Knowledge
Directional only- GLM-5-Turbo
- 53.9
- Estimated · #62/183
- GPT-5.4
- 69.2
- Supported · #15/183
- Basis
- BenchAlign lane · 0 vs 7 public rows
- Reading
- Directional only
GLM-5-Turbo vs GPT-5.4
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.4 has the higher public score estimate, 70.85 versus 61.64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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.
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-Turbo
GLM-5-Turbo 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
Repository review cost
50K fresh input + 3K output tokens
GLM-5-Turbo
GLM-5-Turbo has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GLM-5-Turbo is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5-Turbo is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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-5-Turbo does not fit this workload in one request. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Shared
37 GPT-5.4 only
- Shared results
- 1
- GLM-5-Turbo only
- 0
- GPT-5.4 only
- 37
- Like-for-like categories
- 0 / 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.
Instruction following
Directional only- GLM-5-Turbo
- 89.7
- #24/123
- GPT-5.4
- 90.6
- #19/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GLM-5-Turbo
- Not ranked
- GPT-5.4
- 52.5
- Supported · #40/152
- Basis
- BenchAlign lane · 1 vs 14 public rows
- Reading
- Not comparable
Coding
Not comparable- GLM-5-Turbo
- Not ranked
- GPT-5.4
- 53.9
- Supported · #42/151
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5-Turbo
- 70.3
- Unranked · 2 rankable rows
- GPT-5.4
- 57.2
- #17/20
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5-Turbo
- Not ranked
- GPT-5.4
- 64.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5-Turbo
- Not ranked
- GPT-5.4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5-Turbo
- Not ranked
- GPT-5.4
- 69.3
- #20/48
- Basis
- Provisional lane · 0 vs 3 weighted rows
- Reading
- Not comparable
| Category | GLM-5-Turbo | GPT-5.4 | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 53.9Estimated · #62/183 | 69.2Supported · #15/183 | Directional onlyBenchAlign lane · 0 vs 7 public rows | Directional only |
| Instruction following | 89.7#24/123 | 90.6#19/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 52.5Supported · #40/152 | Not comparableBenchAlign lane · 1 vs 14 public rows | Not comparable |
| Coding | Not ranked | 53.9Supported · #42/151 | Not comparableBenchAlign lane · 0 vs 4 public rows | Not comparable |
| Reasoning | 70.3Unranked · 2 rankable rows | 57.2#17/20 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | Not ranked | 64.5Unranked · 2 rankable rows | 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 |
| Multimodal | Not ranked | 69.3#20/48 | Not comparableProvisional lane · 0 vs 3 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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-Turbo
- $0.0032
- Fits in one request
- GPT-5.4
- $0.01
- Fits in one request
GLM-5-Turbo has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5-Turbo
- $0.072
- Fits in one request
- GPT-5.4
- $0.17
- Fits in one request
GLM-5-Turbo 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-Turbo
- $0.304
- Does not fit in one request
- Cached input priced at the published list-input rate
- GPT-5.4
- $0.25
- Fits in one request
GLM-5-Turbo does not fit this workload in one request. GLM-5-Turbo 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-TurboGPT-5.4
Context window
Maximum documented context; output-token limits may be lower.
