Model comparison

GLM-5 vs GPT-5.4 nano

Data verified July 23, 2026

Head-to-head evidence from 19 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

GLM-5

Z.AI

66.06/100

Margin

0.7pts

winning →

GPT-5.4 nano

OpenAI

66.79/100

3 category wins0 category wins

Public leaderboard positions: GLM-5 #28 (Supported); GPT-5.4 nano #25 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5 and GPT-5.4 nano share 19 comparable benchmark results. 3 of 8 categories are comparable. 30 results are unique to GLM-5; 10 to GPT-5.4 nano.

Updated July 23, 2026

Shared results: 19
GLM-5 only: 30
GPT-5.4 nano only: 10
Comparable categories: 3 / 8

Pick GPT-5.4 nano if you want the stronger benchmark profile. GLM-5 only becomes the better choice if mathematics is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

Confidence note. This is a partial-evidence comparison with 19 shared benchmark results across 6 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.4 nano has the cleaner BenchAlign overall profile here, landing at 66.79 versus 66.06. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.20 input / $1.25 output per 1M tokens for GPT-5.4 nano. That is roughly 2.6x on output cost alone. GPT-5.4 nano is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.4 nano gives you the larger context window at 400K, compared with 200K for GLM-5.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for GLM-5 and GPT-5.4 nano
Category	GLM-5	Δ	GPT-5.4 nano
Math	GLM-556.3	Margin← 35.3	GPT-5.4 nano21.0
Knowledge	GLM-566.4	Margin← 22.6	GPT-5.4 nano43.8
Agentic	GLM-556.2	Margin← 13.3	GPT-5.4 nano42.9
Coding	GLM-566.3	MarginNo overlap	GPT-5.4 nanoNot measured
Reasoning	GLM-560.8	MarginNo overlap	GPT-5.4 nanoNot measured
Multilingual	GLM-583.1	MarginNo overlap	GPT-5.4 nanoNot measured
Multimodal	GLM-5Not measured	MarginNo overlap	GPT-5.4 nano66.1
Inst. Following	GLM-592.6	MarginNo overlap	GPT-5.4 nanoNot measured

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

A · GLM-5B · GPT-5.4 nano

HLE
Knowledge
Source ↗
A 50.4%B 37.7%
Winner: GLM-5Δ 12.7
HLE: GLM-5 scored 50.4%; GPT-5.4 nano scored 37.7%. GLM-5 wins this benchmark.
Terminal-Bench 2.0
Agentic
Source ↗
A 56.2%B 46.3%
Winner: GLM-5Δ 9.9
Terminal-Bench 2.0: GLM-5 scored 56.2%; GPT-5.4 nano scored 46.3%. GLM-5 wins this benchmark.
FrontierMath v2 (Tiers 1-3)
Math
Source ↗
A 16.434%B 25.860%
Winner: GPT-5.4 nanoΔ 9.4
FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; GPT-5.4 nano scored 25.860%. GPT-5.4 nano wins this benchmark.
FrontierMath v2 (Tier 4)
Math
Source ↗
A 2.100%B 6.250%
Winner: GPT-5.4 nanoΔ 4.2
FrontierMath v2 (Tier 4): GLM-5 scored 2.100%; GPT-5.4 nano scored 6.250%. GPT-5.4 nano wins this benchmark.
GPQA
Knowledge
Source ↗
A 86%B 82.8%
Winner: GLM-5Δ 3.2
GPQA: GLM-5 scored 86%; GPT-5.4 nano scored 82.8%. GLM-5 wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

Metric	GLM-5	GPT-5.4 nano	Comparison
Input / output priceUSD per 1M tokens	GLM-5$1 input / $3.2 output	GPT-5.4 nano$0.2 input / $1.25 output	GPT-5.4 nano has the lower combined listed price.
Generation speedtokens per second	GLM-574 tok/s	GPT-5.4 nano191 tok/s	GPT-5.4 nano has the higher measured throughput.
First-answer latencyseconds to first token	GLM-51.64 s	GPT-5.4 nano3.64 s	GLM-5 reaches the first token sooner.
Context windowmaximum listed tokens	GLM-5200K	GPT-5.4 nano400K	GPT-5.4 nano lists the larger context window.

