Coding
Like-for-like- GLM-4.7
- 47.7
- Supported · #71/151
- GPT-5.4 mini
- 42.7
- Supported · #104/151
- Basis
- BenchAlign lane · 3 vs 4 public rows
- Reading
- GLM-4.7 leads · intervals overlap
GLM-4.7 vs GPT-5.4 mini
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.4 mini has the higher public score estimate, 61.11 versus 57.71, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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-4.7
GLM-4.7 leads on the public coding lane, 47.7 to 42.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.4 mini
GPT-5.4 mini has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-4.7 and GPT-5.4 mini are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
Confidence: listed-rates
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.
8 GLM-4.7 only
5 Shared
14 GPT-5.4 mini only
- Shared results
- 5
- GLM-4.7 only
- 8
- GPT-5.4 mini only
- 14
- Like-for-like categories
- 2 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Knowledge
Like-for-like- GLM-4.7
- 47.6
- Supported · #95/183
- GPT-5.4 mini
- 55.6
- Supported · #52/183
- Basis
- BenchAlign lane · 3 vs 5 public rows
- Reading
- GPT-5.4 mini leads · intervals overlap
Agentic
Directional only- GLM-4.7
- 45.3
- Estimated · #82/152
- GPT-5.4 mini
- 39.1
- Estimated · #119/152
- Basis
- BenchAlign lane · 4 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-4.7
- 82.8
- #50/123
- GPT-5.4 mini
- 89.8
- #23/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-4.7
- 69.8
- Unranked · 2 rankable rows
- GPT-5.4 mini
- 73.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-4.7
- 26.0
- Unranked · 2 rankable rows
- GPT-5.4 mini
- 44.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-4.7
- Not ranked
- GPT-5.4 mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-4.7
- Not ranked
- GPT-5.4 mini
- 57.2
- #31/48
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | GLM-4.7 | GPT-5.4 mini | Basis | Reading |
|---|---|---|---|---|
| Coding | 47.7Supported · #71/151 | 42.7Supported · #104/151 | Like-for-likeBenchAlign lane · 3 vs 4 public rows | GLM-4.7 leads · intervals overlap |
| Knowledge | 47.6Supported · #95/183 | 55.6Supported · #52/183 | Like-for-likeBenchAlign lane · 3 vs 5 public rows | GPT-5.4 mini leads · intervals overlap |
| Agentic | 45.3Estimated · #82/152 | 39.1Estimated · #119/152 | Directional onlyBenchAlign lane · 4 vs 6 public rows | Directional only |
| Instruction following | 82.8#50/123 | 89.8#23/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 69.8Unranked · 2 rankable rows | 73.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 26.0Unranked · 2 rankable rows | 44.5Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 57.2#31/48 | Not comparableProvisional lane · 0 vs 1 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.
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 (Tiers 1-3)
Math
Terminal-Bench 2.0
Agentic
HLE
Knowledge
GPQA
Knowledge
FrontierMath v2 (Tier 4)
Math
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-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.4 mini
- $0.003
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.4 mini
- $0.051
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- GPT-5.4 mini
- $0.075
- Fits in one request
GLM-4.7 does not fit this workload in one request. GLM-4.7 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-4.7GPT-5.4 mini
Context window
Maximum documented context; output-token limits may be lower.
GLM-4.7
200K
GPT-5.4 mini
API model ID
GLM-4.7
Not sourced
GPT-5.4 mini
gpt-5.4-mini
OpenAI GPT-5.4 mini model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-4.7
No comparable hosted API rate
GPT-5.4 mini
$0.075 per 1M cached input tokens
OpenAI pricingDocumented inputs
GLM-4.7
Not sourced
GPT-5.4 mini
text, image
OpenAI model catalogDocumented outputs
GLM-4.7
Not sourced
GPT-5.4 mini
Provider availability
GLM-4.7
Not sourced
GPT-5.4 mini
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GLM-4.7
Reasoning
GPT-5.4 mini
Reasoning
Weight access
GLM-4.7
Open Weight
GPT-5.4 mini
Proprietary
License
GLM-4.7
Open Weight
GPT-5.4 mini
Proprietary
Release date
GLM-4.7
2025-10-01
GPT-5.4 mini
2026-03-17
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
- GPT-5.4 mini has the higher public score estimate, 61.11 versus 57.71, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.4 mini has the larger documented window (400K).
