Model A

Z.AI

61.45/100

Supported · Public rank #56

90% interval 50.1–72.8

GLM-5 vs GPT-5.4 nano

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

GPT-5.4 nano

OpenAI

59.57/100

Supported · Public rank #68

90% interval 45.5–73.7

Decision reading

GLM-5 has the higher public score estimate, 61.45 versus 59.57, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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 nano
GPT-5.4 nano has the larger documented context window.
Confidence: documented
Chat turn cost
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.
Confidence: listed-rates

Show secondary and unsupported calls

Repository review cost
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.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GLM-5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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 does not fit this workload in one request. GLM-5 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.

29 GLM-5 only

7 Shared

11 GPT-5.4 nano only

Shared results: 7
GLM-5 only: 29
GPT-5.4 nano only: 11
Like-for-like categories: 0 / 8

4 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.

Agentic

Directional only

GLM-5: 51.0; Estimated · #45/152
GPT-5.4 nano: 34.6; Supported · #133/152
Basis: BenchAlign lane · 11 vs 6 public rows
Reading: Directional only

Coding

Directional only

GLM-5: 56.1; Estimated · #39/151
GPT-5.4 nano: 37.1; Supported · #126/151
Basis: BenchAlign lane · 6 vs 3 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5: 54.1; Estimated · #57/183
GPT-5.4 nano: 47.4; Supported · #97/183
Basis: BenchAlign lane · 6 vs 5 public rows
Reading: Directional only

Instruction following

Directional only

GLM-5: 88.5; #31/123
GPT-5.4 nano: 93.2; #9/123
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5: 52.1; Unranked · 4 rankable rows
GPT-5.4 nano: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5: 56.9; #7/7
GPT-5.4 nano: 43.9; Unranked · 2 rankable rows
Basis: Provisional lane · 4 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5: 48.7; #6/12
GPT-5.4 nano: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5: Not ranked
GPT-5.4 nano: 23.8; #45/48
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5 and GPT-5.4 nano
Category	GLM-5	GPT-5.4 nano	Basis	Reading
Agentic	51.0Estimated · #45/152	34.6Supported · #133/152	Directional onlyBenchAlign lane · 11 vs 6 public rows	Directional only
Coding	56.1Estimated · #39/151	37.1Supported · #126/151	Directional onlyBenchAlign lane · 6 vs 3 public rows	Directional only
Knowledge	54.1Estimated · #57/183	47.4Supported · #97/183	Directional onlyBenchAlign lane · 6 vs 5 public rows	Directional only
Instruction following	88.5#31/123	93.2#9/123	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	52.1Unranked · 4 rankable rows	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	56.9#7/7	43.9Unranked · 2 rankable rows	Not comparableProvisional lane · 4 vs 2 weighted rows	Not comparable
Multilingual	48.7#6/12	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	23.8#45/48	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.

HLE
Knowledge
GLM-5: 50.4%GPT-5.4 nano: 37.7%Normalized gap 12.7GLM-5 source GPT-5.4 nano source
Terminal-Bench 2.0
Agentic
GLM-5: 56.2%GPT-5.4 nano: 46.3%Normalized gap 9.9GLM-5 source GPT-5.4 nano source
FrontierMath v2 (Tiers 1-3)
Math
GLM-5: 16.434%GPT-5.4 nano: 25.860%Normalized gap 9.4Shared source
FrontierMath v2 (Tier 4)
Math
GLM-5: 2.100%GPT-5.4 nano: 6.250%Normalized gap 4.2Shared source
GPQA
Knowledge
GLM-5: 86%GPT-5.4 nano: 82.8%Normalized gap 3.2GLM-5 source GPT-5.4 nano source

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: $0.0026; Fits in one request
GPT-5.4 nano: $0.00082; Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5: $0.0596; Fits in one request
GPT-5.4 nano: $0.01375; Fits in one request

GPT-5.4 nano 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: $0.252; Does not fit in one request; Cached input priced at the published list-input rate
GPT-5.4 nano: $0.0205; Fits in one request

GLM-5 does not fit this workload in one request. GLM-5 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-5GPT-5.4 nano

Context window

Maximum documented context; output-token limits may be lower.

