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Model A
LFM2.5-VL-450M

LiquidAI

Evidence status unavailable

90% interval unavailable

LFM2.5-VL-450M vs Ling 2.6 Flash

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

InclusionAI logo
Model B
Ling 2.6 Flash

InclusionAI

43.68/100

Estimated · Public rank #176

90% interval 32.255.2

Decision reading

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 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

    Ling 2.6 Flash

    Ling 2.6 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

    LFM2.5-VL-450M 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

    LFM2.5-VL-450M and Ling 2.6 Flash are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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-450M does not fit this workload in one request. LFM2.5-VL-450M has no comparable published API token rate. Ling 2.6 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
0
LFM2.5-VL-450M only
7
Ling 2.6 Flash only
0
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.

Agentic

Directional only
LFM2.5-VL-450M
44.7
Estimated · #98/151
Ling 2.6 Flash
39.9
Estimated · #122/151
Basis
BenchAlign lane · 1 vs 0 public rows
Reading
Directional only

Knowledge

Directional only
LFM2.5-VL-450M
35.7
Estimated · #160/181
Ling 2.6 Flash
41.4
Estimated · #132/181
Basis
BenchAlign lane · 2 vs 0 public rows
Reading
Directional only

Coding

Not comparable
LFM2.5-VL-450M
Not ranked
Ling 2.6 Flash
42.7
Estimated · #127/183
Basis
BenchAlign lane · 0 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-VL-450M
Not ranked
Ling 2.6 Flash
41.2
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
LFM2.5-VL-450M
Not ranked
Ling 2.6 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-VL-450M
Not ranked
Ling 2.6 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-VL-450M
Not ranked
Ling 2.6 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
LFM2.5-VL-450M
Not ranked
Ling 2.6 Flash
46.6
#86/120
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-VL-450M
Self-hosted; infrastructure cost varies
Fits in one request
Ling 2.6 Flash
API rate not published
Fits in one request

LFM2.5-VL-450M has no comparable published API token rate. Ling 2.6 Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-VL-450M
Self-hosted; infrastructure cost varies
Fits in one request
Ling 2.6 Flash
API rate not published
Fits in one request

LFM2.5-VL-450M has no comparable published API token rate. Ling 2.6 Flash has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-VL-450M
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Ling 2.6 Flash
API rate not published
Fits in one request
Cached-input rate unavailable

LFM2.5-VL-450M does not fit this workload in one request. LFM2.5-VL-450M has no comparable published API token rate. Ling 2.6 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.

Context window

Maximum documented context; output-token limits may be lower.

LFM2.5-VL-450M

128K

Ling 2.6 Flash

262K

API model ID

LFM2.5-VL-450M

Not sourced

Ling 2.6 Flash

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-VL-450M

No comparable hosted API rate

Ling 2.6 Flash

No comparable hosted API rate

Documented inputs

LFM2.5-VL-450M

Not sourced

Ling 2.6 Flash

Not sourced

Documented outputs

LFM2.5-VL-450M

Not sourced

Ling 2.6 Flash

Not sourced

Provider availability

LFM2.5-VL-450M

Not sourced

Ling 2.6 Flash

Not sourced

Reasoning profile

LFM2.5-VL-450M

Non-Reasoning

Ling 2.6 Flash

Non-Reasoning

Weight access

LFM2.5-VL-450M

Open Weight

Ling 2.6 Flash

Open Weight

License

LFM2.5-VL-450M

Open Weight

Ling 2.6 Flash

Open Weight

Release date

LFM2.5-VL-450M

2026-04-08

Ling 2.6 Flash

2026-04-21

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
Ling 2.6 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 evidence7 rows

Agentic

  • BFCL v4

    LFM2.5-VL-450M21.1%
    Source
    Ling 2.6 Flash

    Not directly comparable

Knowledge

  • GPQA

    LFM2.5-VL-450M25.7%
    Source
    Ling 2.6 Flash

    Not directly comparable

  • MMLU-Pro

    LFM2.5-VL-450M19.3%
    Source
    Ling 2.6 Flash

    Not directly comparable

Multimodal

  • MMMU

    LFM2.5-VL-450M32.7%
    Source
    Ling 2.6 Flash

    Not directly comparable

  • RealWorldQA

    LFM2.5-VL-450M58.4%
    Source
    Ling 2.6 Flash

    Not directly comparable

  • CountBench

    LFM2.5-VL-450M73.3%
    Source
    Ling 2.6 Flash

    Not directly comparable

Instruction following

  • IFEval

    LFM2.5-VL-450M61.2%
    Source
    Ling 2.6 Flash

    Not directly comparable

Frequently asked questions

Which is better, LFM2.5-VL-450M or Ling 2.6 Flash?

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, LFM2.5-VL-450M or Ling 2.6 Flash?

LFM2.5-VL-450M is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LFM2.5-VL-450M or Ling 2.6 Flash?

LFM2.5-VL-450M scores higher for agentic tasks on the public lane, 44.7 to 39.9. LFM2.5-VL-450M and Ling 2.6 Flash are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, LFM2.5-VL-450M or Ling 2.6 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-VL-450M or Ling 2.6 Flash?

Ling 2.6 Flash has the larger documented context window: 262K, compared with 128K.

Related comparisons

Last updated September 4, 2026

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