GLM-5.1 vs Muse Spark 1.3

Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GLM-5.1

Z.AI

56.31/100

Supported · Public rank #62

90% interval 45.6–67.0

Model B

Muse Spark 1.3

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
Muse Spark 1.3
Muse Spark 1.3 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
Muse Spark 1.3
Muse Spark 1.3 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. 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.
Confidence: rate-fallback

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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

47.2GLM-5.1—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 leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

FrontierMath v2 (Tiers 1-3)Math
Normalized gap 37.2
GLM-5.1:36.842%
Muse Spark 1.3:74.035%
Shared source

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

GLM-5.1: 43.4; Estimated · #62/128
Muse Spark 1.3: Not ranked
Basis: BenchAlign v5.8 lane · 10 vs 9 public rows
Reading: Not comparable

Coding

Not comparable

GLM-5.1: 47.2; Supported · #50/150
Muse Spark 1.3: Not ranked
Basis: BenchAlign v5.8 lane · 8 vs 6 public rows
Reading: Not comparable

Reasoning

Not comparable

GLM-5.1: 76.5; Unranked · 2 rankable rows
Muse Spark 1.3: 82.9; #6/28
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.1: Not ranked
Muse Spark 1.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

GLM-5.1: 52.7; Supported · #70/184
Muse Spark 1.3: Not ranked
Basis: BenchAlign v5.8 lane · 4 vs 0 public rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.1: Not ranked
Muse Spark 1.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.1: 93.8; #6/148
Muse Spark 1.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.1: 66.7; #7/12
Muse Spark 1.3: 78.5; Unranked · 2 rankable rows
Basis: Provisional lane · 3 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.1 and Muse Spark 1.3
Category	GLM-5.1	Muse Spark 1.3	Basis	Reading
Agentic	43.4Estimated · #62/128	Not ranked	Not comparableBenchAlign v5.8 lane · 10 vs 9 public rows	Not comparable
Coding	47.2Supported · #50/150	Not ranked	Not comparableBenchAlign v5.8 lane · 8 vs 6 public rows	Not comparable
Reasoning	76.5Unranked · 2 rankable rows	82.9#6/28	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	52.7Supported · #70/184	Not ranked	Not comparableBenchAlign v5.8 lane · 4 vs 0 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	93.8#6/148	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	66.7#7/12	78.5Unranked · 2 rankable rows	Not comparableProvisional lane · 3 vs 2 weighted rows	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.

Supported evidence per lane · 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

GLM-5.1: $0.0036; Fits in one request
Muse Spark 1.3: $0.00338; Fits in one request

Muse Spark 1.3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5.1: $0.0832; Fits in one request
Muse Spark 1.3: $0.07525; Fits in one request

Muse Spark 1.3 has the lower modeled cost

Costs use the listed standard API rates.

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 1.3: $0.0975; Fits 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.

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.

SpecificationGLM-5.1Muse Spark 1.3

Context window

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

GLM-5.1

203K

Muse Spark 1.3

Meta Muse Spark 1.3 model page

API model ID

GLM-5.1

Not sourced

Muse Spark 1.3

muse-spark-1.3

Meta Muse Spark 1.3 model page

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 1.3

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.3 model page

Documented inputs

GLM-5.1

Not sourced

Muse Spark 1.3

text, image, video, document

Meta Muse Spark 1.3 model page

Documented outputs

GLM-5.1

Not sourced

Muse Spark 1.3

text

Meta Muse Spark 1.3 model page

Provider availability

GLM-5.1

Not sourced

Muse Spark 1.3

Generally Available · Meta Model API, Muse Code

Meta AI Research Muse Spark 1.3 launch

Reasoning profile

GLM-5.1

Reasoning

Muse Spark 1.3

Reasoning

Weight access

GLM-5.1

Open Weight

Muse Spark 1.3

Proprietary

License

GLM-5.1

Open Weight

Muse Spark 1.3

Proprietary

Release date

GLM-5.1

2026-04-07

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: $0.0832 vs $0.07525. Cache-heavy agent loop: $0.352 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, GLM-5.1 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, GLM-5.1 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, GLM-5.1 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, GLM-5.1 or Muse Spark 1.3?

For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.00337 on Muse Spark 1.3; repository review costs $0.0832 and $0.07525; the cache-heavy agent loop costs $0.352 and $0.0975. 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.

Which has the larger context window, GLM-5.1 or Muse Spark 1.3?

Muse Spark 1.3 has the larger documented context window: 1M, compared with 203K.