GLM-5

200K

GPT-5.4 nano

400K

OpenAI GPT-5.4 nano model documentation

API model ID

GLM-5

Not sourced

GPT-5.4 nano

gpt-5.4-nano

OpenAI GPT-5.4 nano model documentation

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GLM-5

Not published

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Documented inputs

GLM-5

Not sourced

GPT-5.4 nano

text, image

OpenAI model catalog

Documented outputs

GLM-5

Not sourced

GPT-5.4 nano

text

OpenAI model catalog

Provider availability

GLM-5

Not sourced

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GLM-5

Non-Reasoning

GPT-5.4 nano

Reasoning

Weight access

GLM-5

Open Weight

GPT-5.4 nano

Proprietary

License

GLM-5

Open Weight

GPT-5.4 nano

Proprietary

Release date

GLM-5

2026-03-01

GPT-5.4 nano

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: GLM-5 has the higher public score estimate, 61.45 versus 59.57, but the 90% score intervals overlap.
Workload cost: Repository review: $0.0596 vs $0.01375. Cache-heavy agent loop: $0.252 vs $0.0205.
Context tradeoff: GPT-5.4 nano 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 evidence47 rows

Agentic

Terminal-Bench 2.0
GLM-556.2%
Source
GPT-5.4 nano46.3%
Source
GLM-5 leads this result
Claw-Eval
GLM-557.7%
Source
GPT-5.4 nano—
Not directly comparable
QwenClawBench
GLM-554.1%
Source
GPT-5.4 nano—
Not directly comparable
τ³-bench results
GLM-565.6%
Source
GPT-5.4 nano—
Not directly comparable
DeepPlanning
GLM-514.6%
Source
GPT-5.4 nano—
Not directly comparable
Toolathlon
GLM-538%
Source
GPT-5.4 nano35.5%
Source
GLM-5 leads this result
MCP Atlas
GLM-531.1%
Source
GPT-5.4 nano56.1%
Source
GPT-5.4 nano leads this result
MCP-Tasks
GLM-560.8%
Source
GPT-5.4 nano—
Not directly comparable
WideResearch
GLM-569.8%
Source
GPT-5.4 nano—
Not directly comparable
CyberGym
GLM-543.2%
Source
GPT-5.4 nano—
Not directly comparable
Gert Labs
GLM-550.99%
Source
GPT-5.4 nano—
Not directly comparable
OSWorld-Verified
GLM-5—
GPT-5.4 nano39%
Source
Not directly comparable
τ²-bench results
GLM-5—
GPT-5.4 nano92.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5—
GPT-5.4 nano41.6%
Source
Not directly comparable

Coding

SWE-bench Verified
GLM-577.8%
Source
GPT-5.4 nano—
Not directly comparable
SWE-bench Verified*
GLM-572.8%
Source
GPT-5.4 nano—
Not directly comparable
SWE-bench Pro
GLM-555.1%
Source
GPT-5.4 nano—
Not directly comparable
SWE Multilingual
GLM-573.3%
Source
GPT-5.4 nano—
Not directly comparable
SWE-Rebench
GLM-562.8%
Source
GPT-5.4 nano—
Not directly comparable
React Native Evals
GLM-574.8%
Source
GPT-5.4 nano—
Not directly comparable
Vibe Code Bench
GLM-5—
GPT-5.4 nano26.10%
Source
Not directly comparable
LiveCodeBench (Vals)
GLM-5—
GPT-5.4 nano84.0%
Source
Not directly comparable
SWE-bench (Vals)
GLM-5—
GPT-5.4 nano69.8%
Source
Not directly comparable

