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BenchLM

GLM-5.2 vs Muse Spark 1.2

Decision reading

Muse Spark 1.2 has the higher public score estimate, 70.28 versus 66.68, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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

Z.AI logo
Model A
GLM-5.2

Z.AI

66.68/100

Supported · Public rank #32

90% interval 59.4–74.0

Meta logo
Model B
Muse Spark 1.2

Meta

70.28/100

Estimated · Public rank #16

90% interval 58.9–76.0

Updated September 18, 2026. Rank says Muse Spark 1.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GLM-5.2

    GLM-5.2 leads on the public coding lane, 60.8 to 60, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Agentic work

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

    Muse Spark 1.2

    Muse Spark 1.2 leads on the public agentic lane, 61 to 58.4, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Muse Spark 1.2

    Muse Spark 1.2 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

  • Cache-heavy agent loop cost

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

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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.

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

60.8GLM-5.260.0Muse Spark 1.2

Like-for-like · BenchAlign

GLM-5.2 leads the like-for-like coding row, although the 90% intervals overlap.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job

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
3
GLM-5.2 only
22
Muse Spark 1.2 only
5
Like-for-like categories
2 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Like-for-like
GLM-5.2
58.4
Supported · #31/154
Muse Spark 1.2
61.0
Supported · #16/154
Basis
BenchAlign lane · 6 vs 2 public rows
Reading
Muse Spark 1.2 leads · intervals overlap

Coding

Like-for-like
GLM-5.2
60.8
Supported · #19/154
Muse Spark 1.2
60.0
Supported · #21/154
Basis
BenchAlign lane · 8 vs 5 public rows
Reading
GLM-5.2 leads · intervals overlap

Knowledge

Directional only
GLM-5.2
60.3
Supported · #37/184
Muse Spark 1.2
70.7
Estimated · #10/184
Basis
BenchAlign lane · 6 vs 1 public rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.2
74.8
Unranked · 2 rankable rows
Muse Spark 1.2
75.3
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5.2
80.7
Unranked · 4 rankable rows
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not ranked
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not ranked
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.2
88.5
#23/124
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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-5.2
$0.0036
Fits in one request
Muse Spark 1.2
$0.00338
Fits in one request

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5.2
$0.0832
Fits in one request
Muse Spark 1.2
$0.07525
Fits in one request

Muse Spark 1.2 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-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate
Muse Spark 1.2
$0.0975
Fits in one request

Muse Spark 1.2 has the lower modeled cost

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

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

GLM-5.2

Not published

Muse Spark 1.2

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.2 model page

Documented inputs

GLM-5.2

Not sourced

Muse Spark 1.2

Not sourced

Documented outputs

GLM-5.2

Not sourced

Muse Spark 1.2

Not sourced

Provider availability

GLM-5.2

Not sourced

Muse Spark 1.2

Not sourced

Reasoning profile

GLM-5.2

Reasoning

Muse Spark 1.2

Reasoning

Weight access

GLM-5.2

Open Weight

Muse Spark 1.2

Proprietary

License

GLM-5.2

Open Weight

Muse Spark 1.2

Proprietary

Release date

GLM-5.2

2026-06-16

Muse Spark 1.2

2026-08-05

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 1.2 has the higher public score estimate, 70.28 versus 66.68, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0832 vs $0.07525. Cache-heavy agent loop: $0.352 vs $0.0975.
Context tradeoff
Both models list 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 evidence30 rows

Agentic

  • Terminal-Bench 3.0

    GLM-5.24.6%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MCP Atlas

    GLM-5.276.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Toolathlon

    GLM-5.248.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ResearchClawBench

    GLM-5.220.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5.267.8%
    Source
    Muse Spark 1.269.7%
    Source

    Muse Spark 1.2 leads this result

  • Terminal-Bench 2.1

    GLM-5.2—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.262.1%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • NL2Repo

    GLM-5.248.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281.0%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ProgramBench

    GLM-5.263.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • cursorBench32

    GLM-5.255.0%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • OpenHarmony Bench

    GLM-5.258.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5.269.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • SWE-bench (Vals)

    GLM-5.282.8%
    Source
    Muse Spark 1.286.6%
    Source

    Muse Spark 1.2 leads this result

  • Terminal-Bench 2.1

    GLM-5.2—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

  • DeepSWE

    GLM-5.2—
    Muse Spark 1.259.3%
    Source

    Not directly comparable

  • VulcanBench v3

    GLM-5.2—
    Muse Spark 1.287.0%
    Source

    Not directly comparable

  • FrontierSWE v2

    GLM-5.2—
    Muse Spark 1.212.0%
    Source

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.220.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

Knowledge

  • GPQA

    GLM-5.291.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • GPQA-D

    GLM-5.291.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HLE

    GLM-5.254.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HLE w/o tools

    GLM-5.240.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-5.285.6%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-5.286.7%
    Source
    Muse Spark 1.288.3%
    Source

    Muse Spark 1.2 leads this result

Math

  • AIME26

    GLM-5.299.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.294.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.292.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MMAnswerBench

    GLM-5.291.0%
    Source
    Muse Spark 1.2—

    Not directly comparable

Questions

Which is better, GLM-5.2 or Muse Spark 1.2?

Muse Spark 1.2 has the higher public score estimate, 70.28 versus 66.68, 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-5.2 or Muse Spark 1.2?

GLM-5.2 leads the public coding lane, 60.8 to 60, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GLM-5.2 or Muse Spark 1.2?

Muse Spark 1.2 leads the public agentic tasks lane, 61 to 58.4, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, GLM-5.2 or Muse Spark 1.2?

For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00337 on Muse Spark 1.2; repository review costs $0.0832 and $0.07525; the cache-heavy agent loop costs $0.352 and $0.0975. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5.2 or Muse Spark 1.2?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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