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$27,962

Break-even405M/day

Muse Spark 1.3

API / mo$4,125

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 evidence45 rows

Agentic

Terminal-Bench 2.0
GLM-5.163.5%
Source
Muse Spark 1.3—
Not directly comparable
BrowseComp
GLM-5.168%
Source
Muse Spark 1.3—
Not directly comparable
τ³-bench results
GLM-5.170.6%
Source
Muse Spark 1.3—
Not directly comparable
MCP Atlas
GLM-5.171.8%
Source
Muse Spark 1.3—
Not directly comparable
CyberGym
GLM-5.168.7%
Source
Muse Spark 1.3—
Not directly comparable
Claw-Eval
GLM-5.162.3%
Source
Muse Spark 1.3—
Not directly comparable
Gert Labs
GLM-5.160.11%
Source
Muse Spark 1.3—
Not directly comparable
ResearchClawBench
GLM-5.118.2%
Source
Muse Spark 1.3—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%
Source
Muse Spark 1.372.3%
Source
Muse Spark 1.3 leads this result
HLE w/ tools
GLM-5.152.3%
Source
Muse Spark 1.3—
Not directly comparable
Terminal-Bench 2.1
GLM-5.1—
Muse Spark 1.388.8%
Source
Not directly comparable
JobBench
GLM-5.1—
Muse Spark 1.364.9%
Source
Not directly comparable
OSWorld 2.0
GLM-5.1—
Muse Spark 1.366.9%
Source
Not directly comparable
DeepSearchQA
GLM-5.1—
Muse Spark 1.389.4%
Source
Not directly comparable
AutomationBench
GLM-5.1—
Muse Spark 1.349.4%
Source
Not directly comparable
ApprenticeBench
GLM-5.1—
Muse Spark 1.319%
Source
Not directly comparable
CWE-bench v1
GLM-5.1—
Muse Spark 1.355.0%
Source
Not directly comparable
MazeBench
GLM-5.1—
Muse Spark 1.30%
Source
Not directly comparable

Coding

SWE-bench Pro
GLM-5.158.4%
Source
Muse Spark 1.3—
Not directly comparable
NL2Repo
GLM-5.142.7%
Source
Muse Spark 1.3—
Not directly comparable
SWE-Rebench
GLM-5.162.7%
Source
Muse Spark 1.3—
Not directly comparable
Terminal-Bench 2.0
GLM-5.163.5%
Source
Muse Spark 1.3—
Not directly comparable
Vibe Code Bench
GLM-5.131.46%
Source
Muse Spark 1.3—
Not directly comparable
OpenHarmony Bench
GLM-5.152.3%
Source
Muse Spark 1.3—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%
Source
Muse Spark 1.3—
Not directly comparable
SWE-bench (Vals)
GLM-5.176.4%
Source
Muse Spark 1.3—
Not directly comparable
Terminal-Bench 2.1
GLM-5.1—
Muse Spark 1.388.8%
Source
Not directly comparable
DeepSWE
GLM-5.1—
Muse Spark 1.375.4%
Source
Not directly comparable
SWE-Atlas Codebase QnA
GLM-5.1—
Muse Spark 1.359.4%
Source
Not directly comparable
CursorBench 4.0
GLM-5.1—
Muse Spark 1.341.6%
Source
Not directly comparable
Bug Hunt Bench
GLM-5.1—
Muse Spark 1.332.2 fixes
Source
Not directly comparable
FrontierSWE v2
GLM-5.1—
Muse Spark 1.325.8%
Source
Not directly comparable

Reasoning

MRCR v2 256K-512K
GLM-5.1—
Muse Spark 1.398.5%
Source
Not directly comparable
MRCR v2 512K-1M
GLM-5.1—
Muse Spark 1.398.1%
Source
Not directly comparable

Knowledge

GPQA-D
GLM-5.186.2%
Source
Muse Spark 1.3—
Not directly comparable
HLE
GLM-5.131%
Source
Muse Spark 1.3—
Not directly comparable
GPQA Diamond (Vals)
GLM-5.184.5%
Source
Muse Spark 1.3—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.186.9%
Source
Muse Spark 1.3—
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
GLM-5.1—
Muse Spark 1.315.9%
Source
Not directly comparable

Math

AIME26
GLM-5.195.3%
Source
Muse Spark 1.3—
Not directly comparable
HMMT Nov 2025
GLM-5.194.0%
Source
Muse Spark 1.3—
Not directly comparable
HMMT Feb 2026
GLM-5.182.6%
Source
Muse Spark 1.3—
Not directly comparable
MMAnswerBench
GLM-5.183.8%
Source
Muse Spark 1.3—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared source
GLM-5.136.842%
Muse Spark 1.374.035%
Muse Spark 1.3 leads this result
FrontierMath v2 (Tier 4)
GLM-5.1—
Muse Spark 1.346.341%
Source
Not directly comparable

45 public results · 2 shared

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Last updated October 17, 2026

GLM-5.1 vs Muse Spark 1.3

Decision reading

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

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

If you already use one of these models

Questions

Which is better, GLM-5.1 or Muse Spark 1.3?

Which is better for coding, GLM-5.1 or Muse Spark 1.3?

Which is better for agentic tasks, GLM-5.1 or Muse Spark 1.3?

Which costs less, GLM-5.1 or Muse Spark 1.3?

Which has the larger context window, GLM-5.1 or Muse Spark 1.3?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Instruction following

Math

Related comparisons

Watch GLM-5.1 vs Muse Spark 1.3