Coding work
Code generation, repair, and software-engineering tasks
GLM-5.3
GLM-5.3 leads on the public coding lane, 56.7 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GLM-5.2 vs GLM-5.3
Updated September 28, 2026. Rank says GLM-5.3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GLM-5.3 has the higher public score estimate, 65.44 versus 62.62, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 10
- GLM-5.2 only
- 15
- GLM-5.3 only
- 14
- Like-for-like categories
- 3 / 8
Supported: GLM-5.2 · Estimated: GLM-5.3How the comparison works
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-5.3
GLM-5.3 leads on the public agentic lane, 67.3 to 55.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Show secondary and unsupported calls
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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-ratesCache-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-fallbackRepository 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
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.
56.1GLM-5.256.7GLM-5.3
Like-for-like · BenchAlign v5.7
GLM-5.3 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.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
15 GLM-5.2 only
10 Shared
14 GLM-5.3 only
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.
LiveCodeBench (Vals)Coding
Normalized gap 11.0GLM-5.2:69.5%GLM-5.3:80.5%MMLU-Pro (Vals)Knowledge
Normalized gap 0.1GLM-5.2:86.7%GLM-5.3:86.8%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
- 55.9
- Supported · #26/111
- GLM-5.3
- 67.3
- Supported · #9/111
- Basis
- BenchAlign v5.7 lane · 6 vs 9 public rows
- Reading
- GLM-5.3 leads · intervals overlap
Coding
Like-for-like- GLM-5.2
- 56.1
- Supported · #23/136
- GLM-5.3
- 56.7
- Supported · #22/136
- Basis
- BenchAlign v5.7 lane · 8 vs 13 public rows
- Reading
- GLM-5.3 leads · intervals overlap
Knowledge
Like-for-like- GLM-5.2
- 57.2
- Supported · #42/160
- GLM-5.3
- 61.9
- Supported · #33/160
- Basis
- BenchAlign v5.7 lane · 6 vs 2 public rows
- Reading
- GLM-5.3 leads · intervals overlap
Reasoning
Not comparable- GLM-5.2
- 76.1
- Unranked · 2 rankable rows
- GLM-5.3
- 77.1
- #11/27
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not ranked
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not ranked
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- 88.5
- #23/124
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.2 | GLM-5.3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.9Supported · #26/111 | 67.3Supported · #9/111 | Like-for-likeBenchAlign v5.7 lane · 6 vs 9 public rows | GLM-5.3 leads · intervals overlap |
| Coding | 56.1Supported · #23/136 | 56.7Supported · #22/136 | Like-for-likeBenchAlign v5.7 lane · 8 vs 13 public rows | GLM-5.3 leads · intervals overlap |
| Knowledge | 57.2Supported · #42/160 | 61.9Supported · #33/160 | Like-for-likeBenchAlign v5.7 lane · 6 vs 2 public rows | GLM-5.3 leads · intervals overlap |
| Reasoning | 76.1Unranked · 2 rankable rows | 77.1#11/27 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 88.5#23/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 80.7Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.2
- $0.0832
- Fits in one request
- GLM-5.3
- Self-hosted; infrastructure cost varies
- 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-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
- GLM-5.3
- 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. GLM-5.3 has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.2GLM-5.3
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
GLM-5.3
API model ID
GLM-5.2
Not sourced
GLM-5.3
zai-org/GLM-5.3
Z.AI GLM-5.3 model cardCached-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
GLM-5.3
No comparable hosted API rate
Z.AI GLM-5.3 model cardDocumented inputs
GLM-5.2
Not sourced
GLM-5.3
Not sourced
Documented outputs
GLM-5.2
Not sourced
GLM-5.3
Not sourced
Provider availability
GLM-5.2
Not sourced
GLM-5.3
Not sourced
Reasoning profile
GLM-5.2
Reasoning
GLM-5.3
Reasoning
Weight access
GLM-5.2
Open Weight
GLM-5.3
Open Weight
License
GLM-5.2
Open Weight
GLM-5.3
Open Weight
Release date
GLM-5.2
2026-06-16
GLM-5.3
2026-08-14
If you are considering the documented upgrade path
- Deployment change
- Both entries list Z.AI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GLM-5.3 has the higher public score estimate, 65.44 versus 62.62, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5.2 or GLM-5.3?
