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BenchLM

LFM2.5-2.6B vs MiMo-V2-Omni

Decision reading

MiMo-V2-Omni has the higher public score estimate, 57.53 versus 39.9, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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

LiquidAI logo
Model A
LFM2.5-2.6B

LiquidAI

39.9/100

Estimated · Public rank #190

90% interval 28.4–51.4

Xiaomi logo
Model B
MiMo-V2-Omni

Xiaomi

57.53/100

Supported · Public rank #79

90% interval 43.6–71.5

Updated September 18, 2026. Rank says MiMo-V2-Omni is ahead. Price, access, and your workload can each overturn that. 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

    MiMo-V2-Omni

    MiMo-V2-Omni 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

    MiMo-V2-Omni 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

    MiMo-V2-Omni 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. LFM2.5-2.6B does not fit this workload in one request. LFM2.5-2.6B has no comparable published API token rate. MiMo-V2-Omni 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

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.

39.2LFM2.5-2.6B—MiMo-V2-Omni

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
1
LFM2.5-2.6B only
6
MiMo-V2-Omni only
1
Like-for-like categories
0 / 8

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

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.

Knowledge

Directional only
LFM2.5-2.6B
37.0
Estimated · #151/184
MiMo-V2-Omni
49.4
Estimated · #86/184
Basis
BenchAlign lane · 0 vs 0 public rows
Reading
Directional only

Instruction following

Directional only
LFM2.5-2.6B
38.1
#104/124
MiMo-V2-Omni
62.6
#72/124
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
LFM2.5-2.6B
37.2
Estimated · #131/154
MiMo-V2-Omni
Not ranked
Basis
BenchAlign lane · 4 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-2.6B
39.2
Estimated · #125/154
MiMo-V2-Omni
Not ranked
Basis
BenchAlign lane · 1 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-2.6B
24.9
Unranked · 2 rankable rows
MiMo-V2-Omni
72.6
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
LFM2.5-2.6B
Not ranked
MiMo-V2-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-2.6B
Not ranked
MiMo-V2-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-2.6B
Not ranked
MiMo-V2-Omni
63.1
Unranked · 1 rankable row
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

LFM2.5-2.6B
Self-hosted; infrastructure cost varies
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

LFM2.5-2.6B has no comparable published API token rate. MiMo-V2-Omni has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-2.6B
Self-hosted; infrastructure cost varies
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

LFM2.5-2.6B has no comparable published API token rate. MiMo-V2-Omni has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-2.6B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
MiMo-V2-Omni
API rate not published
Fits 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. MiMo-V2-Omni 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.

API model ID

LFM2.5-2.6B

Not sourced

MiMo-V2-Omni

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

LFM2.5-2.6B

No comparable hosted API rate

LiquidAI Hugging Face model card

MiMo-V2-Omni

No comparable hosted API rate

Documented inputs

LFM2.5-2.6B

Not sourced

MiMo-V2-Omni

Not sourced

Documented outputs

LFM2.5-2.6B

Not sourced

MiMo-V2-Omni

Not sourced

Provider availability

LFM2.5-2.6B

Not sourced

MiMo-V2-Omni

Not sourced

Reasoning profile

LFM2.5-2.6B

Reasoning

MiMo-V2-Omni

Reasoning

Weight access

LFM2.5-2.6B

Open Weight

MiMo-V2-Omni

Proprietary

License

LFM2.5-2.6B

Open Weight

MiMo-V2-Omni

Proprietary

Release date

LFM2.5-2.6B

2026-08-04

MiMo-V2-Omni

2026-03-18

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
MiMo-V2-Omni has the higher public score estimate, 57.53 versus 39.9, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
MiMo-V2-Omni has the larger documented window (262K).

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 evidence8 rows

Agentic

  • BFCL v4

    LFM2.5-2.6B56.9%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • τ³-bench results

    LFM2.5-2.6B5.7%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • Claw-Eval

    LFM2.5-2.6B62.9%
    Source
    MiMo-V2-Omni45.2%
    Source

    LFM2.5-2.6B leads this result

  • PinchBench

    LFM2.5-2.6B68.2%
    Source
    MiMo-V2-Omni—

    Not directly comparable

Coding

  • LiveCodeBench v6

    LFM2.5-2.6B59.4%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • SWE-bench Verified

    LFM2.5-2.6B—
    MiMo-V2-Omni74.8%
    Source

    Not directly comparable

Math

  • AIME 2025

    LFM2.5-2.6B51.9%
    Source
    MiMo-V2-Omni—

    Not directly comparable

Instruction following

  • IFBench

    LFM2.5-2.6B59.2%
    Source
    MiMo-V2-Omni—

    Not directly comparable

Questions

Which is better, LFM2.5-2.6B or MiMo-V2-Omni?

MiMo-V2-Omni has the higher public score estimate, 57.53 versus 39.9, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, LFM2.5-2.6B or MiMo-V2-Omni?

MiMo-V2-Omni is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LFM2.5-2.6B or MiMo-V2-Omni?

MiMo-V2-Omni is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, LFM2.5-2.6B or MiMo-V2-Omni?

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, LFM2.5-2.6B or MiMo-V2-Omni?

MiMo-V2-Omni has the larger documented context window: 262K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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