Model A

Google

70.11/100

Supported · Public rank #21

90% interval 63.7–75.9

Gemini 3.6 Flash vs GPT-5.5

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

GPT-5.5

OpenAI

73.27/100

Supported · Public rank #9

90% interval 71.0–75.6

Decision reading

GPT-5.5 has the higher public score estimate, 73.27 versus 70.11, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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-5.5
GPT-5.5 leads on the public coding lane, 67.7 to 58.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.5
GPT-5.5 leads on the public agentic lane, 63.9 to 50.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.6 Flash
Gemini 3.6 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.6 Flash
Gemini 3.6 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.6 Flash
Gemini 3.6 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 Gemini 3.6 Flash only

7 Shared

31 GPT-5.5 only

Shared results: 7
Gemini 3.6 Flash only: 1
GPT-5.5 only: 31
Like-for-like categories: 3 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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 3.6 Flash: 50.7; Supported · #60/151
GPT-5.5: 63.9; Supported · #14/151
Basis: BenchAlign lane · 2 vs 13 public rows
Reading: GPT-5.5 leads · intervals overlap

Coding

Like-for-like

Gemini 3.6 Flash: 58.9; Supported · #30/183
GPT-5.5: 67.7; Supported · #8/183
Basis: BenchAlign lane · 4 vs 9 public rows
Reading: GPT-5.5 leads · intervals overlap

Knowledge

Like-for-like

Gemini 3.6 Flash: 68.6; Supported · #18/181
GPT-5.5: 73.3; Supported · #7/181
Basis: BenchAlign lane · 2 vs 6 public rows
Reading: GPT-5.5 leads · intervals overlap

Reasoning

Not comparable

Gemini 3.6 Flash: 77.8; Unranked · 2 rankable rows
GPT-5.5: 63.5; #15/22
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.6 Flash: Not ranked
GPT-5.5: 69.6; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.6 Flash: Not ranked
GPT-5.5: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 3.6 Flash: 82.3; Unranked · 1 rankable row
GPT-5.5: 70.5; #19/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.6 Flash: Not ranked
GPT-5.5: 92.9; #7/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.6 Flash and GPT-5.5
Category	Gemini 3.6 Flash	GPT-5.5	Basis	Reading
Agentic	50.7Supported · #60/151	63.9Supported · #14/151	Like-for-likeBenchAlign lane · 2 vs 13 public rows	GPT-5.5 leads · intervals overlap
Coding	58.9Supported · #30/183	67.7Supported · #8/183	Like-for-likeBenchAlign lane · 4 vs 9 public rows	GPT-5.5 leads · intervals overlap
Knowledge	68.6Supported · #18/181	73.3Supported · #7/181	Like-for-likeBenchAlign lane · 2 vs 6 public rows	GPT-5.5 leads · intervals overlap
Reasoning	77.8Unranked · 2 rankable rows	63.5#15/22	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Math	Not ranked	69.6Unranked · 3 rankable rows	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
Multimodal	82.3Unranked · 1 rankable row	70.5#19/48	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Instruction following	Not ranked	92.9#7/120	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

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

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.

cursorBench32
Coding
Gemini 3.6 Flash: 53.5%GPT-5.5: 58.4%Normalized gap 4.9Shared source
OSWorld-Verified
Agentic
Gemini 3.6 Flash: 83%GPT-5.5: 78.7%Normalized gap 4.3Gemini 3.6 Flash source GPT-5.5 source
LiveCodeBench (Vals)
Coding
Gemini 3.6 Flash: 88.1%GPT-5.5: 85.3%Normalized gap 2.8Gemini 3.6 Flash source GPT-5.5 source
MMLU-Pro (Vals)
Knowledge
Gemini 3.6 Flash: 89.3%GPT-5.5: 88.1%Normalized gap 1.2Gemini 3.6 Flash source GPT-5.5 source

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 3.6 Flash: $0.00525; Fits in one request
GPT-5.5: $0.02; Fits in one request

Gemini 3.6 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.6 Flash: $0.0975; Fits in one request
GPT-5.5: $0.34; Fits in one request

Gemini 3.6 Flash 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 3.6 Flash: $0.135; Fits in one request
GPT-5.5: $0.5; Fits in one request

Gemini 3.6 Flash has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

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

SpecificationGemini 3.6 FlashGPT-5.5

Context window

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

Gemini 3.6 Flash

Google latest Gemini model guide

GPT-5.5

OpenAI pricing

API model ID

Gemini 3.6 Flash

gemini-3.6-flash

Google Gemini 3.6 Flash model documentation

GPT-5.5

gpt-5.5

OpenAI pricing

Cached-input rate

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

Gemini 3.6 Flash

$0.15 per 1M cached input tokens

Google Gemini API pricing

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

Documented inputs

Gemini 3.6 Flash

text, image, video, audio, pdf

Google Gemini 3.6 Flash model documentation

GPT-5.5

Not sourced

Documented outputs

Gemini 3.6 Flash

text

Google Gemini 3.6 Flash model documentation

GPT-5.5

Not sourced

Provider availability

Gemini 3.6 Flash

Generally Available · Gemini API, Google AI Studio

Google latest Gemini model guide

GPT-5.5

Not sourced

Reasoning profile

Gemini 3.6 Flash

Reasoning

GPT-5.5

Reasoning

Weight access

Gemini 3.6 Flash

Proprietary

GPT-5.5

Proprietary

License

Gemini 3.6 Flash

Proprietary

GPT-5.5

Proprietary

Release date

Gemini 3.6 Flash

2026-07-21

GPT-5.5

2026-04-23

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-5.5 has the higher public score estimate, 73.27 versus 70.11, but the 90% score intervals overlap.
Workload cost: Repository review: $0.0975 vs $0.34. Cache-heavy agent loop: $0.135 vs $0.5.
Context tradeoff: Both models list 1M.