GLM-5-Turbo
200K
GPT-5.4
1.05M
OpenAI pricingAPI model ID
GLM-5-Turbo
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-Turbo
Not published
GPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingDocumented inputs
GLM-5-Turbo
Not sourced
GPT-5.4
Not sourced
Documented outputs
GLM-5-Turbo
Not sourced
GPT-5.4
Not sourced
Provider availability
GLM-5-Turbo
Not sourced
GPT-5.4
Not sourced
Reasoning profile
GLM-5-Turbo
Reasoning
GPT-5.4
Reasoning
Weight access
GLM-5-Turbo
Proprietary
GPT-5.4
Proprietary
License
GLM-5-Turbo
Proprietary
GPT-5.4
Proprietary
Release date
GLM-5-Turbo
2026-03-01
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, 70.85 versus 61.64, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.072 vs $0.17. Cache-heavy agent loop: $0.304 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 evidence38 rows
Agentic
Claw-Eval
Shared sourceGLM-5-Turbo55.8%GPT-5.460.3%GPT-5.4 leads this result
Terminal-Bench 2.0
GLM-5-Turbo—GPT-5.475.1%SourceNot directly comparable
CyberGym
GLM-5-Turbo—GPT-5.479.0%SourceNot directly comparable
BrowseComp
GLM-5-Turbo—GPT-5.482.7%SourceNot directly comparable
OSWorld-Verified
GLM-5-Turbo—GPT-5.475%SourceNot directly comparable
MCP Atlas
GLM-5-Turbo—GPT-5.470.6%SourceNot directly comparable
Toolathlon
GLM-5-Turbo—GPT-5.454.6%SourceNot directly comparable
τ²-bench results
GLM-5-Turbo—GPT-5.498.9%SourceNot directly comparable
DeepSearchQA
GLM-5-Turbo—GPT-5.473.6%SourceNot directly comparable
Gert Labs
GLM-5-Turbo—GPT-5.464.89%SourceNot directly comparable
ResearchClawBench
GLM-5-Turbo—GPT-5.415.3%SourceNot directly comparable
JobBench
GLM-5-Turbo—GPT-5.438.9%SourceNot directly comparable
ExploitGym
GLM-5-Turbo—GPT-5.46.0%SourceNot directly comparable
ApprenticeBench
GLM-5-Turbo—GPT-5.411%SourceNot directly comparable
Coding
Reasoning
Knowledge
GPQA
GLM-5-Turbo—GPT-5.492.8%SourceNot directly comparable
HLE
GLM-5-Turbo—GPT-5.452.1%SourceNot directly comparable
HLE w/o tools
GLM-5-Turbo—GPT-5.439.8%SourceNot directly comparable
GPQA-D
GLM-5-Turbo—GPT-5.492.8%SourceNot directly comparable
HealthBench Hard
GLM-5-Turbo—GPT-5.440.1%SourceNot directly comparable
MedXpertQA (Text)
GLM-5-Turbo—GPT-5.459.6%SourceNot directly comparable
HealthBench Professional
GLM-5-Turbo—GPT-5.448.1%SourceNot directly comparable
Math
Multimodal
MMMU-Pro
GLM-5-Turbo—GPT-5.481.2%SourceNot directly comparable
OfficeQA Pro
GLM-5-Turbo—GPT-5.453.2%SourceNot directly comparable
MMMU-Pro w/ Python
GLM-5-Turbo—GPT-5.482.1%SourceNot directly comparable
CharXiv
GLM-5-Turbo—GPT-5.482.8%SourceNot directly comparable
ERQA
GLM-5-Turbo—GPT-5.465.4%SourceNot directly comparable
SimpleVQA
GLM-5-Turbo—GPT-5.461.1%SourceNot directly comparable
ScreenSpot Pro
GLM-5-Turbo—GPT-5.485.4%SourceNot directly comparable
ZeroBench
GLM-5-Turbo—GPT-5.441.0%SourceNot directly comparable
MedXpertQA (MM)
GLM-5-Turbo—GPT-5.477.1%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5-Turbo or GPT-5.4?
GPT-5.4 has the higher public score estimate, 70.85 versus 61.64, 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-Turbo or GPT-5.4?
GLM-5-Turbo is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GLM-5-Turbo or GPT-5.4?
GLM-5-Turbo is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5-Turbo or GPT-5.4?
For the stated presets, chat costs $0.0032 on GLM-5-Turbo and $0.01 on GPT-5.4; repository review costs $0.072 and $0.17; the cache-heavy agent loop costs $0.304 and $0.25. GLM-5-Turbo does not fit this workload in one request. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5-Turbo or GPT-5.4?
GPT-5.4 has the larger documented context window: 1.05M, compared with 200K.
Related comparisons
Last updated September 10, 2026
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