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BenchLM
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Model A
Gemini 3.1 Pro

Google

69.95/100

Supported · Public rank #19

90% interval 63.0–76.9

Gemini 3.1 Pro vs GPT-5.5

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

OpenAI logo
Model B
GPT-5.5

OpenAI

72.07/100

Supported · Public rank #10

90% interval 68.5–75.7

Decision reading

GPT-5.5 has the higher public score estimate, 72.07 versus 69.95, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

17 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, 67.6 to 46.3, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Agentic work

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

    GPT-5.5

    GPT-5.5 leads on the public agentic lane, 61 to 38.9, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Chat turn cost

    1K fresh input + 500 output tokens

    Gemini 3.1 Pro

    Gemini 3.1 Pro 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 3.1 Pro

    Gemini 3.1 Pro 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 3.1 Pro

    Gemini 3.1 Pro 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.

Shared results
17
Gemini 3.1 Pro only
11
GPT-5.5 only
22
Like-for-like categories
3 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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 3.1 Pro
38.9
Supported · #121/152
GPT-5.5
61.0
Supported · #17/152
Basis
BenchAlign lane · 6 vs 14 public rows
Reading
GPT-5.5 leads

Coding

Like-for-like
Gemini 3.1 Pro
46.3
Supported · #82/151
GPT-5.5
67.6
Supported · #7/151
Basis
BenchAlign lane · 5 vs 9 public rows
Reading
GPT-5.5 leads

Knowledge

Like-for-like
Gemini 3.1 Pro
66.1
Supported · #21/183
GPT-5.5
72.9
Supported · #7/183
Basis
BenchAlign lane · 6 vs 6 public rows
Reading
GPT-5.5 leads · intervals overlap

Multimodal

Directional only
Gemini 3.1 Pro
79.2
#12/48
GPT-5.5
71.3
#19/48
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.1 Pro
50.6
Unranked · 2 rankable rows
GPT-5.5
64.0
#13/20
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
54.3
Unranked · 2 rankable rows
GPT-5.5
69.6
Unranked · 3 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not ranked
GPT-5.5
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not ranked
GPT-5.5
93.2
#7/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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.

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 3.1 Pro
$0.008
Fits in one request
GPT-5.5
$0.02
Fits in one request

Gemini 3.1 Pro has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Pro
$0.136
Fits in one request
GPT-5.5
$0.34
Fits in one request

Gemini 3.1 Pro 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 3.1 Pro
$0.2
Fits in one request
GPT-5.5
$0.5
Fits in one request

Gemini 3.1 Pro 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.

Cached-input rate

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

Gemini 3.1 Pro

$0.2 per 1M cached input tokens

Google Gemini API pricing

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

Reasoning profile

Gemini 3.1 Pro

Reasoning

GPT-5.5

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

GPT-5.5

Proprietary

License

Gemini 3.1 Pro

Proprietary

GPT-5.5

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

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, 72.07 versus 69.95, but the 90% score intervals overlap.
Workload cost
Repository review: $0.136 vs $0.34. Cache-heavy agent loop: $0.2 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 evidence50 rows

Agentic

  • Claw-Eval

    Gemini 3.1 Pro57.8%
    Source
    GPT-5.5—

    Not directly comparable

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    GPT-5.5—

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    GPT-5.598%
    Source

    GPT-5.5 leads this result

  • Gert Labs

    Shared source
    Gemini 3.1 Pro56.87%
    GPT-5.572.93%

    GPT-5.5 leads this result

  • ResearchClawBench

    Shared source
    Gemini 3.1 Pro13.3%
    GPT-5.517.0%

    GPT-5.5 leads this result

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.1 Pro70.8%
    Source
    GPT-5.576.4%
    Source

    GPT-5.5 leads this result

  • Terminal-Bench 2.0

    Gemini 3.1 Pro—
    GPT-5.582%
    Source

    Not directly comparable

  • CyberGym

    Gemini 3.1 Pro—
    GPT-5.581.8%
    Source

    Not directly comparable

  • BrowseComp

    Gemini 3.1 Pro—
    GPT-5.584.4%
    Source

    Not directly comparable

  • OSWorld-Verified

    Gemini 3.1 Pro—
    GPT-5.578.7%
    Source

    Not directly comparable

  • MCP Atlas

    Gemini 3.1 Pro—
    GPT-5.575.3%
    Source

    Not directly comparable

  • Toolathlon

    Gemini 3.1 Pro—
    GPT-5.555.6%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemini 3.1 Pro—
    GPT-5.513.0%
    Source

