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Model comparison

LFM2.5-2.6B vs Ling 3.0 Flash

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

LFM2.5-2.6B

LiquidAI

—

Evidence status unavailable

90% interval unavailable

Ling 3.0 Flash

InclusionAI

49.9/100

Estimated · Public rank #118

90% interval 40.0–59.8

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 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

    Ling 3.0 Flash

    Ling 3.0 Flash 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. Ling 3.0 Flash 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
2
LFM2.5-2.6B only
5
Ling 3.0 Flash only
15
Like-for-like categories
1 / 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.

Instruction following

Like-for-like
LFM2.5-2.6B
59.2
Ling 3.0 Flash
74.5
Weighted basis
1 vs 1 rows
Reading
Ling 3.0 Flash leads

Agentic

Not comparable
LFM2.5-2.6B
Not measured
Ling 3.0 Flash
72.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-2.6B
Not measured
Ling 3.0 Flash
47.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-2.6B
Not measured
Ling 3.0 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
LFM2.5-2.6B
Not measured
Ling 3.0 Flash
31.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
LFM2.5-2.6B
Not measured
Ling 3.0 Flash
90.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-2.6B
Not measured
Ling 3.0 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-2.6B
Not measured
Ling 3.0 Flash
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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
Ling 3.0 Flash
API rate not published
Fits in one request

LFM2.5-2.6B has no comparable published API token rate. Ling 3.0 Flash 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
Ling 3.0 Flash
API rate not published
Fits in one request

LFM2.5-2.6B has no comparable published API token rate. Ling 3.0 Flash 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
Ling 3.0 Flash
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. Ling 3.0 Flash 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

Ling 3.0 Flash

Not sourced

Documented inputs

LFM2.5-2.6B

Not sourced

Ling 3.0 Flash

Not sourced

Documented outputs

LFM2.5-2.6B

Not sourced

Ling 3.0 Flash

Not sourced

Provider availability

LFM2.5-2.6B

Not sourced

Ling 3.0 Flash

Not sourced

Reasoning profile

LFM2.5-2.6B

Reasoning

Ling 3.0 Flash

Reasoning

Weight access

LFM2.5-2.6B

Open Weight

Ling 3.0 Flash

Open Weight

License

LFM2.5-2.6B

Open Weight

Ling 3.0 Flash

Open Weight

Release date

LFM2.5-2.6B

2026-08-04

Ling 3.0 Flash

2026-07-23

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
Ling 3.0 Flash 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 evidence22 rows

Agentic

  • BFCL v4

    LFM2.5-2.6B56.9%
    Source
    Ling 3.0 Flash73.0%
    Source

    Ling 3.0 Flash leads this result

  • τ³-bench results

    LFM2.5-2.6B5.7%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • Claw-Eval

    LFM2.5-2.6B62.9%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • PinchBench

    LFM2.5-2.6B68.2%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • MCP Atlas

    LFM2.5-2.6B—
    Ling 3.0 Flash65.5%
    Source

    Not directly comparable

  • skillsBench

    LFM2.5-2.6B—
    Ling 3.0 Flash44.8%
    Source

    Not directly comparable

  • WideResearch

    LFM2.5-2.6B—
    Ling 3.0 Flash73.6%
    Source

    Not directly comparable

  • BrowseComp

    LFM2.5-2.6B—
    Ling 3.0 Flash72.2%
    Source

    Not directly comparable

  • DRACO

    LFM2.5-2.6B—
    Ling 3.0 Flash70.4%
    Source

    Not directly comparable

Coding

  • LiveCodeBench v6

    LFM2.5-2.6B59.4%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • SWE-bench Pro

    LFM2.5-2.6B—
    Ling 3.0 Flash56.6%
    Source

    Not directly comparable

  • SWE Multilingual

    LFM2.5-2.6B—
    Ling 3.0 Flash72.4%
    Source

    Not directly comparable

  • LiveCodeBench v5

    LFM2.5-2.6B—
    Ling 3.0 Flash82.8%
    Source

    Not directly comparable

  • SciCode

    LFM2.5-2.6B—
    Ling 3.0 Flash41.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    LFM2.5-2.6B—
    Ling 3.0 Flash85.0%
    Source

    Not directly comparable

  • GPQA-D

    LFM2.5-2.6B—
    Ling 3.0 Flash85.0%
    Source

    Not directly comparable

  • HLE

    LFM2.5-2.6B—
    Ling 3.0 Flash22.7%
    Source

    Not directly comparable

Math

  • AIME 2025

    LFM2.5-2.6B51.9%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • AIME26

    LFM2.5-2.6B—
    Ling 3.0 Flash93.2%
    Source

    Not directly comparable

  • HMMT Feb 2026

    LFM2.5-2.6B—
    Ling 3.0 Flash87.0%
    Source

    Not directly comparable

  • IMOAnswerBench

    LFM2.5-2.6B—
    Ling 3.0 Flash83.7%
    Source

    Not directly comparable

Instruction following

  • IFBench

    LFM2.5-2.6B59.2%
    Source
    Ling 3.0 Flash74.5%
    Source

    Ling 3.0 Flash leads this result

Frequently asked questions

Which is better, LFM2.5-2.6B or Ling 3.0 Flash?

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, LFM2.5-2.6B or Ling 3.0 Flash?

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, LFM2.5-2.6B or Ling 3.0 Flash?

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, LFM2.5-2.6B or Ling 3.0 Flash?

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 Ling 3.0 Flash?

Ling 3.0 Flash has the larger documented context window: 262K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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