Agentic
Like-for-like- GLM-5.3
- 68.5
- Supported · #8/153
- GPT-5.2
- 42.8
- Supported · #105/153
- Basis
- BenchAlign lane · 9 vs 4 public rows
- Reading
- GLM-5.3 leads
GLM-5.3 vs GPT-5.2
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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-5.3
GLM-5.3 leads on the public coding lane, 61.5 to 46.5, 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-5.3
GLM-5.3 leads on the public agentic lane, 68.5 to 42.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GLM-5.3
GLM-5.3 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.
24 GLM-5.3 only
15 GPT-5.2 only
- Shared results
- 0
- GLM-5.3 only
- 24
- GPT-5.2 only
- 15
- Like-for-like categories
- 3 / 8
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-5.3
- 61.5
- Supported · #18/152
- GPT-5.2
- 46.5
- Supported · #82/152
- Basis
- BenchAlign lane · 13 vs 3 public rows
- Reading
- GLM-5.3 leads · intervals overlap
Knowledge
Like-for-like- GLM-5.3
- 61.8
- Supported · #29/183
- GPT-5.2
- 61.7
- Supported · #30/183
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Practical tie
Reasoning
Not comparable- GLM-5.3
- 75.8
- #10/20
- GPT-5.2
- 53.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.3
- Not ranked
- GPT-5.2
- 57.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.3
- Not ranked
- GPT-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.3
- Not ranked
- GPT-5.2
- 66.3
- #23/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.3
- Not ranked
- GPT-5.2
- 92.6
- #14/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.3 | GPT-5.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 68.5Supported · #8/153 | 42.8Supported · #105/153 | Like-for-likeBenchAlign lane · 9 vs 4 public rows | GLM-5.3 leads |
| Coding | 61.5Supported · #18/152 | 46.5Supported · #82/152 | Like-for-likeBenchAlign lane · 13 vs 3 public rows | GLM-5.3 leads · intervals overlap |
| Knowledge | 61.8Supported · #29/183 | 61.7Supported · #30/183 | Like-for-likeBenchAlign lane · 2 vs 1 public rows | Practical tie |
| Reasoning | 75.8#10/20 | 53.7Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 57.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 66.3#23/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.6#14/123 | Not comparableProvisional lane · 0 vs 0 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.
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-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.2
- $0.00875
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.2
- $0.1295
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- GPT-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3 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-5.3GPT-5.2
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.3
GPT-5.2
400K
API model ID
GLM-5.3
zai-org/GLM-5.3
Z.AI GLM-5.3 model cardGPT-5.2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.3
No comparable hosted API rate
Z.AI GLM-5.3 model cardGPT-5.2
Not published
Documented inputs
GLM-5.3
Not sourced
GPT-5.2
Not sourced
Documented outputs
GLM-5.3
Not sourced
GPT-5.2
Not sourced
Provider availability
GLM-5.3
Not sourced
GPT-5.2
Not sourced
Reasoning profile
GLM-5.3
Reasoning
GPT-5.2
Reasoning
Weight access
GLM-5.3
Open Weight
GPT-5.2
Proprietary
License
GLM-5.3
Open Weight
GPT-5.2
Proprietary
Release date
GLM-5.3
2026-08-14
GPT-5.2
2025-12-11
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GLM-5.3 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 evidence39 rows
Agentic
Terminal-Bench 2.1
GLM-5.388.2%SourceGPT-5.2—Not directly comparable
terminalBench3
GLM-5.328.3%SourceGPT-5.2—Not directly comparable
CyberGym
GLM-5.384.5%SourceGPT-5.2—Not directly comparable
ExploitGym
GLM-5.315.0%SourceGPT-5.2—Not directly comparable
Toolathlon-Verified
GLM-5.373.0%SourceGPT-5.2—Not directly comparable
AutomationBench
GLM-5.348.2%SourceGPT-5.2—Not directly comparable
Agents' Last Exam
GLM-5.328.5%SourceGPT-5.2—Not directly comparable
HLE w/ tools
GLM-5.362.5%SourceGPT-5.2—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.371.5%SourceGPT-5.2—Not directly comparable
BrowseComp
GLM-5.3—GPT-5.265.8%SourceNot directly comparable
OSWorld-Verified
GLM-5.3—GPT-5.247.3%SourceNot directly comparable
Gert Labs
GLM-5.3—GPT-5.246.54%SourceNot directly comparable
JobBench
GLM-5.3—GPT-5.234.3%SourceNot directly comparable
Coding
Terminal-Bench 2.1
GLM-5.388.2%SourceGPT-5.2—Not directly comparable
terminalBench3
GLM-5.328.3%SourceGPT-5.2—Not directly comparable
DeepSWE
GLM-5.366.9%SourceGPT-5.2—Not directly comparable
NL2Repo
GLM-5.358%SourceGPT-5.2—Not directly comparable
ProgramBench
GLM-5.319.0%SourceGPT-5.2—Not directly comparable
FrontierSWE
GLM-5.378.1%SourceGPT-5.2—Not directly comparable
sweMarathon
GLM-5.342.5%SourceGPT-5.2—Not directly comparable
PostTrain Bench
GLM-5.339.8%SourceGPT-5.2—Not directly comparable
VulcanBench v3
GLM-5.378.3%SourceGPT-5.2—Not directly comparable
OpenHarmony Bench
GLM-5.360.8%SourceGPT-5.2—Not directly comparable
FrontierSWE v2
GLM-5.330.2%SourceGPT-5.2—Not directly comparable
LiveCodeBench (Vals)
GLM-5.380.5%SourceGPT-5.2—Not directly comparable
SWE-bench (Vals)
GLM-5.395.4%SourceGPT-5.2—Not directly comparable
SWE-bench Verified
GLM-5.3—GPT-5.280%SourceNot directly comparable
SWE-bench Pro
GLM-5.3—GPT-5.255.6%SourceNot directly comparable
Vibe Code Bench
GLM-5.3—GPT-5.253.50%SourceNot directly comparable
Reasoning
ARC-AGI-2
GLM-5.3—GPT-5.252.9%SourceNot directly comparable
Knowledge
Math
Multimodal
Frequently asked questions
Which is better, GLM-5.3 or GPT-5.2?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5.3 or GPT-5.2?
GLM-5.3 leads the public coding lane, 61.5 to 46.5, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GLM-5.3 or GPT-5.2?
GLM-5.3 leads the public agentic tasks lane, 68.5 to 42.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GLM-5.3 or GPT-5.2?
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-5.3 or GPT-5.2?
GLM-5.3 has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 14, 2026
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