Agentic
Like-for-like- GLM-5.2
- 58.5
- Supported · #29/153
- Muse Spark
- 56.1
- Supported · #35/153
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- GLM-5.2 leads · intervals overlap
GLM-5.2 vs Muse Spark
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5.2 has the higher public score estimate, 68.19 versus 67.77, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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, 61 to 59.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the public agentic lane, 58.5 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GLM-5.2
GLM-5.2 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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: 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.
20 GLM-5.2 only
5 Shared
19 Muse Spark only
- Shared results
- 5
- GLM-5.2 only
- 20
- Muse Spark only
- 19
- Like-for-like categories
- 3 / 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.
Coding
Like-for-like- GLM-5.2
- 61.0
- Supported · #19/152
- Muse Spark
- 59.1
- Supported · #25/152
- Basis
- BenchAlign lane · 8 vs 4 public rows
- Reading
- GLM-5.2 leads · intervals overlap
Knowledge
Like-for-like- GLM-5.2
- 60.7
- Supported · #35/183
- Muse Spark
- 65.4
- Supported · #24/183
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- Muse Spark leads · intervals overlap
Instruction following
Directional only- GLM-5.2
- 89.8
- #22/123
- Muse Spark
- 93.2
- #8/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- Muse Spark
- 45.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not ranked
- Muse Spark
- 77.5
- #14/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | GLM-5.2 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.5Supported · #29/153 | 56.1Supported · #35/153 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | GLM-5.2 leads · intervals overlap |
| Coding | 61.0Supported · #19/152 | 59.1Supported · #25/152 | Like-for-likeBenchAlign lane · 8 vs 4 public rows | GLM-5.2 leads · intervals overlap |
| Knowledge | 60.7Supported · #35/183 | 65.4Supported · #24/183 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | Muse Spark leads · intervals overlap |
| Instruction following | 89.8#22/123 | 93.2#8/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 74.8Unranked · 2 rankable rows | 45.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 80.7Unranked · 4 rankable rows | 55.3Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 77.5#14/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | 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.
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
HLE
Knowledge
HLE w/o tools
Knowledge
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
- 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-5.2
- $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-5.2
- $0.352
- Fits 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-5.2 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.
SpecificationGLM-5.2Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Muse Spark
262K
API model ID
GLM-5.2
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-5.2
Not published
Muse Spark
No comparable hosted API rate
Documented inputs
GLM-5.2
Not sourced
Muse Spark
Not sourced
Documented outputs
GLM-5.2
Not sourced
Muse Spark
Not sourced
Provider availability
GLM-5.2
Not sourced
Muse Spark
Not sourced
Reasoning profile
GLM-5.2
Reasoning
Muse Spark
Reasoning
Weight access
GLM-5.2
Open Weight
Muse Spark
Proprietary
License
GLM-5.2
Open Weight
Muse Spark
Proprietary
Release date
GLM-5.2
2026-06-16
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
- GLM-5.2 has the higher public score estimate, 68.19 versus 67.77, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GLM-5.2 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 evidence44 rows
Agentic
Terminal-Bench 3.0
GLM-5.24.6%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
GLM-5.281%SourceMuse Spark59%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceMuse Spark—Not directly comparable
Toolathlon
GLM-5.248.2%SourceMuse Spark—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%SourceMuse Spark—Not directly comparable
τ²-bench results
GLM-5.2—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
GLM-5.2—Muse Spark74.8%SourceNot directly comparable
CyberGym
GLM-5.2—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
GLM-5.2—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceMuse Spark52.4%SourceGLM-5.2 leads this result
NL2Repo
GLM-5.248.9%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceMuse Spark—Not directly comparable
ProgramBench
GLM-5.263.7%SourceMuse Spark—Not directly comparable
cursorBench32
GLM-5.255.0%SourceMuse Spark—Not directly comparable
OpenHarmony Bench
GLM-5.258.4%SourceMuse Spark—Not directly comparable
LiveCodeBench (Vals)
GLM-5.269.5%SourceMuse Spark—Not directly comparable
SWE-bench (Vals)
GLM-5.282.8%SourceMuse Spark—Not directly comparable
SWE-bench Verified
GLM-5.2—Muse Spark77.4%SourceNot directly comparable
LiveCodeBench Pro
GLM-5.2—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
GLM-5.2—Muse Spark19.67%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-5.291.2%SourceMuse Spark—Not directly comparable
GPQA-D
GLM-5.291.2%SourceMuse Spark89.5%SourceGLM-5.2 leads this result
HLE
GLM-5.254.7%SourceMuse Spark50.4%SourceGLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%SourceMuse Spark42.8%SourceMuse Spark leads this result
GPQA Diamond (Vals)
GLM-5.285.6%SourceMuse Spark—Not directly comparable
MMLU-Pro (Vals)
GLM-5.286.7%SourceMuse Spark—Not directly comparable
HealthBench Hard
GLM-5.2—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
GLM-5.2—Muse Spark52.6%SourceNot directly comparable
Math
AIME26
GLM-5.299.2%SourceMuse Spark—Not directly comparable
HMMT Nov 2025
GLM-5.294.4%SourceMuse Spark—Not directly comparable
HMMT Feb 2026
GLM-5.292.5%SourceMuse Spark—Not directly comparable
MMAnswerBench
GLM-5.291.0%SourceMuse Spark—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.2—Muse Spark39.000%SourceNot directly comparable
FrontierMath v2 (Tier 4)
GLM-5.2—Muse Spark14.600%SourceNot directly comparable
Multimodal
CharXiv
GLM-5.2—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
GLM-5.2—Muse Spark80.4%SourceNot directly comparable
ERQA
GLM-5.2—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
GLM-5.2—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
GLM-5.2—Muse Spark84.1%SourceNot directly comparable
ZeroBench
GLM-5.2—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
GLM-5.2—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.2 or Muse Spark?
GLM-5.2 has the higher public score estimate, 68.19 versus 67.77, 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?
GLM-5.2 leads the public coding lane, 61 to 59.1, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GLM-5.2 or Muse Spark?
GLM-5.2 leads the public agentic tasks lane, 58.5 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, GLM-5.2 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-5.2 or Muse Spark?
GLM-5.2 has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 14, 2026
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