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Model comparison

Gemini 3.1 Pro vs A.X series

Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Gemini 3.1 Pro

Google

54.7/100

Estimated · Public rank #87

90% interval 38.8–70.6

A.X series

SK Telecom

Evidence status unavailable

90% interval unavailable

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 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

    Gemini 3.1 Pro

    Gemini 3.1 Pro has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • 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. A.X series does not fit this workload in one request. A.X series has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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
23
A.X series only
0
Like-for-like categories
0 / 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.

Agentic

Not comparable
Gemini 3.1 Pro
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.1 Pro
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.1 Pro
77.1
A.X series
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.1 Pro
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
31.8
A.X series
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Pro
82.6
A.X series
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not measured
A.X series
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.

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
A.X series
API rate not published
Fits in one request

A.X series has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Pro
$0.136
Fits in one request
A.X series
API rate not published
Fits in one request

A.X series has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 3.1 Pro
$0.2
Fits in one request
A.X series
API rate not published
Does not fit in one request
Cached-input rate unavailable

A.X series does not fit this workload in one request. A.X series has no comparable published API token 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 3.1 Pro

$0.2 per 1M cached input tokens

Google Gemini API pricing

A.X series

No comparable hosted API rate

Reasoning profile

Gemini 3.1 Pro

Reasoning

A.X series

Non-Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

A.X series

Proprietary

License

Gemini 3.1 Pro

Proprietary

A.X series

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

A.X series

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
A complete comparable API-rate estimate is not available for both models.
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 evidence23 rows

Agentic

  • Claw-Eval

    Gemini 3.1 Pro57.8%
    Source
    A.X series

    Not directly comparable

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    A.X series

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    A.X series

    Not directly comparable

  • Gert Labs

    Gemini 3.1 Pro56.87%
    Source
    A.X series

    Not directly comparable

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    A.X series

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    A.X series

    Not directly comparable

  • React Native Evals

    Gemini 3.1 Pro78.9%
    Source
    A.X series

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.1 Pro32.03%
    Source
    A.X series

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    A.X series

    Not directly comparable

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    A.X series

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    A.X series

    Not directly comparable

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    A.X series

    Not directly comparable

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    A.X series

    Not directly comparable

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    A.X series

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.1 Pro36.900%
    Source
    A.X series

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.1 Pro16.700%
    Source
    A.X series

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    A.X series

    Not directly comparable

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    A.X series

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    A.X series

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    A.X series

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    A.X series

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    A.X series

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    A.X series

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.1 Pro or A.X series?

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 A.X series?

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 3.1 Pro or A.X series?

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 3.1 Pro or A.X series?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Gemini 3.1 Pro or A.X series?

Gemini 3.1 Pro has the larger documented context window: 1M, compared with 64K.

Related comparisons

Last updated July 29, 2026

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