Model A

Z.AI

—

Evidence status unavailable

90% interval unavailable

GLM-5.3 vs Muse Spark

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Muse Spark

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
GLM-5.3
GLM-5.3 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
A complete comparable API-rate estimate is not available for both models.
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.

15 GLM-5.3 only

1 Shared

23 Muse Spark only

Shared results: 1
GLM-5.3 only: 15
Muse Spark only: 23
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

GLM-5.3: Not measured
Muse Spark: 59.0
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Coding

Not comparable

GLM-5.3: Not measured
Muse Spark: 67.8
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Reasoning

Not comparable

GLM-5.3: Not measured
Muse Spark: 42.5
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Knowledge

Not comparable

GLM-5.3: Not measured
Muse Spark: 50.4
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Math

Not comparable

GLM-5.3: Not measured
Muse Spark: 32.9
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3: Not measured
Muse Spark: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.3: Not measured
Muse Spark: 82.5
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3: Not measured
Muse Spark: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GLM-5.3 and Muse Spark
Category	GLM-5.3	Muse Spark	Weighted basis	Reading
Agentic	Not measured	59.0	Not comparable0 vs 1 rows	Not comparable
Coding	Not measured	67.8	Not comparable0 vs 2 rows	Not comparable
Reasoning	Not measured	42.5	Not comparable0 vs 1 rows	Not comparable
Knowledge	Not measured	50.4	Not comparable0 vs 1 rows	Not comparable
Math	Not measured	32.9	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	82.5	Not comparable0 vs 2 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	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

GLM-5.3: API rate not published; Fits in one request
Muse Spark: API rate not published; Fits in one request

GLM-5.3 has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3: API rate not published; Fits in one request
Muse Spark: API rate not published; Fits in one request

GLM-5.3 has no comparable published API token rate. Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-5.3: API rate not published; Fits in one request; Cached-input rate unavailable
Muse Spark: API rate not published; Fits in one request; Cached-input rate unavailable

GLM-5.3 has no comparable published API token 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.

SpecificationGLM-5.3Muse Spark

Context window

Maximum documented context; output-token limits may be lower.

GLM-5.3

Z.AI GLM-5.3 launch post

Muse Spark

262K

API model ID

GLM-5.3

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-5.3

No comparable hosted API rate

Z.AI GLM-5.3 launch post

Muse Spark

No comparable hosted API rate

Documented inputs

GLM-5.3

Not sourced

Muse Spark

Not sourced

Documented outputs

GLM-5.3

Not sourced

Muse Spark

Not sourced

Provider availability

GLM-5.3

Not sourced

Muse Spark

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Muse Spark

Reasoning

Weight access

GLM-5.3

Proprietary

Muse Spark

Proprietary

License

GLM-5.3

Proprietary

Muse Spark

Proprietary

Release date

GLM-5.3

2026-08-14

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: 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: GLM-5.3 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 evidence39 rows

Agentic

Terminal-Bench 2.1
GLM-5.388.2%
Source
Muse Spark—
Not directly comparable
terminalBench3
GLM-5.328.3%
Source
Muse Spark—
Not directly comparable
CyberGym
GLM-5.384.5%
Source
Muse Spark43.5%
Source
GLM-5.3 leads this result
ExploitGym
GLM-5.315.0%
Source
Muse Spark—
Not directly comparable
Toolathlon-Verified
GLM-5.373.0%
Source
Muse Spark—
Not directly comparable
AutomationBench
GLM-5.348.2%
Source
Muse Spark—
Not directly comparable
Agents' Last Exam
GLM-5.328.5%
Source
Muse Spark—
Not directly comparable
HLE w/ tools
GLM-5.362.5%
Source
Muse Spark—
Not directly comparable
Terminal-Bench 2.0
GLM-5.3—
Muse Spark59%
Source
Not directly comparable
τ²-bench results
GLM-5.3—
Muse Spark91.5%
Source
Not directly comparable
DeepSearchQA
GLM-5.3—
Muse Spark74.8%
Source
Not directly comparable
Claw-Eval
GLM-5.3—
Muse Spark63.8%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.388.2%
Source
Muse Spark—
Not directly comparable
terminalBench3
GLM-5.328.3%
Source
Muse Spark—
Not directly comparable
deepSwe
GLM-5.366.9%
Source
Muse Spark—
Not directly comparable
NL2Repo
GLM-5.358%
Source
Muse Spark—
Not directly comparable
ProgramBench
GLM-5.319.0%
Source
Muse Spark—
Not directly comparable
FrontierSWE
GLM-5.378.1%
Source
Muse Spark—
Not directly comparable
sweMarathon
GLM-5.342.5%
Source
Muse Spark—
Not directly comparable
PostTrain Bench
GLM-5.339.8%
Source
Muse Spark—
Not directly comparable
SWE-bench Verified
GLM-5.3—
Muse Spark77.4%
Source
Not directly comparable
SWE-bench Pro
GLM-5.3—
Muse Spark52.4%
Source
Not directly comparable
LiveCodeBench Pro
GLM-5.3—
Muse Spark80.0%
Source
Not directly comparable
Vibe Code Bench
GLM-5.3—
Muse Spark19.67%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GLM-5.3—
Muse Spark42.5%
Source
Not directly comparable

Knowledge

GPQA-D
GLM-5.3—
Muse Spark89.5%
Source
Not directly comparable
HLE
GLM-5.3—
Muse Spark50.4%
Source
Not directly comparable
HLE w/o tools
GLM-5.3—
Muse Spark42.8%
Source
Not directly comparable
HealthBench Hard
GLM-5.3—
Muse Spark42.8%
Source
Not directly comparable
MedXpertQA (Text)
GLM-5.3—
Muse Spark52.6%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GLM-5.3—
Muse Spark39.000%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.3—
Muse Spark14.600%
Source
Not directly comparable

Multimodal

CharXiv
GLM-5.3—
Muse Spark86.4%
Source
Not directly comparable
MMMU-Pro
GLM-5.3—
Muse Spark80.4%
Source
Not directly comparable
ERQA
GLM-5.3—
Muse Spark64.7%
Source
Not directly comparable
SimpleVQA
GLM-5.3—
Muse Spark71.3%
Source
Not directly comparable
ScreenSpot Pro
GLM-5.3—
Muse Spark84.1%
Source
Not directly comparable
ZeroBench
GLM-5.3—
Muse Spark33.0%
Source
Not directly comparable
MedXpertQA (MM)
GLM-5.3—
Muse Spark78.4%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5.3 or Muse Spark?

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, GLM-5.3 or Muse Spark?

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, GLM-5.3 or Muse Spark?

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, GLM-5.3 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-5.3 or Muse Spark?

GLM-5.3 has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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GLM-5.3 vs Muse Spark

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GLM-5.3 or Muse Spark?

Which is better for coding, GLM-5.3 or Muse Spark?

Which is better for agentic tasks, GLM-5.3 or Muse Spark?

Which costs less, GLM-5.3 or Muse Spark?

Which has the larger context window, GLM-5.3 or Muse Spark?

Related comparisons

Watch GLM-5.3 vs Muse Spark