Flash-Lite	GPT-5.2	Basis	Reading
Agentic	35.6Supported · #81/130	44.6Supported · #60/130	Like-for-likeBenchAlign v5.8 lane · 3 vs 4 public rows	GPT-5.2 leads · intervals overlap
Coding	35.8Supported · #77/151	36.9Supported · #75/151	Like-for-likeBenchAlign v5.8 lane · 4 vs 3 public rows	GPT-5.2 leads · intervals overlap
Knowledge	49.1Supported · #86/186	59.8Supported · #51/186	Like-for-likeBenchAlign v5.8 lane · 2 vs 1 public rows	GPT-5.2 leads · intervals overlap
Reasoning	70.9Unranked · 3 rankable rows	64.3Unranked · 4 rankable rows	Not comparableProvisional lane · 1 vs 1 weighted rows	Not comparable
Multimodal	80.9Unranked · 1 rankable row	75.9#22/55	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	92.6#19/148	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	37.8Unranked · 2 rankable rows	71.1Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Gemini 3.5 Flash-Lite: $0.00155; Fits in one request
GPT-5.2: $0.00875; Fits in one request

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash-Lite: $0.0225; Fits in one request
GPT-5.2: $0.1295; Fits in one request

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemini 3.5 Flash-Lite: $0.037; Fits in one request
GPT-5.2: $0.525; Fits in one request; Cached input priced at the published list-input rate

Gemini 3.5 Flash-Lite has the lower modeled cost

GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGemini 3.5 Flash-LiteGPT-5.2

Context window

Maximum documented context; output-token limits may be lower.

Gemini 3.5 Flash-Lite

Google latest Gemini model guide

GPT-5.2

400K

API model ID

Gemini 3.5 Flash-Lite

gemini-3.5-flash-lite

Google Gemini 3.5 Flash-Lite model documentation

GPT-5.2

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemini 3.5 Flash-Lite

$0.03 per 1M cached input tokens

Google Gemini API pricing

GPT-5.2

Not published

Documented inputs

Gemini 3.5 Flash-Lite

text, image, video, audio, pdf

Google Gemini 3.5 Flash-Lite model documentation

GPT-5.2

Not sourced

Documented outputs

Gemini 3.5 Flash-Lite

text

Google Gemini 3.5 Flash-Lite model documentation

GPT-5.2

Not sourced

Provider availability

Gemini 3.5 Flash-Lite

Generally Available · Gemini API, Google AI Studio

Google latest Gemini model guide

GPT-5.2

Not sourced

Reasoning profile

Gemini 3.5 Flash-Lite

Reasoning

GPT-5.2

Reasoning

Weight access

Gemini 3.5 Flash-Lite

Proprietary

GPT-5.2

Proprietary

License

Gemini 3.5 Flash-Lite

Proprietary

GPT-5.2

Proprietary

Release date

Gemini 3.5 Flash-Lite

2026-07-21

GPT-5.2

2025-12-11

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: GPT-5.2 has the higher public score estimate, 59.88 versus 49.61, but the 90% score intervals overlap.
Workload cost: Repository review: $0.0225 vs $0.1295. Cache-heavy agent loop: $0.037 vs $0.525.
Context tradeoff: Gemini 3.5 Flash-Lite has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 3.5 Flash-Lite or GPT-5.2?

GPT-5.2 has the higher public score estimate, 59.88 versus 49.61, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 3.5 Flash-Lite or GPT-5.2?

GPT-5.2 has the higher public coding point estimate, 36.9 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GPT-5.2?

GPT-5.2 has the higher public agentic tasks point estimate, 44.6 to 35.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Gemini 3.5 Flash-Lite or GPT-5.2?

For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.00875 on GPT-5.2; repository review costs $0.0225 and $0.1295; the cache-heavy agent loop costs $0.037 and $0.525. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 3.5 Flash-Lite or GPT-5.2?

