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BenchLM
Model A
Gemini 3.5 Flash

Google

64.7/100

Estimated · Public rank #39

90% interval 54.1–75.2

Gemini 3.5 Flash vs GPT-5.5 Pro

Updated August 17, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
GPT-5.5 Pro

OpenAI

64.2/100

Estimated · Public rank #41

90% interval 49.5–78.8

Decision reading

Gemini 3.5 Flash has the higher public score estimate, 64.67 versus 64.17, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.5 Pro

    GPT-5.5 Pro has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Gemini 3.5 Flash

    Gemini 3.5 Flash 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

    Gemini 3.5 Flash has the lower estimated token cost for this stated workload. GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Gemini 3.5 Flash

    Gemini 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

What is actually comparable

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

Shared results
3
Gemini 3.5 Flash only
20
GPT-5.5 Pro only
3
Like-for-like categories
2 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Like-for-like
Gemini 3.5 Flash
40.2
GPT-5.5 Pro
57.2
Weighted basis
1 vs 1 rows
Reading
GPT-5.5 Pro leads

Math

Like-for-like
Gemini 3.5 Flash
32.9
GPT-5.5 Pro
48.1
Weighted basis
2 vs 2 rows
Reading
GPT-5.5 Pro leads

Agentic

Not comparable
Gemini 3.5 Flash
77.2
GPT-5.5 Pro
90.1
Weighted basis
2 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.5 Flash
55.1
GPT-5.5 Pro
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.5 Flash
74.7
GPT-5.5 Pro
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.5 Flash
Not measured
GPT-5.5 Pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.5 Flash
83.8
GPT-5.5 Pro
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.5 Flash
Not measured
GPT-5.5 Pro
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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
$0.006
Fits in one request
GPT-5.5 Pro
$0.12
Fits in one request

Gemini 3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash
$0.102
Fits in one request
GPT-5.5 Pro
$2.04
Fits in one request

Gemini 3.5 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 3.5 Flash
$0.15
Fits in one request
GPT-5.5 Pro
$8.40
Fits in one request
Cached input priced at the published list-input rate

Gemini 3.5 Flash has the lower modeled cost

GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

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

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

$0.15 per 1M cached input tokens

Google Gemini API pricing

GPT-5.5 Pro

Not published

OpenAI pricing

Provider availability

Gemini 3.5 Flash

Not sourced

GPT-5.5 Pro

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Gemini 3.5 Flash

Reasoning

GPT-5.5 Pro

Reasoning

Weight access

Gemini 3.5 Flash

Proprietary

GPT-5.5 Pro

Proprietary

License

Gemini 3.5 Flash

Proprietary

GPT-5.5 Pro

Proprietary

Release date

Gemini 3.5 Flash

2026-05-19

GPT-5.5 Pro

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
Gemini 3.5 Flash has the higher public score estimate, 64.67 versus 64.17, but the 90% score intervals overlap.
Workload cost
Repository review: $0.102 vs $2.04. Cache-heavy agent loop: $0.15 vs $8.40.
Context tradeoff
GPT-5.5 Pro has the larger documented window (1.05M).

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 evidence26 rows

Agentic

  • Terminal-Bench 2.0

    Gemini 3.5 Flash76.2%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • MCP Atlas

    Gemini 3.5 Flash83.6%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • Toolathlon

    Gemini 3.5 Flash56.5%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • OSWorld-Verified

    Gemini 3.5 Flash78.4%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • Finance Agent v2

    Gemini 3.5 Flash57.9%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • Gert Labs

    Gemini 3.5 Flash61.85%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.5 Flash18.0%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • BrowseComp

    Gemini 3.5 Flash—
    GPT-5.5 Pro90.1%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    Gemini 3.5 Flash76.2%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.5 Flash55.1%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.5 Flash48.68%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • cursorBench31

    Gemini 3.5 Flash49.8%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • cursorBench32

    Gemini 3.5 Flash48.8%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • EEBench

    Gemini 3.5 Flash34.3%
    Source
    GPT-5.5 Pro—

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 3.5 Flash77.3%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • MRCR 1M

    Gemini 3.5 Flash26.6%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • ARC-AGI-2

    Gemini 3.5 Flash72.1%
    Source
    GPT-5.5 Pro—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.5 Flash92.7%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • HLE

    Gemini 3.5 Flash40.2%
    Source
    GPT-5.5 Pro57.2%
    Source

    GPT-5.5 Pro leads this result

  • HLE w/o tools

    Gemini 3.5 Flash—
    GPT-5.5 Pro43.1%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 3.5 Flash38.966%
    GPT-5.5 Pro51.000%

    GPT-5.5 Pro leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Gemini 3.5 Flash14.583%
    GPT-5.5 Pro39.600%

    GPT-5.5 Pro leads this result

  • FrontierMath (legacy)

    Gemini 3.5 Flash—
    GPT-5.5 Pro52.4%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Gemini 3.5 Flash84.2%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • MMMU-Pro

    Gemini 3.5 Flash83.6%
    Source
    GPT-5.5 Pro—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 3.5 Flash33.6%
    Source
    GPT-5.5 Pro—

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.5 Flash or GPT-5.5 Pro?

Gemini 3.5 Flash has the higher public score estimate, 64.67 versus 64.17, 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 or GPT-5.5 Pro?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Gemini 3.5 Flash or GPT-5.5 Pro?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Gemini 3.5 Flash or GPT-5.5 Pro?

For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.12 on GPT-5.5 Pro; repository review costs $0.102 and $2.04; the cache-heavy agent loop costs $0.15 and $8.40. GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 3.5 Flash or GPT-5.5 Pro?

GPT-5.5 Pro has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 17, 2026

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