Reasoning

LongBench v2
GLM-560.8%
Source
GPT-5.4 nano—
Not directly comparable
AI-Needle
GLM-563.3%
Source
GPT-5.4 nano—
Not directly comparable

Knowledge

GPQA
GLM-586%
Source
GPT-5.4 nano82.8%
Source
GLM-5 leads this result
GPQA-D
GLM-586.0%
Source
GPT-5.4 nano—
Not directly comparable
SuperGPQA
GLM-566.8%
Source
GPT-5.4 nano—
Not directly comparable
MMLU-Pro
GLM-585.7%
Source
GPT-5.4 nano—
Not directly comparable
MMLU-Pro (Arcee)
GLM-585.8%
Source
GPT-5.4 nano—
Not directly comparable
HLE
GLM-550.4%
Source
GPT-5.4 nano37.7%
Source
GLM-5 leads this result
HLE w/o tools
GLM-5—
GPT-5.4 nano24.3%
Source
Not directly comparable
GPQA Diamond (Vals)
GLM-5—
GPT-5.4 nano77.5%
Source
Not directly comparable
MMLU-Pro (Vals)
GLM-5—
GPT-5.4 nano77.2%
Source
Not directly comparable

Math

AIME26
GLM-595.8%
Source
GPT-5.4 nano—
Not directly comparable
AIME25 (Arcee)
GLM-593.3%
Source
GPT-5.4 nano—
Not directly comparable
HMMT Feb 2025
GLM-597.5%
Source
GPT-5.4 nano—
Not directly comparable
HMMT Nov 2025
GLM-596.9%
Source
GPT-5.4 nano—
Not directly comparable
HMMT Feb 2026
GLM-586.4%
Source
GPT-5.4 nano—
Not directly comparable
MMAnswerBench
GLM-582.5%
Source
GPT-5.4 nano—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared source
GLM-516.434%
GPT-5.4 nano25.860%
GPT-5.4 nano leads this result
FrontierMath v2 (Tier 4)
Shared source
GLM-52.100%
GPT-5.4 nano6.250%
GPT-5.4 nano leads this result

Multilingual

MMLU-ProX
GLM-583.1%
Source
GPT-5.4 nano—
Not directly comparable
NOVA-63
GLM-555.1%
Source
GPT-5.4 nano—
Not directly comparable

Multimodal

MMMU-Pro
GLM-5—
GPT-5.4 nano66.1%
Source
Not directly comparable
MMMU-Pro w/ Python
GLM-5—
GPT-5.4 nano69.5%
Source
Not directly comparable

Instruction following

IFEval
GLM-592.6%
Source
GPT-5.4 nano—
Not directly comparable

Frequently asked questions

Which is better, GLM-5 or GPT-5.4 nano?

GLM-5 has the higher public score estimate, 61.45 versus 59.57, 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 or GPT-5.4 nano?

GLM-5 scores higher for coding on the public lane, 56.1 to 37.1. GLM-5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GLM-5 or GPT-5.4 nano?

GLM-5 scores higher for agentic tasks on the public lane, 51 to 34.6. GLM-5 is 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-5 or GPT-5.4 nano?

For the stated presets, chat costs $0.0026 on GLM-5 and $0.00082 on GPT-5.4 nano; repository review costs $0.0596 and $0.01375; the cache-heavy agent loop costs $0.252 and $0.0205. GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5 or GPT-5.4 nano?

GPT-5.4 nano has the larger documented context window: 400K, compared with 200K.

Related comparisons

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Last updated September 10, 2026

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GLM-5 vs GPT-5.4 nano

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, GLM-5 or GPT-5.4 nano?

Which is better for coding, GLM-5 or GPT-5.4 nano?

Which is better for agentic tasks, GLM-5 or GPT-5.4 nano?

Which costs less, GLM-5 or GPT-5.4 nano?

Which has the larger context window, GLM-5 or GPT-5.4 nano?

Related comparisons

Watch GLM-5 vs GPT-5.4 nano