Gemini 3.5 Flash-Lite has the larger documented context window: 1M, compared with 400K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence39 rows

Agentic

Terminal-Bench 2.1
Gemini 3.5 Flash-Lite54.0%
Source
GPT-5.2—
Not directly comparable
OSWorld-Verified
Gemini 3.5 Flash-Lite74%
Source
GPT-5.247.3%
Source
Gemini 3.5 Flash-Lite leads this result
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%
Source
GPT-5.2—
Not directly comparable
BrowseComp
Gemini 3.5 Flash-Lite—
GPT-5.265.8%
Source
Not directly comparable
Gert Labs
Gemini 3.5 Flash-Lite—
GPT-5.246.54%
Source
Not directly comparable
JobBench
Gemini 3.5 Flash-Lite—
GPT-5.234.3%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
Gemini 3.5 Flash-Lite54.0%
Source
GPT-5.2—
Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%
Source
GPT-5.255.6%
Source
GPT-5.2 leads this result
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%
Source
GPT-5.2—
Not directly comparable
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%
Source
GPT-5.2—
Not directly comparable
SWE-bench Verified
Gemini 3.5 Flash-Lite—
GPT-5.280%
Source
Not directly comparable
Vibe Code Bench
Gemini 3.5 Flash-Lite—
GPT-5.253.50%
Source
Not directly comparable

Reasoning

MRCRv2
Gemini 3.5 Flash-Lite72.2%
Source
GPT-5.2—
Not directly comparable
ARC-AGI-2
Gemini 3.5 Flash-Lite—
GPT-5.252.9%
Source
Not directly comparable
ARC-AGI-1
Gemini 3.5 Flash-Lite—
GPT-5.286.17%
Source
Not directly comparable

Multimodal

MMMU
Gemini 3.5 Flash-Lite—
GPT-5.286.7%
Source
Not directly comparable
MMMU-Pro
Gemini 3.5 Flash-Lite—
GPT-5.279.5%
Source
Not directly comparable
RealWorldQA
Gemini 3.5 Flash-Lite—
GPT-5.283.3%
Source
Not directly comparable
OmniDocBench 1.5
Gemini 3.5 Flash-Lite—
GPT-5.285.7%
Source
Not directly comparable
Video-MME (with subtitle)
Gemini 3.5 Flash-Lite—
GPT-5.286.0%
Source
Not directly comparable
Video-MME (w/o subtitle)
Gemini 3.5 Flash-Lite—
GPT-5.285.8%
Source
Not directly comparable
MathVision
Gemini 3.5 Flash-Lite—
GPT-5.283.0%
Source
Not directly comparable
We-Math
Gemini 3.5 Flash-Lite—
GPT-5.279.0%
Source
Not directly comparable
DynaMath
Gemini 3.5 Flash-Lite—
GPT-5.286.8%
Source
Not directly comparable
SimpleVQA
Gemini 3.5 Flash-Lite—
GPT-5.255.8%
Source
Not directly comparable
CC-OCR
Gemini 3.5 Flash-Lite—
GPT-5.270.3%
Source
Not directly comparable
AI2D_TEST
Gemini 3.5 Flash-Lite—
GPT-5.292.2%
Source
Not directly comparable
CountBench
Gemini 3.5 Flash-Lite—
GPT-5.291.9%
Source
Not directly comparable
ERQA
Gemini 3.5 Flash-Lite—
GPT-5.259.8%
Source
Not directly comparable
VideoMMMU
Gemini 3.5 Flash-Lite—
GPT-5.285.9%
Source
Not directly comparable
MLVU (M-Avg)
Gemini 3.5 Flash-Lite—
GPT-5.285.6%
Source
Not directly comparable
CharXiv
Gemini 3.5 Flash-Lite—
GPT-5.282.1%
Source
Not directly comparable
V*
Gemini 3.5 Flash-Lite—
GPT-5.275.9%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%
Source
GPT-5.2—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%
Source
GPT-5.2—
Not directly comparable
GPQA
Gemini 3.5 Flash-Lite—
GPT-5.292.4%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
Gemini 3.5 Flash-Lite25.965%
GPT-5.267.400%
GPT-5.2 leads this result
FrontierMath v2 (Tier 4)
Shared source
Gemini 3.5 Flash-Lite0.000%
GPT-5.231.700%
GPT-5.2 leads this result
AIME 2025
Gemini 3.5 Flash-Lite—
GPT-5.2100%
Source
Not directly comparable

39 public results · 4 shared

Related comparisons

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Last updated October 19, 2026

Gemini 3.5 Flash-Lite vs GPT-5.2

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemini 3.5 Flash-Lite or GPT-5.2?

Which is better for coding, Gemini 3.5 Flash-Lite or GPT-5.2?

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GPT-5.2?

Which costs less, Gemini 3.5 Flash-Lite or GPT-5.2?

Which has the larger context window, Gemini 3.5 Flash-Lite or GPT-5.2?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemini 3.5 Flash-Lite vs GPT-5.2