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

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

OSWorld-Verified
Gemini 3.6 Flash83%
Source
GPT-5.578.7%
Source
Gemini 3.6 Flash leads this result
Terminal-Bench 2.1 (Vals)
Gemini 3.6 Flash73.8%
Source
GPT-5.576.4%
Source
GPT-5.5 leads this result
Terminal-Bench 2.0
Gemini 3.6 Flash—
GPT-5.582%
Source
Not directly comparable
CyberGym
Gemini 3.6 Flash—
GPT-5.581.8%
Source
Not directly comparable
BrowseComp
Gemini 3.6 Flash—
GPT-5.584.4%
Source
Not directly comparable
MCP Atlas
Gemini 3.6 Flash—
GPT-5.575.3%
Source
Not directly comparable
Toolathlon
Gemini 3.6 Flash—
GPT-5.555.6%
Source
Not directly comparable
τ²-bench results
Gemini 3.6 Flash—
GPT-5.598%
Source
Not directly comparable
Gert Labs
Gemini 3.6 Flash—
GPT-5.572.93%
Source
Not directly comparable
ResearchClawBench
Gemini 3.6 Flash—
GPT-5.517.0%
Source
Not directly comparable
OSWorld 2.0
Gemini 3.6 Flash—
GPT-5.513.0%
Source
Not directly comparable
JobBench
Gemini 3.6 Flash—
GPT-5.542.7%
Source
Not directly comparable
ExploitGym
Gemini 3.6 Flash—
GPT-5.513.4%
Source
Not directly comparable

Coding

deepSwe
Gemini 3.6 Flash49%
Source
GPT-5.5—
Not directly comparable
cursorBench32
Shared source
Gemini 3.6 Flash53.5%
GPT-5.558.4%
GPT-5.5 leads this result
LiveCodeBench (Vals)
Gemini 3.6 Flash88.1%
Source
GPT-5.585.3%
Source
Gemini 3.6 Flash leads this result
SWE-bench (Vals)
Gemini 3.6 Flash79.6%
Source
GPT-5.582.6%
Source
GPT-5.5 leads this result
SWE-bench Pro
Gemini 3.6 Flash—
GPT-5.558.6%
Source
Not directly comparable
Terminal-Bench 2.0
Gemini 3.6 Flash—
GPT-5.582.0%
Source
Not directly comparable
Vibe Code Bench
Gemini 3.6 Flash—
GPT-5.569.85%
Source
Not directly comparable
React Native Evals
Gemini 3.6 Flash—
GPT-5.584.7%
Source
Not directly comparable
cursorBench31
Gemini 3.6 Flash—
GPT-5.559.2%
Source
Not directly comparable
FrontierCode 1.1 Main
Gemini 3.6 Flash—
GPT-5.543.0%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
Gemini 3.6 Flash—
GPT-5.583.1%
Source
Not directly comparable
MRCR v2 128K-256K
Gemini 3.6 Flash—
GPT-5.587.5%
Source
Not directly comparable
ARC-AGI-2
Gemini 3.6 Flash—
GPT-5.585%
Source
Not directly comparable
ARC-AGI-3
Gemini 3.6 Flash—
GPT-5.50.4%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Gemini 3.6 Flash93.4%
Source
GPT-5.593.2%
Source
Gemini 3.6 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.6 Flash89.3%
Source
GPT-5.588.1%
Source
Gemini 3.6 Flash leads this result
GPQA
Gemini 3.6 Flash—
GPT-5.593.6%
Source
Not directly comparable
GPQA-D
Gemini 3.6 Flash—
GPT-5.593.6%
Source
Not directly comparable
HLE
Gemini 3.6 Flash—
GPT-5.552.2%
Source
Not directly comparable
HLE w/o tools
Gemini 3.6 Flash—
GPT-5.541.4%
Source
Not directly comparable

Math

FrontierMath (legacy)
Gemini 3.6 Flash—
GPT-5.551.7%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Gemini 3.6 Flash—
GPT-5.551.700%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.6 Flash—
GPT-5.535.400%
Source
Not directly comparable

Multimodal

MMMU-Pro
Gemini 3.6 Flash—
GPT-5.581.2%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemini 3.6 Flash—
GPT-5.583.2%
Source
Not directly comparable
OfficeQA Pro
Gemini 3.6 Flash—
GPT-5.554.1%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemini 3.6 Flash or GPT-5.5?

GPT-5.5 has the higher public score estimate, 73.27 versus 70.11, 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 3.6 Flash or GPT-5.5?

GPT-5.5 leads the public coding lane, 67.7 to 58.9, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Gemini 3.6 Flash or GPT-5.5?

GPT-5.5 leads the public agentic tasks lane, 63.9 to 50.7, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Gemini 3.6 Flash or GPT-5.5?

For the stated presets, chat costs $0.00525 on Gemini 3.6 Flash and $0.02 on GPT-5.5; repository review costs $0.0975 and $0.34; the cache-heavy agent loop costs $0.135 and $0.5. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.6 Flash or GPT-5.5?

Both models list the same context window, 1M.

Related comparisons

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Last updated September 4, 2026

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Gemini 3.6 Flash vs GPT-5.5

Which one for your work

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Long documents

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Gemini 3.6 Flash or GPT-5.5?

Which is better for coding, Gemini 3.6 Flash or GPT-5.5?

Which is better for agentic tasks, Gemini 3.6 Flash or GPT-5.5?

Which costs less, Gemini 3.6 Flash or GPT-5.5?

Which has the larger context window, Gemini 3.6 Flash or GPT-5.5?

Related comparisons

Watch Gemini 3.6 Flash vs GPT-5.5