Agentic
Like-for-like- GLM-5.2
- 81.0
- Qwen3.6-27B
- 59.3
- Weighted basis
- 1 vs 1 rows
- Reading
- GLM-5.2 leads
Model comparison
GLM-5.2 vs Qwen3.6-27B
Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
20 confirmed releases in the last 30 daystrack changes
GLM-5.2 has the higher public score estimate, 62.94 versus 52.81, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
10 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.
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the same 1 weighted benchmark row.
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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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: 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.
8 GLM-5.2 only
10 Shared
28 Qwen3.6-27B only
- Shared results
- 10
- GLM-5.2 only
- 8
- Qwen3.6-27B only
- 28
- Like-for-like categories
- 2 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Math
Like-for-like- GLM-5.2
- 95.9
- Qwen3.6-27B
- 89.2
- Weighted basis
- 2 vs 2 rows
- Reading
- GLM-5.2 leads
Coding
Directional only- GLM-5.2
- 62.1
- Qwen3.6-27B
- 77.5
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5.2
- 59.6
- Qwen3.6-27B
- 53.3
- Weighted basis
- 2 vs 4 rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- Not measured
- Qwen3.6-27B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not measured
- Qwen3.6-27B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not measured
- Qwen3.6-27B
- 76.7
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- Not measured
- Qwen3.6-27B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.2 | Qwen3.6-27B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 81.0 | 59.3 | Like-for-like1 vs 1 rows | GLM-5.2 leads |
| Math | 95.9 | 89.2 | Like-for-like2 vs 2 rows | GLM-5.2 leads |
| Coding | 62.1 | 77.5 | Directional only1 vs 3 rows | Directional only |
| Knowledge | 59.6 | 53.3 | Directional only2 vs 4 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 76.7 | 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.
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.
HLE
Knowledge
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
HMMT Feb 2026
Math
AIME26
Math
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
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.6-27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.2
- $0.0832
- Fits in one request
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.6-27B 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
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- 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. Qwen3.6-27B 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-5.2Qwen3.6-27B
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Qwen3.6-27B
262K
API model ID
GLM-5.2
Not sourced
Qwen3.6-27B
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
Qwen3.6-27B
No comparable hosted API rate
Documented inputs
GLM-5.2
Not sourced
Qwen3.6-27B
Not sourced
Documented outputs
GLM-5.2
Not sourced
Qwen3.6-27B
Not sourced
Provider availability
GLM-5.2
Not sourced
Qwen3.6-27B
Not sourced
Reasoning profile
GLM-5.2
Reasoning
Qwen3.6-27B
Reasoning
Weight access
GLM-5.2
Open Weight
Qwen3.6-27B
Open Weight
License
GLM-5.2
Open Weight
Qwen3.6-27B
Open Weight
Release date
GLM-5.2
2026-06-16
Qwen3.6-27B
2026-04-21
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, 62.94 versus 52.81, 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.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GLM-5.2
API / mo$4,350
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
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 evidence46 rows
Agentic
