Updated October 10, 2026. Rank says GLM-5.3-Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
GLM-5.3-Flash has the higher public point estimate, 57.36 versus 53.87. Their conditional score ranges overlap. These ranges do not establish rank confidence. 5 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-5.3-Flash
GLM-5.3-Flash has the higher public coding point estimate, 49.3 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GLM-5.3-Flash
GLM-5.3-Flash has the higher public agentic point estimate, 55.7 to 34.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GLM-5.3-Flash
GLM-5.3-Flash has the larger documented context window.
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-5.3-Flash 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.
MMLU-Pro (Vals)Knowledge
Normalized gap 1.5LiveCodeBench (Vals)Coding
Normalized gap 1.0Each 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-5.3-Flash | GPT-5.4 mini | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.7Supported · #34/123 | 34.4Supported · #77/123 | Like-for-likeBenchAlign v5.8 lane · 6 vs 6 public rows | GLM-5.3-Flash leads |
| Coding | 49.3Supported · #42/146 | 35.8Supported · #73/146 | Like-for-likeBenchAlign v5.8 lane · 8 vs 5 public rows | GLM-5.3-Flash leads · intervals overlap |
| Knowledge | 60.5Supported · #43/177 | 50.4Supported · #74/177 | Like-for-likeBenchAlign v5.8 lane · 2 vs 5 public rows | GLM-5.3-Flash leads · intervals overlap |
| Multimodal | 84.0#14/54 | 73.8#28/54 | Directional onlyProvisional lane · 2 vs 1 weighted rows | Directional only |
| Reasoning | 81.1#11/28 | 43.8Unranked · 4 rankable rows | 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 |
| Instruction following | Not ranked | 89.9#27/127 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 44.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 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
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-5.3-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-5.3-Flash
GPT-5.4 mini
GLM-5.3-Flash
glm-5.3-flash
GLM-5.3-Flash model cardGPT-5.4 mini
gpt-5.4-mini
OpenAI GPT-5.4 mini model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.3-Flash
No comparable hosted API rate
GLM-5.3-Flash model cardGPT-5.4 mini
$0.075 per 1M cached input tokens
OpenAI pricingGLM-5.3-Flash
Not sourced
GPT-5.4 mini
text, image
OpenAI model catalogGLM-5.3-Flash
Not sourced
GPT-5.4 mini
GLM-5.3-Flash
Not sourced
GPT-5.4 mini
Generally Available · OpenAI Responses API
OpenAI model catalogGLM-5.3-Flash
Reasoning
GPT-5.4 mini
Reasoning
GLM-5.3-Flash
Open Weight
GPT-5.4 mini
Proprietary
GLM-5.3-Flash
Open Weight
GPT-5.4 mini
Proprietary
GLM-5.3-Flash
2026-08-26
GPT-5.4 mini
2026-03-17
GLM-5.3-Flash has the higher public point estimate, 57.36 versus 53.87. 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-5.3-Flash has the higher public coding point estimate, 49.3 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GLM-5.3-Flash has the higher public agentic tasks point estimate, 55.7 to 34.4, 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.
GLM-5.3-Flash has the larger documented context window: 1M, compared with 400K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
Toolathlon-Verified
Not directly comparable
AutomationBench
Not directly comparable
Agents' Last Exam
Not directly comparable
HLE w/ tools
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.3-Flash leads this result
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
DeepSWE
Not directly comparable
NL2Repo
Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 mini leads this result
SWE-bench (Vals)
GLM-5.3-Flash leads this result
OpenHarmony Bench
Not directly comparable
FrontierSWE v2
Not directly comparable
Bug Hunt Bench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
FrontierCode 1.1 Main
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
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
GPQA Diamond (Vals)
GLM-5.3-Flash leads this result
MMLU-Pro (Vals)
GLM-5.3-Flash leads this result
GPQA
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
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Last updated October 10, 2026