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 evidence27 rows
Agentic
Terminal-Bench 2.0
GLM-4.741%SourceGPT-5.4 mini60%SourceGPT-5.4 mini leads this result
BrowseComp
GLM-4.752%SourceGPT-5.4 mini—Not directly comparable
VITA-Bench
GLM-4.715.5%SourceGPT-5.4 mini—Not directly comparable
Gert Labs
GLM-4.739.95%SourceGPT-5.4 mini—Not directly comparable
OSWorld-Verified
GLM-4.7—GPT-5.4 mini72.1%SourceNot directly comparable
MCP Atlas
GLM-4.7—GPT-5.4 mini57.7%SourceNot directly comparable
Toolathlon
GLM-4.7—GPT-5.4 mini42.9%SourceNot directly comparable
τ²-bench results
GLM-4.7—GPT-5.4 mini93.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GLM-4.7—GPT-5.4 mini54.7%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-4.773.8%SourceGPT-5.4 mini—Not directly comparable
LiveCodeBench
GLM-4.784.9%SourceGPT-5.4 mini—Not directly comparable
SWE-Rebench
GLM-4.758.7%SourceGPT-5.4 mini—Not directly comparable
Vibe Code Bench
GLM-4.7—GPT-5.4 mini47.97%SourceNot directly comparable
FrontierCode 1.1 Main
GLM-4.7—GPT-5.4 mini27.0%SourceNot directly comparable
LiveCodeBench (Vals)
GLM-4.7—GPT-5.4 mini81.5%SourceNot directly comparable
SWE-bench (Vals)
GLM-4.7—GPT-5.4 mini73.0%SourceNot directly comparable
Knowledge
GPQA
GLM-4.785.7%SourceGPT-5.4 mini88%SourceGPT-5.4 mini leads this result
MMLU-Pro
GLM-4.784.3%SourceGPT-5.4 mini—Not directly comparable
HLE
GLM-4.724.8%SourceGPT-5.4 mini41.5%SourceGPT-5.4 mini leads this result
HLE w/o tools
GLM-4.7—GPT-5.4 mini28.2%SourceNot directly comparable
GPQA Diamond (Vals)
GLM-4.7—GPT-5.4 mini83.1%SourceNot directly comparable
MMLU-Pro (Vals)
GLM-4.7—GPT-5.4 mini84.6%SourceNot directly comparable
Math
AIME 2025
GLM-4.795.7%SourceGPT-5.4 mini—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGLM-4.72.439%GPT-5.4 mini28.280%GPT-5.4 mini leads this result
FrontierMath v2 (Tier 4)
Shared sourceGLM-4.70.000%GPT-5.4 mini2.080%GPT-5.4 mini leads this result
Multimodal
Frequently asked questions
Which is better, GLM-4.7 or GPT-5.4 mini?
GPT-5.4 mini has the higher public score estimate, 61.11 versus 57.71, 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-4.7 or GPT-5.4 mini?
GLM-4.7 leads the public coding lane, 47.7 to 42.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GLM-4.7 or GPT-5.4 mini?
GLM-4.7 scores higher for agentic tasks on the public lane, 45.3 to 39.1. GLM-4.7 and GPT-5.4 mini are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GLM-4.7 or GPT-5.4 mini?
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-4.7 or GPT-5.4 mini?
GPT-5.4 mini has the larger documented context window: 400K, compared with 200K.
Related comparisons
Last updated September 10, 2026
Watch GLM-4.7 vs GPT-5.4 mini
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.