Updated October 10, 2026. Rank says GLM-5.2 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
GLM-5.2 has the higher public point estimate, 61.55 versus 57.36. Their conditional score ranges overlap. These ranges do not establish rank confidence. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
GLM-5.2
GLM-5.2 has the higher public coding point estimate, 54.5 to 49.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 has the higher public agentic point estimate, 56.3 to 55.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
GLM-5.2 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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 11.0MMLU-Pro (Vals)Knowledge
Normalized gap 0.6Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | GLM-5.2 | GLM-5.3-Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 56.3Supported · #33/123 | 55.7Supported · #34/123 | Like-for-likeBenchAlign v5.8 lane · 7 vs 6 public rows | GLM-5.2 leads · intervals overlap |
| Coding | 54.5Supported · #31/146 | 49.3Supported · #42/146 | Like-for-likeBenchAlign v5.8 lane · 9 vs 8 public rows | GLM-5.2 leads · intervals overlap |
| Knowledge | 57.9Supported · #49/177 | 60.5Supported · #43/177 | Like-for-likeBenchAlign v5.8 lane · 6 vs 2 public rows | GLM-5.3-Flash leads · intervals overlap |
| Reasoning | 79.9Unranked · 2 rankable rows | 81.1#11/28 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 84.0#14/54 | 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 |
| Instruction following | 89.9#26/127 | 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 v5.8) 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.
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.
1K fresh input + 500 output tokens
GLM-5.3-Flash has no comparable published API token rate.
50K fresh input + 3K output tokens
GLM-5.3-Flash has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3-Flash has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
GLM-5.3-Flash
GLM-5.2
Not sourced
GLM-5.3-Flash
glm-5.3-flash
GLM-5.3-Flash model cardA 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-Flash
No comparable hosted API rate
GLM-5.3-Flash model cardGLM-5.2
Not sourced
GLM-5.3-Flash
Not sourced
GLM-5.2
Not sourced
GLM-5.3-Flash
Not sourced
GLM-5.2
Not sourced
GLM-5.3-Flash
Not sourced
GLM-5.2
Reasoning
GLM-5.3-Flash
Reasoning
GLM-5.2
Open Weight
GLM-5.3-Flash
Open Weight
GLM-5.2
Open Weight
GLM-5.3-Flash
Open Weight
GLM-5.2
2026-06-16
GLM-5.3-Flash
2026-08-26
GLM-5.2 has the higher public point estimate, 61.55 versus 57.36. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GLM-5.2 has the higher public coding point estimate, 54.5 to 49.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GLM-5.2 has the higher public agentic tasks point estimate, 56.3 to 55.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
GLM-5.3-Flash leads this result
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.2 leads this result
HLE w/ tools
GLM-5.3-Flash leads this result
Toolathlon-Verified
Not directly comparable
AutomationBench
Not directly comparable
Agents' Last Exam
Not directly comparable
SWE-bench Pro
Not directly comparable
NL2Repo
GLM-5.3-Flash leads this result
Terminal-Bench 2.1
GLM-5.3-Flash leads this result
ProgramBench
Not directly comparable
CursorBench 3.2
Not directly comparable
OpenHarmony Bench
Shared sourceGLM-5.2 leads this result
LiveCodeBench (Vals)
GLM-5.3-Flash leads this result
SWE-bench (Vals)
GLM-5.3-Flash leads this result
PostTrainBench v1.1
Not directly comparable
DeepSWE
Not directly comparable
FrontierSWE v2
Not directly comparable
Bug Hunt Bench
Not directly comparable
CritPt
Not directly comparable
OfficeQA Pro
Not directly comparable
CharXiv
Not directly comparable
Chartography (tools)
Not directly comparable
BabyVision
Not directly comparable
MMVU
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
GLM-5.3-Flash leads this result
MMLU-Pro (Vals)
GLM-5.2 leads this result
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Last updated October 10, 2026
