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BenchLM

LFM2.5-8B-A1B vs Qwen3.5 Flash

Updated September 27, 2026. Rank says Qwen3.5 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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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.

Model A
LiquidAI logo

LiquidAI

31.87/100

Estimated · Public rank #146

90% interval 20.4–43.4

Model B
Alibaba logo

Alibaba

45.49/100

Estimated · Public rank #93

90% interval 36.4–54.6

Shared results
0
LFM2.5-8B-A1B only
7
Qwen3.5 Flash only
6
Like-for-like categories
0 / 8
Estimated: LFM2.5-8B-A1B and Qwen3.5 FlashHow the comparison works

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

    Qwen3.5 Flash

    Qwen3.5 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

    LFM2.5-8B-A1B 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

    LFM2.5-8B-A1B and Qwen3.5 Flash are 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. LFM2.5-8B-A1B does not fit this workload in one request. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate. LFM2.5-8B-A1B has no comparable published API token rate.

    Confidence: rate-fallback
  • 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.

—LFM2.5-8B-A1B26.5Qwen3.5 Flash

Not comparable · BenchAlign v5.7

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.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
LFM2.5-8B-A1B
27.2
Supported · #144/158
Qwen3.5 Flash
43.0
Estimated · #80/158
Basis
BenchAlign v5.7 lane · 0 vs 2 public rows
Reading
Directional only

Agentic

Not comparable
LFM2.5-8B-A1B
Not ranked
Qwen3.5 Flash
Not ranked
Basis
BenchAlign v5.7 lane · 2 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-8B-A1B
Not ranked
Qwen3.5 Flash
26.5
Estimated · #97/135
Basis
BenchAlign v5.7 lane · 0 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-8B-A1B
22.1
Unranked · 2 rankable rows
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-8B-A1B
Not ranked
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-8B-A1B
Not ranked
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
LFM2.5-8B-A1B
35.4
#108/124
Qwen3.5 Flash
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
LFM2.5-8B-A1B
17.3
Unranked · 3 rankable rows
Qwen3.5 Flash
28.4
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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

LFM2.5-8B-A1B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.5 Flash
$0.0003
Fits in one request

LFM2.5-8B-A1B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-8B-A1B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.5 Flash
$0.0062
Fits in one request

LFM2.5-8B-A1B has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-8B-A1B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Qwen3.5 Flash
$0.026
Fits in one request
Cached input priced at the published list-input rate

LFM2.5-8B-A1B does not fit this workload in one request. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate. LFM2.5-8B-A1B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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.

LFM2.5-8B-A1B

128K

Qwen3.5 Flash

1M

API model ID

LFM2.5-8B-A1B

Not sourced

Qwen3.5 Flash

Not sourced

Cached-input rate

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

LFM2.5-8B-A1B

No comparable hosted API rate

Qwen3.5 Flash

Not published

Documented inputs

LFM2.5-8B-A1B

Not sourced

Qwen3.5 Flash

Not sourced

Documented outputs

LFM2.5-8B-A1B

Not sourced

Qwen3.5 Flash

Not sourced

Provider availability

LFM2.5-8B-A1B

Not sourced

Qwen3.5 Flash

Not sourced

Reasoning profile

LFM2.5-8B-A1B

Reasoning

Qwen3.5 Flash

Reasoning

Weight access

LFM2.5-8B-A1B

Open Weight

Qwen3.5 Flash

Proprietary

License

LFM2.5-8B-A1B

Open Weight

Qwen3.5 Flash

Proprietary

Release date

LFM2.5-8B-A1B

2026-05-28

Qwen3.5 Flash

2026-03-04

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
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
Qwen3.5 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, LFM2.5-8B-A1B or Qwen3.5 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, LFM2.5-8B-A1B or Qwen3.5 Flash?

LFM2.5-8B-A1B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LFM2.5-8B-A1B or Qwen3.5 Flash?

LFM2.5-8B-A1B and Qwen3.5 Flash are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, LFM2.5-8B-A1B or Qwen3.5 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, LFM2.5-8B-A1B or Qwen3.5 Flash?

Qwen3.5 Flash has the larger documented context window: 1M, compared with 128K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence13 rows

Agentic

  • BFCL v4

    LFM2.5-8B-A1B49.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • τ²-bench results

    LFM2.5-8B-A1B88.1%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Coding

  • LiveCodeBench (Vals)

    LFM2.5-8B-A1B—
    Qwen3.5 Flash83.3%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    LFM2.5-8B-A1B—
    Qwen3.5 Flash64.4%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    LFM2.5-8B-A1B—
    Qwen3.5 Flash82.8%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    LFM2.5-8B-A1B—
    Qwen3.5 Flash84.1%
    Source

    Not directly comparable

Instruction following

  • IFEval

    LFM2.5-8B-A1B91.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • IFBench

    LFM2.5-8B-A1B56.5%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Math

  • MATH-500

    LFM2.5-8B-A1B88.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • AIME 2025

    LFM2.5-8B-A1B42.5%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • AIME26

    LFM2.5-8B-A1B50.0%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    LFM2.5-8B-A1B—
    Qwen3.5 Flash6.207%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    LFM2.5-8B-A1B—
    Qwen3.5 Flash0.000%
    Source

    Not directly comparable

13 public results · 0 shared

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Last updated September 27, 2026