GLM-5.3 has the higher public score estimate, 65.44 versus 62.62, 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 GLM-5.3?
GLM-5.3 leads the public coding lane, 56.7 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GLM-5.2 or GLM-5.3?
GLM-5.3 leads the public agentic tasks lane, 67.3 to 55.9, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, GLM-5.2 or GLM-5.3?
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 GLM-5.3?
Both models list the same context window, 1M.
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 3.0
GLM-5.24.6%SourceGLM-5.3—Not directly comparable
Terminal-Bench 2.1
GLM-5.281.0%SourceGLM-5.388.2%SourceGLM-5.3 leads this result
MCP Atlas
GLM-5.276.8%SourceGLM-5.3—Not directly comparable
Toolathlon
GLM-5.248.2%SourceGLM-5.3—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceGLM-5.3—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%SourceGLM-5.371.5%SourceGLM-5.3 leads this result
terminalBench3
GLM-5.2—GLM-5.328.3%SourceNot directly comparable
CyberGym
GLM-5.2—GLM-5.384.5%SourceNot directly comparable
ExploitGym
GLM-5.2—GLM-5.315.0%SourceNot directly comparable
Toolathlon-Verified
GLM-5.2—GLM-5.373.0%SourceNot directly comparable
AutomationBench
GLM-5.2—GLM-5.348.2%SourceNot directly comparable
Agents' Last Exam
GLM-5.2—GLM-5.328.5%SourceNot directly comparable
HLE w/ tools
GLM-5.2—GLM-5.362.5%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceGLM-5.3—Not directly comparable
NL2Repo
GLM-5.248.9%SourceGLM-5.358%SourceGLM-5.3 leads this result
Terminal-Bench 2.1
GLM-5.281.0%SourceGLM-5.388.2%SourceGLM-5.3 leads this result
ProgramBench
GLM-5.263.7%SourceGLM-5.319.0%SourceGLM-5.2 leads this result
cursorBench32
GLM-5.255.0%SourceGLM-5.3—Not directly comparable
OpenHarmony Bench
Shared sourceGLM-5.258.4%GLM-5.360.8%GLM-5.3 leads this result
LiveCodeBench (Vals)
GLM-5.269.5%SourceGLM-5.380.5%SourceGLM-5.3 leads this result
SWE-bench (Vals)
GLM-5.282.8%SourceGLM-5.395.4%SourceGLM-5.3 leads this result
terminalBench3
GLM-5.2—GLM-5.328.3%SourceNot directly comparable
DeepSWE
GLM-5.2—GLM-5.366.9%SourceNot directly comparable
FrontierSWE
GLM-5.2—GLM-5.378.1%SourceNot directly comparable
sweMarathon
GLM-5.2—GLM-5.342.5%SourceNot directly comparable
PostTrain Bench
GLM-5.2—GLM-5.339.8%SourceNot directly comparable
VulcanBench v3
GLM-5.2—GLM-5.378.3%SourceNot directly comparable
FrontierSWE v2
GLM-5.2—GLM-5.330.2%SourceNot directly comparable
Reasoning
CritPt
GLM-5.220.9%SourceGLM-5.3—Not directly comparable
Knowledge
GPQA
GLM-5.291.2%SourceGLM-5.3—Not directly comparable
GPQA-D
GLM-5.291.2%SourceGLM-5.3—Not directly comparable
HLE
GLM-5.254.7%SourceGLM-5.3—Not directly comparable
HLE w/o tools
GLM-5.240.5%SourceGLM-5.3—Not directly comparable
GPQA Diamond (Vals)
GLM-5.285.6%SourceGLM-5.388.1%SourceGLM-5.3 leads this result
MMLU-Pro (Vals)
GLM-5.286.7%SourceGLM-5.386.8%SourceGLM-5.3 leads this result
Math
39 public results · 10 shared
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Last updated September 28, 2026