Updated October 29, 2026. Rank says GLM-5.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GLM-5.2 has the higher public score estimate, 60.03 versus 46.15, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 12 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, 53.7 to 30.1, 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, 57.1 to 33.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GLM-5.2
GLM-5.2 has the larger documented context window.
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 17.1HLE w/o toolsKnowledge
Normalized gap 16.2LiveCodeBench (Vals)Coding
Normalized gap 14.5FrontierMath v2 (Tiers 1-3)Math
Normalized gap 14.3MMLU-Pro (Vals)Knowledge
Normalized gap 9.5Each 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 | GPT-5.4 nano | Basis | Reading |
|---|---|---|---|---|
| Agentic | 57.1Supported · #38/135 | 33.7Supported · #97/135 | Like-for-likeBenchAlign v5.8 lane · 8 vs 6 public rows | GLM-5.2 leads |
| Coding | 53.7Supported · #34/156 | 30.1Supported · #100/156 | Like-for-likeBenchAlign v5.8 lane · 9 vs 4 public rows | GLM-5.2 leads |
| Knowledge | 56.4Supported · #56/179 | 39.4Supported · #117/179 | Like-for-likeBenchAlign v5.8 lane · 6 vs 5 public rows | GLM-5.2 leads · intervals overlap |
| Instruction following | 89.9#29/157 | 93.3#12/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 78.8Unranked · 2 rankable rows | 40.6Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 52.6#51/58 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 78.4#3/12 | 52.3Unranked · 2 rankable rows | Not comparableProvisional lane · 4 vs 2 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
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input 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
GPT-5.4 nano
GLM-5.2
Not sourced
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.2
Not published
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingGLM-5.2
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogGLM-5.2
Not sourced
GPT-5.4 nano
GLM-5.2
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogGLM-5.2
Reasoning
GPT-5.4 nano
Reasoning
GLM-5.2
Open Weight
GPT-5.4 nano
Proprietary
GLM-5.2
Open Weight
GPT-5.4 nano
Proprietary
GLM-5.2
2026-06-16
GPT-5.4 nano
2026-03-17
GLM-5.2 has the higher public score estimate, 60.03 versus 46.15, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GLM-5.2 has the higher public coding point estimate, 53.7 to 30.1, 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, 57.1 to 33.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00082 on GPT-5.4 nano; repository review costs $0.0832 and $0.01375; the cache-heavy agent loop costs $0.352 and $0.0205. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
GLM-5.2 has the larger documented context window: 1M, compared with 400K.
Estimates at 50,000 req/day · 1000 tokens/req average.
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
Not directly comparable
MCP Atlas
GLM-5.2 leads this result
Toolathlon
GLM-5.2 leads this result
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.2 leads this result
HLE w/ tools
Not directly comparable
MazeBench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
τ²-bench results
Not directly comparable
SWE-bench Pro
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
ProgramBench
Not directly comparable
CursorBench 3.2
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 nano leads this result
SWE-bench (Vals)
GLM-5.2 leads this result
PostTrainBench v1.1
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
GPQA
GLM-5.2 leads this result
GPQA-D
Not directly comparable
HLE
GLM-5.2 leads this result
HLE w/o tools
GLM-5.2 leads this result
GPQA Diamond (Vals)
GLM-5.2 leads this result
MMLU-Pro (Vals)
GLM-5.2 leads this result
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGLM-5.2 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGLM-5.2 leads this result
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Last updated October 29, 2026
