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Model comparison

LFM2.5-230M vs Muse Glimmer 30B

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

LFM2.5-230M

LiquidAI

—

Evidence status unavailable

90% interval unavailable

Muse Glimmer 30B

Meta

—

Evidence status unavailable

90% interval unavailable

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 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 Glimmer 30B

    Muse Glimmer 30B 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. Muse Glimmer 30B does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate. Muse Glimmer 30B 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. Muse Glimmer 30B 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
1
LFM2.5-230M only
5
Muse Glimmer 30B only
13
Like-for-like categories
0 / 8

1 category uses 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.

Instruction following

Directional only
LFM2.5-230M
50.1
Muse Glimmer 30B
77.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
LFM2.5-230M
Not measured
Muse Glimmer 30B
65.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-230M
Not measured
Muse Glimmer 30B
57.8
Weighted basis
0 vs 3 rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-230M
Not measured
Muse Glimmer 30B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
LFM2.5-230M
21.2
Muse Glimmer 30B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
LFM2.5-230M
Not measured
Muse Glimmer 30B
94.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-230M
Not measured
Muse Glimmer 30B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-230M
Not measured
Muse Glimmer 30B
75.7
Weighted basis
0 vs 2 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
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request

LFM2.5-230M has no comparable published API token rate. Muse Glimmer 30B 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
Muse Glimmer 30B
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. Muse Glimmer 30B 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
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

LFM2.5-230M does not fit this workload in one request. Muse Glimmer 30B does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate. Muse Glimmer 30B 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

Muse Glimmer 30B

131K

API model ID

LFM2.5-230M

Not sourced

Muse Glimmer 30B

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

Muse Glimmer 30B

No comparable hosted API rate

Documented inputs

LFM2.5-230M

Not sourced

Muse Glimmer 30B

Not sourced

Documented outputs

LFM2.5-230M

Not sourced

Muse Glimmer 30B

Not sourced

Provider availability

LFM2.5-230M

Not sourced

Muse Glimmer 30B

Not sourced

Reasoning profile

LFM2.5-230M

Non-Reasoning

Muse Glimmer 30B

Reasoning

Weight access

LFM2.5-230M

Open Weight

Muse Glimmer 30B

Open Weight

License

LFM2.5-230M

Open Weight

Muse Glimmer 30B

Open Weight

Release date

LFM2.5-230M

2026-06-25

Muse Glimmer 30B

2026-08-10

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
Muse Glimmer 30B has the larger documented window (131K).

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 evidence19 rows

Agentic

  • BFCL v4

    LFM2.5-230M21.0%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • MCP Atlas

    LFM2.5-230M—
    Muse Glimmer 30B75.5%
    Source

    Not directly comparable

  • DeepSearchQA

    LFM2.5-230M—
    Muse Glimmer 30B74.6%
    Source

    Not directly comparable

  • skillsBench

    LFM2.5-230M—
    Muse Glimmer 30B44.3%
    Source

    Not directly comparable

  • OSWorld-Verified

    LFM2.5-230M—
    Muse Glimmer 30B65.9%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    LFM2.5-230M—
    Muse Glimmer 30B51.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    LFM2.5-230M—
    Muse Glimmer 30B76%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    LFM2.5-230M—
    Muse Glimmer 30B51.7%
    Source

    Not directly comparable

  • SciCode

    LFM2.5-230M—
    Muse Glimmer 30B43.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    LFM2.5-230M25.4%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • GPQA-D

    LFM2.5-230M25.4%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • MMLU-Pro

    LFM2.5-230M20.3%
    Source
    Muse Glimmer 30B—

    Not directly comparable

Math

  • AIME26

    LFM2.5-230M—
    Muse Glimmer 30B94.7%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    LFM2.5-230M—
    Muse Glimmer 30B78.8%
    Source

    Not directly comparable

  • ScreenSpot Pro

    LFM2.5-230M—
    Muse Glimmer 30B75.4%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    LFM2.5-230M—
    Muse Glimmer 30B75.8%
    Source

    Not directly comparable

  • MMMU-Pro

    LFM2.5-230M—
    Muse Glimmer 30B74%
    Source

    Not directly comparable

Instruction following

  • IFEval

    LFM2.5-230M71.7%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • IFBench

    LFM2.5-230M38.4%
    Source
    Muse Glimmer 30B77%
    Source

    Muse Glimmer 30B leads this result

Frequently asked questions

Which is better, LFM2.5-230M or Muse Glimmer 30B?

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 Muse Glimmer 30B?

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 Muse Glimmer 30B?

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 Muse Glimmer 30B?

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 Muse Glimmer 30B?

Muse Glimmer 30B has the larger documented context window: 131K, compared with 32K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

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