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BenchLM
Model A
LFM2.5-230M

LiquidAI

—

Evidence status unavailable

90% interval unavailable

LFM2.5-230M vs Qwen3.5-122B-A10B

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.5-122B-A10B

Alibaba

59.4/100

Supported · Public rank #61

90% interval 48.2–70.6

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.

3 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

    Qwen3.5-122B-A10B

    Qwen3.5-122B-A10B 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. LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate. Qwen3.5-122B-A10B 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. LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate. Qwen3.5-122B-A10B 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
3
LFM2.5-230M only
3
Qwen3.5-122B-A10B only
12
Like-for-like categories
0 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Knowledge

Directional only
LFM2.5-230M
21.2
Qwen3.5-122B-A10B
83.6
Weighted basis
2 vs 3 rows
Reading
Directional only

Instruction following

Directional only
LFM2.5-230M
50.1
Qwen3.5-122B-A10B
93.4
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
LFM2.5-230M
Not measured
Qwen3.5-122B-A10B
56.4
Weighted basis
0 vs 3 rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-230M
Not measured
Qwen3.5-122B-A10B
72.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-230M
Not measured
Qwen3.5-122B-A10B
60.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
LFM2.5-230M
Not measured
Qwen3.5-122B-A10B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-230M
Not measured
Qwen3.5-122B-A10B
82.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-230M
Not measured
Qwen3.5-122B-A10B
77.2
Weighted basis
0 vs 1 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

LFM2.5-230M
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

LFM2.5-230M has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-230M
Self-hosted; infrastructure cost varies
Does not fit in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-230M
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate. Qwen3.5-122B-A10B 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.

LFM2.5-230M

32K

Qwen3.5-122B-A10B

262K

API model ID

LFM2.5-230M

Not sourced

Qwen3.5-122B-A10B

Not sourced

Cached-input rate

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

LFM2.5-230M

No comparable hosted API rate

Qwen3.5-122B-A10B

No comparable hosted API rate

Documented inputs

LFM2.5-230M

Not sourced

Qwen3.5-122B-A10B

Not sourced

Documented outputs

LFM2.5-230M

Not sourced

Qwen3.5-122B-A10B

Not sourced

Provider availability

LFM2.5-230M

Not sourced

Qwen3.5-122B-A10B

Not sourced

Reasoning profile

LFM2.5-230M

Non-Reasoning

Qwen3.5-122B-A10B

Reasoning

Weight access

LFM2.5-230M

Open Weight

Qwen3.5-122B-A10B

Open Weight

License

LFM2.5-230M

Open Weight

Qwen3.5-122B-A10B

Open Weight

Release date

LFM2.5-230M

2026-06-25

Qwen3.5-122B-A10B

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
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
Qwen3.5-122B-A10B 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 evidence18 rows

Agentic

  • BFCL v4

    LFM2.5-230M21.0%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • Terminal-Bench 2.0

    LFM2.5-230M—
    Qwen3.5-122B-A10B49.4%
    Source

    Not directly comparable

  • BrowseComp

    LFM2.5-230M—
    Qwen3.5-122B-A10B63.8%
    Source

    Not directly comparable

  • OSWorld-Verified

    LFM2.5-230M—
    Qwen3.5-122B-A10B58%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    LFM2.5-230M—
    Qwen3.5-122B-A10B72%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    LFM2.5-230M—
    Qwen3.5-122B-A10B60.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    LFM2.5-230M25.4%
    Source
    Qwen3.5-122B-A10B86.6%
    Source

    Qwen3.5-122B-A10B leads this result

  • GPQA-D

    LFM2.5-230M25.4%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • MMLU-Pro

    LFM2.5-230M20.3%
    Source
    Qwen3.5-122B-A10B86.7%
    Source

    Qwen3.5-122B-A10B leads this result

  • SuperGPQA

    LFM2.5-230M—
    Qwen3.5-122B-A10B67.1%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    LFM2.5-230M—
    Qwen3.5-122B-A10B82.2%
    Source

    Not directly comparable

Multimodal

  • MMMU

    LFM2.5-230M—
    Qwen3.5-122B-A10B83.9%
    Source

    Not directly comparable

  • MMVU

    LFM2.5-230M—
    Qwen3.5-122B-A10B74.7%
    Source

    Not directly comparable

  • MathVision

    LFM2.5-230M—
    Qwen3.5-122B-A10B86.2%
    Source

    Not directly comparable

  • CharXiv

    LFM2.5-230M—
    Qwen3.5-122B-A10B77.2%
    Source

    Not directly comparable

  • V*

    LFM2.5-230M—
    Qwen3.5-122B-A10B93.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    LFM2.5-230M71.7%
    Source
    Qwen3.5-122B-A10B93.4%
    Source

    Qwen3.5-122B-A10B leads this result

  • IFBench

    LFM2.5-230M38.4%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

Frequently asked questions

Which is better, LFM2.5-230M or Qwen3.5-122B-A10B?

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, LFM2.5-230M or Qwen3.5-122B-A10B?

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, LFM2.5-230M or Qwen3.5-122B-A10B?

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, LFM2.5-230M or Qwen3.5-122B-A10B?

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, LFM2.5-230M or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B has the larger documented context window: 262K, compared with 32K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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