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BenchLM
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Model A
LFM2.5-VL-3B

LiquidAI

—

Evidence status unavailable

90% interval unavailable

LFM2.5-VL-3B vs Mellum2-12B-A2.5B-Instruct

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

JetBrains logo
Model B
Mellum2-12B-A2.5B-Instruct

JetBrains

—

Evidence status unavailable

90% interval unavailable

Decision reading

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

2 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.

  • Long documents

    Prompts that approach the documented context limit

    Mellum2-12B-A2.5B-Instruct

    Mellum2-12B-A2.5B-Instruct 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

    LFM2.5-VL-3B and Mellum2-12B-A2.5B-Instruct are 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

    LFM2.5-VL-3B is not ranked on the public lane for agentic, so no winner is named for agentic.

    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-VL-3B does not fit this workload in one request. Mellum2-12B-A2.5B-Instruct does not fit this workload in one request. LFM2.5-VL-3B has no comparable published API token rate. Mellum2-12B-A2.5B-Instruct has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K 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-VL-3B does not fit this workload in one request. LFM2.5-VL-3B has no comparable published API token rate. Mellum2-12B-A2.5B-Instruct has no comparable published API token rate.

    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
2
LFM2.5-VL-3B only
8
Mellum2-12B-A2.5B-Instruct only
4
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
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
45.3
Estimated · #83/152
Basis
BenchAlign lane · 1 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
BenchAlign lane · 0 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
45.6
Estimated · #106/183
Basis
BenchAlign lane · 0 vs 3 public rows
Reading
Not comparable

Math

Not comparable
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
LFM2.5-VL-3B
Not ranked
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 1 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

LFM2.5-VL-3B
Self-hosted; infrastructure cost varies
Fits in one request
Mellum2-12B-A2.5B-Instruct
API rate not published
Fits in one request

LFM2.5-VL-3B has no comparable published API token rate. Mellum2-12B-A2.5B-Instruct has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-VL-3B
Self-hosted; infrastructure cost varies
Does not fit in one request
Mellum2-12B-A2.5B-Instruct
API rate not published
Fits in one request

LFM2.5-VL-3B does not fit this workload in one request. LFM2.5-VL-3B has no comparable published API token rate. Mellum2-12B-A2.5B-Instruct has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-VL-3B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Mellum2-12B-A2.5B-Instruct
API rate not published
Does not fit in one request
Cached-input rate unavailable

LFM2.5-VL-3B does not fit this workload in one request. Mellum2-12B-A2.5B-Instruct does not fit this workload in one request. LFM2.5-VL-3B has no comparable published API token rate. Mellum2-12B-A2.5B-Instruct 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.

LFM2.5-VL-3B

No comparable hosted API rate

Liquid AI LFM2.5-VL-3B model card

Mellum2-12B-A2.5B-Instruct

No comparable hosted API rate

Documented inputs

LFM2.5-VL-3B

Not sourced

Mellum2-12B-A2.5B-Instruct

Not sourced

Documented outputs

LFM2.5-VL-3B

Not sourced

Mellum2-12B-A2.5B-Instruct

Not sourced

Provider availability

LFM2.5-VL-3B

Not sourced

Mellum2-12B-A2.5B-Instruct

Not sourced

Reasoning profile

LFM2.5-VL-3B

Non-Reasoning

Mellum2-12B-A2.5B-Instruct

Non-Reasoning

Weight access

LFM2.5-VL-3B

Open Weight

Mellum2-12B-A2.5B-Instruct

Open Weight

License

LFM2.5-VL-3B

Open Weight

Mellum2-12B-A2.5B-Instruct

Open Weight

Release date

LFM2.5-VL-3B

2026-08-12

Mellum2-12B-A2.5B-Instruct

2026-05-28

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
Mellum2-12B-A2.5B-Instruct has the larger documented window (128K).

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 evidence14 rows

Agentic

  • BFCL v4

    LFM2.5-VL-3B32.5%
    Source
    Mellum2-12B-A2.5B-Instruct44.2%
    Source

    Mellum2-12B-A2.5B-Instruct leads this result

Coding

  • LiveCodeBench v6

    LFM2.5-VL-3B—
    Mellum2-12B-A2.5B-Instruct37.2%
    Source

    Not directly comparable

Knowledge

  • MMLU-Redux

    LFM2.5-VL-3B—
    Mellum2-12B-A2.5B-Instruct78.1%
    Source

    Not directly comparable

  • GPQA

    LFM2.5-VL-3B—
    Mellum2-12B-A2.5B-Instruct40.9%
    Source

    Not directly comparable

  • GPQA-D

    LFM2.5-VL-3B—
    Mellum2-12B-A2.5B-Instruct40.9%
    Source

    Not directly comparable

Multimodal

  • RealWorldQA

    LFM2.5-VL-3B73.1%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • SimpleVQA

    LFM2.5-VL-3B35.4%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • CountBench

    LFM2.5-VL-3B87.3%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • MMMU

    LFM2.5-VL-3B48.4%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • MMMU-Pro

    LFM2.5-VL-3B30.5%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • OCRBench V2

    LFM2.5-VL-3B47.5%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • RefCOCO (avg)

    LFM2.5-VL-3B87.9%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

Instruction following

  • IFEval

    LFM2.5-VL-3B82.3%
    Source
    Mellum2-12B-A2.5B-Instruct75.8%
    Source

    LFM2.5-VL-3B leads this result

  • IFBench

    LFM2.5-VL-3B25.8%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

Frequently asked questions

Which is better, LFM2.5-VL-3B or Mellum2-12B-A2.5B-Instruct?

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, LFM2.5-VL-3B or Mellum2-12B-A2.5B-Instruct?

LFM2.5-VL-3B and Mellum2-12B-A2.5B-Instruct are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LFM2.5-VL-3B or Mellum2-12B-A2.5B-Instruct?

LFM2.5-VL-3B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, LFM2.5-VL-3B or Mellum2-12B-A2.5B-Instruct?

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, LFM2.5-VL-3B or Mellum2-12B-A2.5B-Instruct?

Mellum2-12B-A2.5B-Instruct has the larger documented context window: 128K, compared with 32K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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