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Model comparison

LFM2.5-2.6B vs Muse Spark 1.1

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

Muse Spark 1.1

Meta

76.2/100

Supported · Public rank #7

90% interval 72.3–80.0

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 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

    Muse Spark 1.1

    Muse Spark 1.1 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. Muse Spark 1.1 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
LFM2.5-2.6B only
7
Muse Spark 1.1 only
21
Like-for-like categories
0 / 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.

Agentic

Not comparable
LFM2.5-2.6B
Not measured
Muse Spark 1.1
80.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-2.6B
Not measured
Muse Spark 1.1
61.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-2.6B
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
LFM2.5-2.6B
Not measured
Muse Spark 1.1
62.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
LFM2.5-2.6B
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-2.6B
Not measured
Muse Spark 1.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-2.6B
Not measured
Muse Spark 1.1
88.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
LFM2.5-2.6B
59.2
Muse Spark 1.1
Not measured
Weighted basis
1 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.

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

LFM2.5-2.6B
Self-hosted; infrastructure cost varies
Fits in one request
Muse Spark 1.1
API rate not published
Fits in one request

LFM2.5-2.6B has no comparable published API token rate. Muse Spark 1.1 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
Muse Spark 1.1
API rate not published
Fits in one request

LFM2.5-2.6B has no comparable published API token rate. Muse Spark 1.1 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
Muse Spark 1.1
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. Muse Spark 1.1 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

Muse Spark 1.1

Not sourced

Cached-input rate

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

LFM2.5-2.6B

No comparable hosted API rate

LiquidAI Hugging Face model card

Muse Spark 1.1

No comparable hosted API rate

Documented inputs

LFM2.5-2.6B

Not sourced

Muse Spark 1.1

Not sourced

Documented outputs

LFM2.5-2.6B

Not sourced

Muse Spark 1.1

Not sourced

Provider availability

LFM2.5-2.6B

Not sourced

Muse Spark 1.1

Not sourced

Reasoning profile

LFM2.5-2.6B

Reasoning

Muse Spark 1.1

Reasoning

Weight access

LFM2.5-2.6B

Open Weight

Muse Spark 1.1

Proprietary

License

LFM2.5-2.6B

Open Weight

Muse Spark 1.1

Proprietary

Release date

LFM2.5-2.6B

2026-08-04

Muse Spark 1.1

2026-07-09

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
Muse Spark 1.1 has the larger documented window (1M).

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 evidence28 rows

Agentic

  • BFCL v4

    LFM2.5-2.6B56.9%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • τ³-bench results

    LFM2.5-2.6B5.7%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • Claw-Eval

    LFM2.5-2.6B62.9%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • PinchBench

    LFM2.5-2.6B68.2%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • Terminal-Bench 2.0

    LFM2.5-2.6B—
    Muse Spark 1.180%
    Source

    Not directly comparable

  • MCP Atlas

    LFM2.5-2.6B—
    Muse Spark 1.188.1%
    Source

    Not directly comparable

  • Toolathlon

    LFM2.5-2.6B—
    Muse Spark 1.175.6%
    Source

    Not directly comparable

  • OSWorld-Verified

    LFM2.5-2.6B—
    Muse Spark 1.180.8%
    Source

    Not directly comparable

  • WebArena-Verified

    LFM2.5-2.6B—
    Muse Spark 1.169%
    Source

    Not directly comparable

  • DeepSearchQA

    LFM2.5-2.6B—
    Muse Spark 1.184.9%
    Source

    Not directly comparable

  • CyberGym

    LFM2.5-2.6B—
    Muse Spark 1.159.0%
    Source

    Not directly comparable

  • Finance Agent v2

    LFM2.5-2.6B—
    Muse Spark 1.157.2%
    Source

    Not directly comparable

  • deepSwe

    LFM2.5-2.6B—
    Muse Spark 1.153.3%
    Source

    Not directly comparable

  • OSWorld 2.0

    LFM2.5-2.6B—
    Muse Spark 1.114.2%
    Source

    Not directly comparable

  • JobBench

    LFM2.5-2.6B—
    Muse Spark 1.154.7%
    Source

    Not directly comparable

  • Cybench

    LFM2.5-2.6B—
    Muse Spark 1.192.9%
    Source

    Not directly comparable

  • ExploitGym

    LFM2.5-2.6B—
    Muse Spark 1.10.8%
    Source

    Not directly comparable

Coding

  • LiveCodeBench v6

    LFM2.5-2.6B59.4%
    Source
    Muse Spark 1.1—

    Not directly comparable

  • Terminal-Bench 2.0

    LFM2.5-2.6B—
    Muse Spark 1.180.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    LFM2.5-2.6B—
    Muse Spark 1.161.5%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    LFM2.5-2.6B—
    Muse Spark 1.154.1%
    Source

    Not directly comparable

Knowledge

  • HLE

    LFM2.5-2.6B—
    Muse Spark 1.162.1%
    Source

    Not directly comparable

  • HLE w/o tools

    LFM2.5-2.6B—
    Muse Spark 1.152.2%
    Source

    Not directly comparable

  • HealthBench Professional

    LFM2.5-2.6B—
    Muse Spark 1.159.3%
    Source

    Not directly comparable

Math

  • AIME 2025

    LFM2.5-2.6B51.9%
    Source
    Muse Spark 1.1—

    Not directly comparable

Multimodal

  • CharXiv

    LFM2.5-2.6B—
    Muse Spark 1.188.4%
    Source

    Not directly comparable

  • BabyVision

    LFM2.5-2.6B—
    Muse Spark 1.176.3%
    Source

    Not directly comparable

Instruction following

  • IFBench

    LFM2.5-2.6B59.2%
    Source
    Muse Spark 1.1—

    Not directly comparable

Frequently asked questions

Which is better, LFM2.5-2.6B or Muse Spark 1.1?

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, LFM2.5-2.6B or Muse Spark 1.1?

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 Muse Spark 1.1?

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 Muse Spark 1.1?

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 Muse Spark 1.1?

Muse Spark 1.1 has the larger documented context window: 1M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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