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Model comparison

Gemini 3.1 Pro vs LFM2.5-2.6B

Updated August 4, 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 #89

90% interval 38.8–70.7

LFM2.5-2.6B

LiquidAI

—

Evidence status unavailable

90% interval unavailable

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

1 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. LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B 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
1
Gemini 3.1 Pro only
22
LFM2.5-2.6B only
6
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
LFM2.5-2.6B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.1 Pro
Not measured
LFM2.5-2.6B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.1 Pro
77.1
LFM2.5-2.6B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.1 Pro
Not measured
LFM2.5-2.6B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
31.8
LFM2.5-2.6B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not measured
LFM2.5-2.6B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Pro
82.6
LFM2.5-2.6B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not measured
LFM2.5-2.6B
59.2
Weighted basis
0 vs 1 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
LFM2.5-2.6B
Self-hosted; infrastructure cost varies
Fits in one request

LFM2.5-2.6B 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
LFM2.5-2.6B
Self-hosted; infrastructure cost varies
Fits in one request

LFM2.5-2.6B 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
LFM2.5-2.6B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B 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.

Reasoning profile

Gemini 3.1 Pro

Reasoning

LFM2.5-2.6B

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

LFM2.5-2.6B

Open Weight

License

Gemini 3.1 Pro

Proprietary

LFM2.5-2.6B

Open Weight

Release date

Gemini 3.1 Pro

2026-02-19

LFM2.5-2.6B

2026-08-04

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
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 evidence29 rows

Agentic

  • Claw-Eval

    Gemini 3.1 Pro57.8%
    Source
    LFM2.5-2.6B62.9%
    Source

    LFM2.5-2.6B leads this result

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • Gert Labs

    Gemini 3.1 Pro56.87%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • BFCL v4

    Gemini 3.1 Pro—
    LFM2.5-2.6B56.9%
    Source

    Not directly comparable

  • τ³-bench results

    Gemini 3.1 Pro—
    LFM2.5-2.6B5.7%
    Source

    Not directly comparable

  • PinchBench

    Gemini 3.1 Pro—
    LFM2.5-2.6B68.2%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • React Native Evals

    Gemini 3.1 Pro78.9%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.1 Pro32.03%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • LiveCodeBench v6

    Gemini 3.1 Pro—
    LFM2.5-2.6B59.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    LFM2.5-2.6B—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    LFM2.5-2.6B—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.1 Pro36.900%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.1 Pro16.700%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • AIME 2025

    Gemini 3.1 Pro—
    LFM2.5-2.6B51.9%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    LFM2.5-2.6B—

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    LFM2.5-2.6B—

    Not directly comparable

Instruction following

  • IFBench

    Gemini 3.1 Pro—
    LFM2.5-2.6B59.2%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.1 Pro or LFM2.5-2.6B?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.1 Pro or LFM2.5-2.6B?

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 LFM2.5-2.6B?

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 LFM2.5-2.6B?

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 LFM2.5-2.6B?

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

Related comparisons

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Last updated August 4, 2026

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