Agentic
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- 59.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
GLM-5 (Reasoning) vs Muse Spark
Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Muse Spark has the higher public score estimate, 70.6 versus 60.29, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5 (Reasoning) does not fit this workload in one request. GLM-5 (Reasoning) 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.
1 Shared
23 Muse Spark only
- Shared results
- 1
- GLM-5 (Reasoning) only
- 0
- 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.
Coding
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- 67.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- 42.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- 50.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- 32.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- 82.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5 (Reasoning)
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5 (Reasoning) | 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 (Reasoning)
- $0.0026
- 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 (Reasoning)
- $0.0596
- 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 (Reasoning)
- $0.252
- 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 (Reasoning) does not fit this workload in one request. GLM-5 (Reasoning) 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 (Reasoning)Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
GLM-5 (Reasoning)
200K
Muse Spark
262K
API model ID
GLM-5 (Reasoning)
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 (Reasoning)
Not published
Muse Spark
No comparable hosted API rate
Documented inputs
GLM-5 (Reasoning)
Not sourced
Muse Spark
Not sourced
Documented outputs
GLM-5 (Reasoning)
Not sourced
Muse Spark
Not sourced
Provider availability
GLM-5 (Reasoning)
Not sourced
Muse Spark
Not sourced
Reasoning profile
GLM-5 (Reasoning)
Reasoning
Muse Spark
Reasoning
Weight access
GLM-5 (Reasoning)
Open Weight
Muse Spark
Proprietary
License
GLM-5 (Reasoning)
Open Weight
Muse Spark
Proprietary
Release date
GLM-5 (Reasoning)
2026-03-01
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 60.29, 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence24 rows
Agentic
Terminal-Bench 2.0
GLM-5 (Reasoning)—Muse Spark59%SourceNot directly comparable
τ²-bench results
GLM-5 (Reasoning)—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
GLM-5 (Reasoning)—Muse Spark74.8%SourceNot directly comparable
CyberGym
GLM-5 (Reasoning)—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
GLM-5 (Reasoning)—Muse Spark63.8%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceGLM-5 (Reasoning)23.36%Muse Spark19.67%GLM-5 (Reasoning) leads this result
SWE-bench Verified
GLM-5 (Reasoning)—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
GLM-5 (Reasoning)—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
GLM-5 (Reasoning)—Muse Spark80.0%SourceNot directly comparable
Reasoning
ARC-AGI-2
GLM-5 (Reasoning)—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA-D
GLM-5 (Reasoning)—Muse Spark89.5%SourceNot directly comparable
HLE
GLM-5 (Reasoning)—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
GLM-5 (Reasoning)—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
GLM-5 (Reasoning)—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
GLM-5 (Reasoning)—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
CharXiv
GLM-5 (Reasoning)—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
GLM-5 (Reasoning)—Muse Spark80.4%SourceNot directly comparable
ERQA
GLM-5 (Reasoning)—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
GLM-5 (Reasoning)—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
GLM-5 (Reasoning)—Muse Spark84.1%SourceNot directly comparable
ZeroBench
GLM-5 (Reasoning)—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
GLM-5 (Reasoning)—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5 (Reasoning) or Muse Spark?
Muse Spark has the higher public score estimate, 70.6 versus 60.29, 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 (Reasoning) 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 (Reasoning) 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 (Reasoning) 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 (Reasoning) or Muse Spark?
Muse Spark has the larger documented context window: 262K, compared with 200K.
Related comparisons
Last updated August 21, 2026
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