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BenchLM

Gemini 3.1 Pro vs GPT-4o mini Audio

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

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

Google logo
Model A
Gemini 3.1 Pro

Google

70.14/100

Supported · Public rank #18

90% interval 63.4–76.9

OpenAI logo
Model B
GPT-4o mini Audio

OpenAI

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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.

  • Long documents

    Prompts that approach the documented context limit

    Gemini 3.1 Pro

    Gemini 3.1 Pro has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-4o mini Audio

    GPT-4o mini Audio 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

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-4o mini Audio

    GPT-4o mini Audio 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

    GPT-4o mini Audio is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    GPT-4o mini Audio is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-4o mini Audio does not fit this workload in one request. GPT-4o mini Audio has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

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

46.1Gemini 3.1 Pro—GPT-4o mini Audio

Not comparable · BenchAlign

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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

Open the coding leaderboardShortlist models for this job

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
0
Gemini 3.1 Pro only
28
GPT-4o mini Audio only
0
Like-for-like categories
0 / 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

Not comparable
Gemini 3.1 Pro
39.7
Supported · #121/154
GPT-4o mini Audio
Not ranked
Basis
BenchAlign lane · 6 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.1 Pro
46.1
Supported · #88/154
GPT-4o mini Audio
Not ranked
Basis
BenchAlign lane · 5 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.1 Pro
50.7
Unranked · 2 rankable rows
GPT-4o mini Audio
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.1 Pro
66.0
Supported · #22/184
GPT-4o mini Audio
Not ranked
Basis
BenchAlign lane · 6 vs 0 public rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
54.2
Unranked · 2 rankable rows
GPT-4o mini Audio
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not ranked
GPT-4o mini Audio
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Pro
79.1
#12/48
GPT-4o mini Audio
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not ranked
GPT-4o mini Audio
Not ranked
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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-4o mini Audio
$0.00045
Fits in one request

GPT-4o mini Audio 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-4o mini Audio
$0.0093
Fits in one request

GPT-4o mini Audio 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-4o mini Audio
$0.039
Does not fit in one request
Cached input priced at the published list-input rate

GPT-4o mini Audio does not fit this workload in one request. GPT-4o mini Audio 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.

Reasoning profile

Gemini 3.1 Pro

Reasoning

GPT-4o mini Audio

Non-Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

GPT-4o mini Audio

Proprietary

License

Gemini 3.1 Pro

Proprietary

GPT-4o mini Audio

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

GPT-4o mini Audio

Not sourced

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.136 vs $0.0093. Cache-heavy agent loop: $0.2 vs $0.039.
Context tradeoff
Gemini 3.1 Pro has the larger documented window (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 evidence28 rows

Agentic

  • Claw-Eval

    Gemini 3.1 Pro57.8%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • Gert Labs

    Gemini 3.1 Pro56.87%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.1 Pro70.8%
    Source
    GPT-4o mini Audio—

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • React Native Evals

    Gemini 3.1 Pro78.9%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.1 Pro32.03%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.1 Pro88.5%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.1 Pro78.8%
    Source
    GPT-4o mini Audio—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    GPT-4o mini Audio—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 3.1 Pro95.5%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.1 Pro91.0%
    Source
    GPT-4o mini Audio—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.1 Pro36.900%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.1 Pro16.700%
    Source
    GPT-4o mini Audio—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    GPT-4o mini Audio—

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    GPT-4o mini Audio—

    Not directly comparable

Questions

Which is better, Gemini 3.1 Pro or GPT-4o mini Audio?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.1 Pro or GPT-4o mini Audio?

GPT-4o mini Audio is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemini 3.1 Pro or GPT-4o mini Audio?

GPT-4o mini Audio is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 3.1 Pro or GPT-4o mini Audio?

For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.00045 on GPT-4o mini Audio; repository review costs $0.136 and $0.0093; the cache-heavy agent loop costs $0.2 and $0.039. GPT-4o mini Audio does not fit this workload in one request. GPT-4o mini Audio has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 3.1 Pro or GPT-4o mini Audio?

Gemini 3.1 Pro has the larger documented context window: 1M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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