Coding
Like-for-like- GLM-5.2
- 62.1
- GPT-5.4
- 57.7
- Weighted basis
- 1 vs 1 rows
- Reading
- GLM-5.2 leads
Model comparison
GLM-5.2 vs GPT-5.4
Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
21 confirmed releases in the last 30 daystrack changes
GPT-5.4 has the higher public score estimate, 73.24 versus 62.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
9 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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-5.2
GLM-5.2 leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.4
GPT-5.4 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.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
9 GLM-5.2 only
9 Shared
28 GPT-5.4 only
- Shared results
- 9
- GLM-5.2 only
- 9
- GPT-5.4 only
- 28
- Like-for-like categories
- 2 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Knowledge
Like-for-like- GLM-5.2
- 59.6
- GPT-5.4
- 57.6
- Weighted basis
- 2 vs 2 rows
- Reading
- GLM-5.2 leads
Agentic
Directional only- GLM-5.2
- 81.0
- GPT-5.4
- 77.2
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- Not measured
- GPT-5.4
- 74.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 95.9
- GPT-5.4
- 42.5
- Weighted basis
- 2 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not measured
- GPT-5.4
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not measured
- GPT-5.4
- 73.2
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- Not measured
- GPT-5.4
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.2 | GPT-5.4 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 62.1 | 57.7 | Like-for-like1 vs 1 rows | GLM-5.2 leads |
| Knowledge | 59.6 | 57.6 | Like-for-like2 vs 2 rows | GLM-5.2 leads |
| Agentic | 81.0 | 77.2 | Directional only1 vs 3 rows | Directional only |
| Reasoning | Not measured | 74.0 | Not comparable0 vs 1 rows | Not comparable |
| Math | 95.9 | 42.5 | Not comparable2 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 73.2 | Not comparable0 vs 3 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
HLE
Knowledge
GPQA
Knowledge
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
- GPT-5.4
- $0.01
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5.2
- $0.0832
- Fits in one request
- GPT-5.4
- $0.17
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
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
- GPT-5.4
- $0.25
- Fits in one request
GPT-5.4 has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.2GPT-5.4
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
GPT-5.4
1.05M
OpenAI pricingAPI model ID
GLM-5.2
Not sourced
GPT-5.4
gpt-5.4
OpenAI pricingCached-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
GPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingDocumented inputs
GLM-5.2
Not sourced
GPT-5.4
Not sourced
Documented outputs
GLM-5.2
Not sourced
GPT-5.4
Not sourced
Provider availability
GLM-5.2
Not sourced
GPT-5.4
Not sourced
Reasoning profile
GLM-5.2
Reasoning
GPT-5.4
Reasoning
Weight access
GLM-5.2
Open Weight
GPT-5.4
Proprietary
License
GLM-5.2
Open Weight
GPT-5.4
Proprietary
Release date
GLM-5.2
2026-06-16
GPT-5.4
2026-03-05
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 has the higher public score estimate, 73.24 versus 62.94, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0832 vs $0.17. Cache-heavy agent loop: $0.352 vs $0.25.
- Context tradeoff
- GPT-5.4 has the larger documented window (1.05M).
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 evidence46 rows
Agentic
Terminal-Bench 2.0
GLM-5.281%SourceGPT-5.475.1%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceGPT-5.470.6%SourceGLM-5.2 leads this result
Toolathlon
GLM-5.248.2%SourceGPT-5.454.6%SourceGPT-5.4 leads this result
ResearchClawBench
Shared sourceGLM-5.220.7%GPT-5.415.3%GLM-5.2 leads this result
CyberGym
GLM-5.2—GPT-5.479.0%SourceNot directly comparable
BrowseComp
GLM-5.2—GPT-5.482.7%SourceNot directly comparable
OSWorld-Verified
GLM-5.2—GPT-5.475%SourceNot directly comparable
τ²-bench results
GLM-5.2—GPT-5.498.9%SourceNot directly comparable
Claw-Eval
GLM-5.2—GPT-5.460.3%SourceNot directly comparable
DeepSearchQA
GLM-5.2—GPT-5.473.6%SourceNot directly comparable
Gert Labs
GLM-5.2—GPT-5.464.89%SourceNot directly comparable
JobBench
GLM-5.2—GPT-5.438.9%SourceNot directly comparable
ExploitGym
GLM-5.2—GPT-5.46.0%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceGPT-5.457.7%SourceGLM-5.2 leads this result
NL2Repo
GLM-5.248.9%SourceGPT-5.4—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceGPT-5.4—Not directly comparable
ProgramBench
GLM-5.263.7%SourceGPT-5.4—Not directly comparable
cursorBench32
GLM-5.255.0%SourceGPT-5.4—Not directly comparable
LiveCodeBench Pro
GLM-5.2—GPT-5.487.5%SourceNot directly comparable
React Native Evals
GLM-5.2—GPT-5.485.3%SourceNot directly comparable
Vibe Code Bench
GLM-5.2—GPT-5.467.42%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-5.291.2%SourceGPT-5.492.8%SourceGPT-5.4 leads this result
GPQA-D
GLM-5.291.2%SourceGPT-5.492.8%SourceGPT-5.4 leads this result
HLE
GLM-5.254.7%SourceGPT-5.452.1%SourceGLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%SourceGPT-5.439.8%SourceGLM-5.2 leads this result
HealthBench Hard
GLM-5.2—GPT-5.440.1%SourceNot directly comparable
MedXpertQA (Text)
GLM-5.2—GPT-5.459.6%SourceNot directly comparable
HealthBench Professional
GLM-5.2—GPT-5.448.1%SourceNot directly comparable
Math
AIME26
GLM-5.299.2%SourceGPT-5.4—Not directly comparable
HMMT Nov 2025
GLM-5.294.4%SourceGPT-5.4—Not directly comparable
HMMT Feb 2026
GLM-5.292.5%SourceGPT-5.4—Not directly comparable
MMAnswerBench
GLM-5.291.0%SourceGPT-5.4—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.2—GPT-5.447.600%SourceNot directly comparable
FrontierMath v2 (Tier 4)
GLM-5.2—GPT-5.427.100%SourceNot directly comparable
Multimodal
MMMU-Pro
GLM-5.2—GPT-5.481.2%SourceNot directly comparable
OfficeQA Pro
GLM-5.2—GPT-5.453.2%SourceNot directly comparable
MMMU-Pro w/ Python
GLM-5.2—GPT-5.482.1%SourceNot directly comparable
CharXiv
GLM-5.2—GPT-5.482.8%SourceNot directly comparable
ERQA
GLM-5.2—GPT-5.465.4%SourceNot directly comparable
SimpleVQA
GLM-5.2—GPT-5.461.1%SourceNot directly comparable
ScreenSpot Pro
GLM-5.2—GPT-5.485.4%SourceNot directly comparable
ZeroBench
GLM-5.2—GPT-5.441.0%SourceNot directly comparable
MedXpertQA (MM)
GLM-5.2—GPT-5.477.1%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.2 or GPT-5.4?
GPT-5.4 has the higher public score estimate, 73.24 versus 62.94, 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 GPT-5.4?
GLM-5.2 leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, GLM-5.2 or GPT-5.4?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GLM-5.2 or GPT-5.4?
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.01 on GPT-5.4; repository review costs $0.0832 and $0.17; the cache-heavy agent loop costs $0.352 and $0.25. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5.2 or GPT-5.4?
GPT-5.4 has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated July 31, 2026
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