Skip to main content
BenchLM
Data

Gemini 3.1 Pro vs GPT-5 mini

Updated October 17, 2026. Rank says Gemini 3.1 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInAPI/MCP

Decision reading

Gemini 3.1 Pro has the higher public point estimate, 64.7 versus 48.39. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemini 3.1 Pro

Google

64.7/100

Estimated · Public rank #37

Conditional range 55.0–74.4

Model B
OpenAI logo
GPT-5 mini

OpenAI

48.39/100

Supported · Public rank #104

90% interval 37.0–59.8

Shared results
1
Gemini 3.1 Pro only
29
GPT-5 mini only
0
Like-for-like categories
0 / 8
Estimated: Gemini 3.1 Pro · Supported: GPT-5 mini. Conditional ranges do not establish rank confidence.How 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.

  • 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-5 mini

    GPT-5 mini 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-5 mini

    GPT-5 mini 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-5 mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    GPT-5 mini is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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-5 mini does not fit this workload in one request. GPT-5 mini 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.

39.2Gemini 3.1 Pro26.8GPT-5 mini

Directional only · BenchAlign v5.8

Gemini 3.1 Pro 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 leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

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

Directional only
Gemini 3.1 Pro
38.4
Supported · #70/128
GPT-5 mini
27.7
Estimated · #99/128
Basis
BenchAlign v5.8 lane · 7 vs 0 public rows
Reading
Directional only

Coding

Directional only
Gemini 3.1 Pro
39.2
Supported · #67/150
GPT-5 mini
26.8
Estimated · #103/150
Basis
BenchAlign v5.8 lane · 6 vs 1 public rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.1 Pro
58.2
Unranked · 2 rankable rows
GPT-5 mini
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Pro
80.1
#17/54
GPT-5 mini
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.1 Pro
67.6
Supported · #26/184
GPT-5 mini
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not ranked
GPT-5 mini
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 mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
65.0
Unranked · 2 rankable rows
GPT-5 mini
Not ranked
Basis
Provisional lane · 2 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 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 3.1 Pro
$0.008
Fits in one request
GPT-5 mini
$0.00125
Fits in one request

GPT-5 mini 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 mini
$0.0185
Fits in one request

GPT-5 mini 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 mini
$0.075
Does not fit in one request
Cached input priced at the published list-input rate

GPT-5 mini does not fit this workload in one request. GPT-5 mini 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.

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 mini

Not published

Reasoning profile

Gemini 3.1 Pro

Reasoning

GPT-5 mini

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

GPT-5 mini

Proprietary

License

Gemini 3.1 Pro

Proprietary

GPT-5 mini

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

GPT-5 mini

2025-08-07

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 3.1 Pro has the higher public point estimate, 64.7 versus 48.39. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
Repository review: $0.136 vs $0.0185. Cache-heavy agent loop: $0.2 vs $0.075.
Context tradeoff
Gemini 3.1 Pro has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 3.1 Pro or GPT-5 mini?

Gemini 3.1 Pro has the higher public point estimate, 64.7 versus 48.39. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.1 Pro or GPT-5 mini?

Gemini 3.1 Pro scores higher for coding on the public lane, 39.2 to 26.8. GPT-5 mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemini 3.1 Pro or GPT-5 mini?

Gemini 3.1 Pro scores higher for agentic tasks on the public lane, 38.4 to 27.7. GPT-5 mini is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Gemini 3.1 Pro or GPT-5 mini?

For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.00125 on GPT-5 mini; repository review costs $0.136 and $0.0185; the cache-heavy agent loop costs $0.2 and $0.075. GPT-5 mini does not fit this workload in one request. GPT-5 mini 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-5 mini?

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

Benchmark evidence

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

Browse raw public benchmark evidence30 rows

Agentic

  • Claw-Eval

    Gemini 3.1 Pro57.8%
    Source
    GPT-5 mini—

    Not directly comparable

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    GPT-5 mini—

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    GPT-5 mini—

    Not directly comparable

  • Gert Labs

    Gemini 3.1 Pro56.87%
    Source
    GPT-5 mini—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    GPT-5 mini—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.1 Pro70.8%
    Source
    GPT-5 mini—

    Not directly comparable

  • MazeBench

    Gemini 3.1 Pro1%
    Source
    GPT-5 mini—

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    GPT-5 mini—

    Not directly comparable

  • React Native Evals

    Gemini 3.1 Pro78.9%
    Source
    GPT-5 mini—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    Gemini 3.1 Pro32.03%
    GPT-5 mini14.17%

    Gemini 3.1 Pro leads this result

  • LiveCodeBench (Vals)

    Gemini 3.1 Pro88.5%
    Source
    GPT-5 mini—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.1 Pro78.8%
    Source
    GPT-5 mini—

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 3.1 Pro22.0%
    Source
    GPT-5 mini—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    GPT-5 mini—

    Not directly comparable

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    GPT-5 mini—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    GPT-5 mini—

    Not directly comparable

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    GPT-5 mini—

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    GPT-5 mini—

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    GPT-5 mini—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    GPT-5 mini—

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    GPT-5 mini—

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    GPT-5 mini—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    GPT-5 mini—

    Not directly comparable

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    GPT-5 mini—

    Not directly comparable

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    GPT-5 mini—

    Not directly comparable

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    GPT-5 mini—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 3.1 Pro95.5%
    Source
    GPT-5 mini—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.1 Pro91.0%
    Source
    GPT-5 mini—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.1 Pro59.649%
    Source
    GPT-5 mini—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.1 Pro26.829%
    Source
    GPT-5 mini—

    Not directly comparable

30 public results · 1 shared

Related comparisons

Compare API pricingRead the methodologyOpen the model selector

Watch Gemini 3.1 Pro vs GPT-5 mini

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 5,600+ readers.

Last updated October 17, 2026