Terminal-Bench 2.0
GLM-5.281%SourceQwen3.6-27B59.3%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceQwen3.6-27B—Not directly comparable
Toolathlon
GLM-5.248.2%SourceQwen3.6-27B—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceQwen3.6-27B—Not directly comparable
Claw-Eval
GLM-5.2—Qwen3.6-27B72.4%SourceNot directly comparable
QwenClawBench
GLM-5.2—Qwen3.6-27B53.4%SourceNot directly comparable
QwenWebBench
GLM-5.2—Qwen3.6-27B1487SourceNot directly comparable
AndroidWorld
GLM-5.2—Qwen3.6-27B70.3%SourceNot directly comparable
Gert Labs
GLM-5.2—Qwen3.6-27B54.84%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceQwen3.6-27B53.5%SourceGLM-5.2 leads this result
NL2Repo
GLM-5.248.9%SourceQwen3.6-27B36.2%SourceGLM-5.2 leads this result
Terminal-Bench 2.0
GLM-5.281.0%SourceQwen3.6-27B59.3%SourceGLM-5.2 leads this result
ProgramBench
GLM-5.263.7%SourceQwen3.6-27B—Not directly comparable
cursorBench32
GLM-5.255.0%SourceQwen3.6-27B—Not directly comparable
SWE-bench Verified
GLM-5.2—Qwen3.6-27B77.2%SourceNot directly comparable
SWE Multilingual
GLM-5.2—Qwen3.6-27B71.3%SourceNot directly comparable
LiveCodeBench
GLM-5.2—Qwen3.6-27B83.9%SourceNot directly comparable
Reasoning
CritPt
GLM-5.220.9%SourceQwen3.6-27B—Not directly comparable
Knowledge
GPQA
GLM-5.291.2%SourceQwen3.6-27B87.8%SourceGLM-5.2 leads this result
GPQA-D
GLM-5.291.2%SourceQwen3.6-27B—Not directly comparable
HLE
GLM-5.254.7%SourceQwen3.6-27B24%SourceGLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%SourceQwen3.6-27B—Not directly comparable
MMLU-Pro
GLM-5.2—Qwen3.6-27B86.2%SourceNot directly comparable
MMLU-Redux
GLM-5.2—Qwen3.6-27B93.5%SourceNot directly comparable
SuperGPQA
GLM-5.2—Qwen3.6-27B66%SourceNot directly comparable
C-Eval
GLM-5.2—Qwen3.6-27B91.4%SourceNot directly comparable
Math
AIME26
GLM-5.299.2%SourceQwen3.6-27B94.1%SourceGLM-5.2 leads this result
HMMT Nov 2025
GLM-5.294.4%SourceQwen3.6-27B90.7%SourceGLM-5.2 leads this result
HMMT Feb 2026
GLM-5.292.5%SourceQwen3.6-27B84.3%SourceGLM-5.2 leads this result
MMAnswerBench
GLM-5.291.0%SourceQwen3.6-27B80.8%SourceGLM-5.2 leads this result
HMMT Feb 2025
GLM-5.2—Qwen3.6-27B93.8%SourceNot directly comparable
Multimodal
MMMU
GLM-5.2—Qwen3.6-27B82.9%SourceNot directly comparable
MMMU-Pro
GLM-5.2—Qwen3.6-27B75.8%SourceNot directly comparable
RealWorldQA
GLM-5.2—Qwen3.6-27B84.1%SourceNot directly comparable
DynaMath
GLM-5.2—Qwen3.6-27B85.6%SourceNot directly comparable
MStar
GLM-5.2—Qwen3.6-27B81.4%SourceNot directly comparable
SimpleVQA
GLM-5.2—Qwen3.6-27B56.1%SourceNot directly comparable
CharXiv
GLM-5.2—Qwen3.6-27B78.4%SourceNot directly comparable
CC-OCR
GLM-5.2—Qwen3.6-27B81.2%SourceNot directly comparable
CountBench
GLM-5.2—Qwen3.6-27B97.8%SourceNot directly comparable
RefCOCO (avg)
GLM-5.2—Qwen3.6-27B92.5%SourceNot directly comparable
ERQA
GLM-5.2—Qwen3.6-27B62.5%SourceNot directly comparable
Video-MME (with subtitle)
GLM-5.2—Qwen3.6-27B87.7%SourceNot directly comparable
VideoMMMU
GLM-5.2—Qwen3.6-27B84.4%SourceNot directly comparable
MLVU (M-Avg)
GLM-5.2—Qwen3.6-27B86.6%SourceNot directly comparable
V*
GLM-5.2—Qwen3.6-27B94.7%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.2 or Qwen3.6-27B?
GLM-5.2 has the higher public score estimate, 62.94 versus 52.81, 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 Qwen3.6-27B?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5.2 or Qwen3.6-27B?
GLM-5.2 leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, GLM-5.2 or Qwen3.6-27B?
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 Qwen3.6-27B?
GLM-5.2 has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated July 31, 2026
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