    Not directly comparable

  • JobBench

    Gemini 3.1 Pro—
    GPT-5.542.7%
    Source

    Not directly comparable

  • ExploitGym

    Gemini 3.1 Pro—
    GPT-5.513.4%
    Source

    Not directly comparable

  • ApprenticeBench

    Gemini 3.1 Pro—
    GPT-5.520%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    GPT-5.5—

    Not directly comparable

  • React Native Evals

    Shared source
    Gemini 3.1 Pro78.9%
    GPT-5.584.7%

    GPT-5.5 leads this result

  • Vibe Code Bench

    Shared source
    Gemini 3.1 Pro32.03%
    GPT-5.569.85%

    GPT-5.5 leads this result

  • LiveCodeBench (Vals)

    Gemini 3.1 Pro88.5%
    Source
    GPT-5.585.3%
    Source

    Gemini 3.1 Pro leads this result

  • SWE-bench (Vals)

    Gemini 3.1 Pro78.8%
    Source
    GPT-5.582.6%
    Source

    GPT-5.5 leads this result

  • SWE-bench Pro

    Gemini 3.1 Pro—
    GPT-5.558.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Gemini 3.1 Pro—
    GPT-5.582.0%
    Source

    Not directly comparable

  • cursorBench31

    Gemini 3.1 Pro—
    GPT-5.559.2%
    Source

    Not directly comparable

  • cursorBench32

    Gemini 3.1 Pro—
    GPT-5.558.4%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    Gemini 3.1 Pro—
    GPT-5.543.0%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    GPT-5.585%
    Source

    GPT-5.5 leads this result

  • ARC-AGI-3

    Shared source
    Gemini 3.1 Pro0.4%
    GPT-5.50.4%

    GPT-5.5 leads this result

  • MRCR v2 64K-128K

    Gemini 3.1 Pro—
    GPT-5.583.1%
    Source

    Not directly comparable

  • MRCR v2 128K-256K

    Gemini 3.1 Pro—
    GPT-5.587.5%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    GPT-5.593.6%
    Source

    Gemini 3.1 Pro leads this result

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    GPT-5.541.4%
    Source

    Gemini 3.1 Pro leads this result

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    GPT-5.5—

    Not directly comparable

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    GPT-5.5—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 3.1 Pro95.5%
    Source
    GPT-5.593.2%
    Source

    Gemini 3.1 Pro leads this result

  • MMLU-Pro (Vals)

    Gemini 3.1 Pro91.0%
    Source
    GPT-5.588.1%
    Source

    Gemini 3.1 Pro leads this result

  • GPQA

    Gemini 3.1 Pro—
    GPT-5.593.6%
    Source

    Not directly comparable

  • HLE

    Gemini 3.1 Pro—
    GPT-5.552.2%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 3.1 Pro36.900%
    GPT-5.551.700%

    GPT-5.5 leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Gemini 3.1 Pro16.700%
    GPT-5.535.400%

    GPT-5.5 leads this result

  • FrontierMath (legacy)

    Gemini 3.1 Pro—
    GPT-5.551.7%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    GPT-5.581.2%
    Source

    Gemini 3.1 Pro leads this result

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    GPT-5.5—

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    GPT-5.5—

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    GPT-5.5—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    GPT-5.5—

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    GPT-5.5—

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    GPT-5.5—

    Not directly comparable

  • MMMU-Pro w/ Python

    Gemini 3.1 Pro—
    GPT-5.583.2%
    Source

    Not directly comparable

  • OfficeQA Pro

    Gemini 3.1 Pro—
    GPT-5.554.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.1 Pro or GPT-5.5?

GPT-5.5 has the higher public score estimate, 72.07 versus 69.95, 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 3.1 Pro or GPT-5.5?

GPT-5.5 leads the public coding lane, 67.6 to 46.3, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Gemini 3.1 Pro or GPT-5.5?

GPT-5.5 leads the public agentic tasks lane, 61 to 38.9, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Gemini 3.1 Pro or GPT-5.5?

For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.02 on GPT-5.5; repository review costs $0.136 and $0.34; the cache-heavy agent loop costs $0.2 and $0.5. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.1 Pro or GPT-5.5?

Both models list the same context window, 1M.

Related comparisons

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Last updated September 10, 2026

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