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BenchLM
Model A
GLM-5.1

Z.AI

66.7/100

Supported · Public rank #27

90% interval 56.2–77.3

GLM-5.1 vs Muse Spark

Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Muse Spark

Meta

70.6/100

Supported · Public rank #17

90% interval 61.0–80.2

Decision reading

Muse Spark has the higher public score estimate, 70.6 versus 66.72, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

9 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

    Muse Spark 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark 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

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
9
GLM-5.1 only
11
Muse Spark only
15
Like-for-like categories
1 / 8

3 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

Like-for-like
GLM-5.1
52.3
Muse Spark
50.4
Weighted basis
1 vs 1 rows
Reading
GLM-5.1 leads

Agentic

Directional only
GLM-5.1
65.4
Muse Spark
59.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Directional only
GLM-5.1
61.3
Muse Spark
67.8
Weighted basis
2 vs 2 rows
Reading
Directional only

Math

Directional only
GLM-5.1
62.0
Muse Spark
32.9
Weighted basis
4 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.1
Not measured
Muse Spark
42.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.1
Not measured
Muse Spark
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.1
Not measured
Muse Spark
82.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.1
Not measured
Muse Spark
Not measured
Weighted basis
0 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.

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

GLM-5.1
$0.0036
Fits in one request
Muse Spark
API rate not published
Fits in one request

Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.1
$0.0832
Fits in one request
Muse Spark
API rate not published
Fits in one request

Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-5.1
$0.352
Does not fit in one request
Cached input priced at the published list-input rate
Muse Spark
API rate not published
Fits in one request
Cached-input rate unavailable

GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark 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.

GLM-5.1

203K

Muse Spark

262K

API model ID

GLM-5.1

Not sourced

Muse Spark

Not sourced

Cached-input rate

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

GLM-5.1

Not published

Muse Spark

No comparable hosted API rate

Documented inputs

GLM-5.1

Not sourced

Muse Spark

Not sourced

Documented outputs

GLM-5.1

Not sourced

Muse Spark

Not sourced

Provider availability

GLM-5.1

Not sourced

Muse Spark

Not sourced

Reasoning profile

GLM-5.1

Reasoning

Muse Spark

Reasoning

Weight access

GLM-5.1

Open Weight

Muse Spark

Proprietary

License

GLM-5.1

Open Weight

Muse Spark

Proprietary

Release date

GLM-5.1

2026-04-07

Muse Spark

2026-04-08

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
Muse Spark has the higher public score estimate, 70.6 versus 66.72, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Muse Spark has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Muse Spark
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence35 rows

Agentic

  • Terminal-Bench 2.0

    GLM-5.163.5%
    Source
    Muse Spark59%
    Source

    GLM-5.1 leads this result

  • BrowseComp

    GLM-5.168%
    Source
    Muse Spark—

    Not directly comparable

  • τ³-bench results

    GLM-5.170.6%
    Source
    Muse Spark—

    Not directly comparable

  • MCP Atlas

    GLM-5.171.8%
    Source
    Muse Spark—

    Not directly comparable

  • CyberGym

    Shared source
    GLM-5.168.7%
    Muse Spark43.5%

    GLM-5.1 leads this result

  • Claw-Eval

    Shared source
    GLM-5.162.3%
    Muse Spark63.8%

    Muse Spark leads this result

  • Gert Labs

    GLM-5.160.11%
    Source
    Muse Spark—

    Not directly comparable

  • ResearchClawBench

    GLM-5.118.2%
    Source
    Muse Spark—

    Not directly comparable

  • τ²-bench results

    GLM-5.1—
    Muse Spark91.5%
    Source

    Not directly comparable

  • DeepSearchQA

    GLM-5.1—
    Muse Spark74.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.158.4%
    Source
    Muse Spark52.4%
    Source

    GLM-5.1 leads this result

  • NL2Repo

    GLM-5.142.7%
    Source
    Muse Spark—

    Not directly comparable

  • SWE-Rebench

    GLM-5.162.7%
    Source
    Muse Spark—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    GLM-5.131.46%
    Muse Spark19.67%

    GLM-5.1 leads this result

  • SWE-bench Verified

    GLM-5.1—
    Muse Spark77.4%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    GLM-5.1—
    Muse Spark80.0%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GLM-5.1—
    Muse Spark42.5%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GLM-5.186.2%
    Source
    Muse Spark89.5%
    Source

    Muse Spark leads this result

  • HLE

    GLM-5.152.3%
    Source
    Muse Spark50.4%
    Source

    GLM-5.1 leads this result

  • HLE w/o tools

    GLM-5.1—
    Muse Spark42.8%
    Source

    Not directly comparable

  • HealthBench Hard

    GLM-5.1—
    Muse Spark42.8%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    GLM-5.1—
    Muse Spark52.6%
    Source

    Not directly comparable

Math

  • AIME26

    GLM-5.195.3%
    Source
    Muse Spark—

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.194.0%
    Source
    Muse Spark—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.182.6%
    Source
    Muse Spark—

    Not directly comparable

  • MMAnswerBench

    GLM-5.183.8%
    Source
    Muse Spark—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GLM-5.133.448%
    Muse Spark39.000%

    Muse Spark leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GLM-5.112.500%
    Muse Spark14.600%

    Muse Spark leads this result

Multimodal

  • CharXiv

    GLM-5.1—
    Muse Spark86.4%
    Source

    Not directly comparable

  • MMMU-Pro

    GLM-5.1—
    Muse Spark80.4%
    Source

    Not directly comparable

  • ERQA

    GLM-5.1—
    Muse Spark64.7%
    Source

    Not directly comparable

  • SimpleVQA

    GLM-5.1—
    Muse Spark71.3%
    Source

    Not directly comparable

  • ScreenSpot Pro

    GLM-5.1—
    Muse Spark84.1%
    Source

    Not directly comparable

  • ZeroBench

    GLM-5.1—
    Muse Spark33.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    GLM-5.1—
    Muse Spark78.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-5.1 or Muse Spark?

Muse Spark has the higher public score estimate, 70.6 versus 66.72, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-5.1 or Muse Spark?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GLM-5.1 or Muse Spark?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GLM-5.1 or Muse Spark?

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, GLM-5.1 or Muse Spark?

Muse Spark has the larger documented context window: 262K, compared with 203K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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