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LFM2.5-24B-A2B vs Muse Spark 1.3

Updated October 13, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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. 1 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
LFM2.5-24B-A2B

LiquidAI

37.8/100

Estimated · Public rank #141

Conditional range 23.4–52.1

Model B
Meta logo
Muse Spark 1.3

Meta

—

Evidence status unavailable

90% interval unavailable

Shared results
1
LFM2.5-24B-A2B only
12
Muse Spark 1.3 only
18
Like-for-like categories
0 / 8
Estimated: LFM2.5-24B-A2B. Conditional ranges do not establish rank confidence.How 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

    Muse Spark 1.3

    Muse Spark 1.3 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    LFM2.5-24B-A2B

    LFM2.5-24B-A2B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    LFM2.5-24B-A2B

    LFM2.5-24B-A2B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Muse Spark 1.3 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

    Muse Spark 1.3 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • 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. LFM2.5-24B-A2B does not fit this workload in one request.

    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.

28.5LFM2.5-24B-A2B—Muse Spark 1.3

Not comparable · BenchAlign v5.8

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
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.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Not comparable
LFM2.5-24B-A2B
30.2
Estimated · #88/124
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 8 public rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-24B-A2B
28.5
Estimated · #98/149
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 2 vs 6 public rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-24B-A2B
Not ranked
Muse Spark 1.3
82.9
#6/29
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-24B-A2B
Not ranked
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
LFM2.5-24B-A2B
40.8
Estimated · #112/181
Muse Spark 1.3
Not ranked
Basis
BenchAlign v5.8 lane · 3 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-24B-A2B
Not ranked
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
LFM2.5-24B-A2B
75.6
#61/128
Muse Spark 1.3
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
LFM2.5-24B-A2B
Not ranked
Muse Spark 1.3
78.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 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 v5.8) 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.

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

LFM2.5-24B-A2B
$0.0006
Fits in one request
Muse Spark 1.3
$0.00338
Fits in one request

LFM2.5-24B-A2B has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

LFM2.5-24B-A2B
$0.008
Fits in one request
Muse Spark 1.3
$0.07525
Fits in one request

LFM2.5-24B-A2B has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

LFM2.5-24B-A2B
$0.022
Does not fit in one request
Muse Spark 1.3
$0.0975
Fits in one request

LFM2.5-24B-A2B does not fit this workload in one request.

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.

Cached-input rate

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

LFM2.5-24B-A2B

$0.05 per 1M cached input tokens

Liquid AI launch post

Muse Spark 1.3

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.3 model page

Reasoning profile

LFM2.5-24B-A2B

Reasoning

Muse Spark 1.3

Reasoning

Weight access

LFM2.5-24B-A2B

Open Weight

Muse Spark 1.3

Proprietary

License

LFM2.5-24B-A2B

Open Weight

Muse Spark 1.3

Proprietary

Release date

LFM2.5-24B-A2B

2026-10-13

Muse Spark 1.3

2026-09-02

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
Repository review: $0.008 vs $0.07525. Cache-heavy agent loop: $0.022 vs $0.0975.
Context tradeoff
Muse Spark 1.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, LFM2.5-24B-A2B or Muse Spark 1.3?

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, LFM2.5-24B-A2B or Muse Spark 1.3?

Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LFM2.5-24B-A2B or Muse Spark 1.3?

Muse Spark 1.3 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, LFM2.5-24B-A2B or Muse Spark 1.3?

For the stated presets, chat costs $0.0006 on LFM2.5-24B-A2B and $0.00337 on Muse Spark 1.3; repository review costs $0.008 and $0.07525; the cache-heavy agent loop costs $0.022 and $0.0975. LFM2.5-24B-A2B does not fit this workload in one request.

Which has the larger context window, LFM2.5-24B-A2B or Muse Spark 1.3?

Muse Spark 1.3 has the larger documented context window: 1M, compared with 131K.

Benchmark evidence

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

Browse raw public benchmark evidence31 rows

Agentic

  • AutomationBench

    LFM2.5-24B-A2B27.7%
    Source
    Muse Spark 1.349.4%
    Source

    Muse Spark 1.3 leads this result

  • BFCL v4

    LFM2.5-24B-A2B65.0%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • τ³-bench results

    LFM2.5-24B-A2B8.0%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • PinchBench

    LFM2.5-24B-A2B83.2%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • WebArena-Verified

    LFM2.5-24B-A2B26.8%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Terminal-Bench 2.1

    LFM2.5-24B-A2B—
    Muse Spark 1.388.8%
    Source

    Not directly comparable

  • JobBench

    LFM2.5-24B-A2B—
    Muse Spark 1.364.9%
    Source

    Not directly comparable

  • OSWorld 2.0

    LFM2.5-24B-A2B—
    Muse Spark 1.366.9%
    Source

    Not directly comparable

  • DeepSearchQA

    LFM2.5-24B-A2B—
    Muse Spark 1.389.4%
    Source

    Not directly comparable

  • ApprenticeBench

    LFM2.5-24B-A2B—
    Muse Spark 1.319%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    LFM2.5-24B-A2B—
    Muse Spark 1.372.3%
    Source

    Not directly comparable

  • CWE-bench v1

    LFM2.5-24B-A2B—
    Muse Spark 1.355.0%
    Source

    Not directly comparable

Coding

  • SciCode

    LFM2.5-24B-A2B32.7%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • SWE-bench Verified

    LFM2.5-24B-A2B51%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Terminal-Bench 2.1

    LFM2.5-24B-A2B—
    Muse Spark 1.388.8%
    Source

    Not directly comparable

  • DeepSWE

    LFM2.5-24B-A2B—
    Muse Spark 1.375.4%
    Source

    Not directly comparable

  • SWE-Atlas Codebase QnA

    LFM2.5-24B-A2B—
    Muse Spark 1.359.4%
    Source

    Not directly comparable

  • CursorBench 4.0

    LFM2.5-24B-A2B—
    Muse Spark 1.341.6%
    Source

    Not directly comparable

  • Bug Hunt Bench

    LFM2.5-24B-A2B—
    Muse Spark 1.332.2 fixes
    Source

    Not directly comparable

  • FrontierSWE v2

    LFM2.5-24B-A2B—
    Muse Spark 1.325.8%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 256K-512K

    LFM2.5-24B-A2B—
    Muse Spark 1.398.5%
    Source

    Not directly comparable

  • MRCR v2 512K-1M

    LFM2.5-24B-A2B—
    Muse Spark 1.398.1%
    Source

    Not directly comparable

Knowledge

  • GPQA

    LFM2.5-24B-A2B74.4%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • GPQA-D

    LFM2.5-24B-A2B74.4%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • HLE

    LFM2.5-24B-A2B7.4%
    Source
    Muse Spark 1.3—

    Not directly comparable

Instruction following

  • IFEval

    LFM2.5-24B-A2B96.8%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • IFBench

    LFM2.5-24B-A2B74.7%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • Gray Swan IPI (15 attempts)

    LFM2.5-24B-A2B—
    Muse Spark 1.315.9%
    Source

    Not directly comparable

Math

  • MATH-500

    LFM2.5-24B-A2B96.0%
    Source
    Muse Spark 1.3—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    LFM2.5-24B-A2B—
    Muse Spark 1.374.035%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    LFM2.5-24B-A2B—
    Muse Spark 1.346.341%
    Source

    Not directly comparable

31 public results · 1 shared

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Last updated October 13, 2026