Benchmark Deep Dive

AgenticGLM-5 wins

17 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
Terminal-Bench 2.0Source	56.2%	46.3%	GLM-5 leads
Claw-EvalSource	57.7%	—	Not comparable
QwenClawBenchSource	54.1%	—	Not comparable
τ³-bench resultsSource	65.6%	—	Not comparable
DeepPlanningSource	14.6%	—	Not comparable
ToolathlonSource	38%	35.5%	GLM-5 leads
MCP AtlasSource	31.1%	56.1%	GPT-5.4 nano leads
MCP-TasksSource	60.8%	—	Not comparable
WideResearchSource	69.8%	—	Not comparable
τ²-bench resultsSource	98.2%	76%	GLM-5 leads
CyberGymSource	43.2%	—	Not comparable
APEX-Agents-AASource	14.5%	24.9%	GPT-5.4 nano leads
Gert LabsSource	50.99%	—	Not comparable
OSWorld-VerifiedSource	—	39%	Not comparable
AA Agentic IndexSource	—	27.5%	Not comparable
GDPval-AASource	—	30.0%	Not comparable
GDPval-AASource	—	1100	Not comparable

Coding

9 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
SWE-bench VerifiedSource	77.8%	—	Not comparable
SWE-bench Verified*Source	72.8%	—	Not comparable
SWE-bench ProSource	55.1%	—	Not comparable
SWE MultilingualSource	73.3%	—	Not comparable
SWE-RebenchSource	62.8%	—	Not comparable
React Native EvalsSource	74.8%	—	Not comparable
AA-SciCodeSource	46.2%	46.9%	GPT-5.4 nano leads
Vibe Code BenchSource	—	26.10%	Not comparable
AA Coding IndexSource	—	56.1%	Not comparable

Reasoning

4 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
LongBench v2Source	60.8%	—	Not comparable
AI-NeedleSource	63.3%	—	Not comparable
AA-LCRSource	63.3%	66.0%	GPT-5.4 nano leads
CritPtSource	2.0%	9.3%	GPT-5.4 nano leads

KnowledgeGLM-5 wins

13 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
GPQASource	86%	82.8%	GLM-5 leads
GPQA-DSource	86.0%	—	Not comparable
SuperGPQASource	66.8%	—	Not comparable
MMLU-ProSource	85.7%	—	Not comparable
MMLU-Pro (Arcee)Source	85.8%	—	Not comparable
HLESource	50.4%	37.7%	GLM-5 leads
Artificial Analysis Intelligence IndexSource	39.5%	38.2%	GLM-5 leads
AA-GPQA DiamondSource	82.0%	81.7%	GLM-5 leads
AA-HLESource	27.2%	26.5%	GLM-5 leads
AA-Omniscience IndexSource	2.0%	-29.5%	GLM-5 leads
AA-Omniscience AccuracySource	26.9%	25.4%	GLM-5 leads
AA-Omniscience Hallucination RateSource	34.0%	73.6%	GLM-5 leads
HLE w/o toolsSource	—	24.3%	Not comparable

MathGLM-5 wins

8 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
AIME26Source	95.8%	—	Not comparable
AIME25 (Arcee)Source	93.3%	—	Not comparable
HMMT Feb 2025Source	97.5%	—	Not comparable
HMMT Nov 2025Source	96.9%	—	Not comparable
HMMT Feb 2026Source	86.4%	—	Not comparable
MMAnswerBenchSource	82.5%	—	Not comparable
FrontierMath v2 (Tiers 1-3)Source	16.434%	25.860%	GPT-5.4 nano leads
FrontierMath v2 (Tier 4)Source	2.100%	6.250%	GPT-5.4 nano leads

Multilingual

2 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
MMLU-ProXSource	83.1%	—	Not comparable
NOVA-63Source	55.1%	—	Not comparable

Multimodal

4 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
Design Arena WebsiteSource	1278	—	Not comparable
MMMU-ProSource	—	66.1%	Not comparable
MMMU-Pro w/ PythonSource	—	69.5%	Not comparable
AA-MMMU-ProSource	—	65.4%	Not comparable

Inst. Following

2 benchmarks

Benchmark	GLM-5	GPT-5.4 nano	Result
IFEvalSource	92.6%	—	Not comparable
AA-IFBenchSource	72.3%	75.9%	GPT-5.4 nano leads

Frequently Asked Questions (4)

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

GPT-5.4 nano is ahead on BenchLM's BenchAlign leaderboard, 66.79 to 66.06. The biggest single separator in this matchup is HLE, where the scores are 50.4% and 37.7%.

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

GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 43.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

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

GLM-5 has the edge for math in this comparison, averaging 56.3 versus 21. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.

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

GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 42.9. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.

Related Comparisons

Explore More

Z.AI Compare Pricing Methodology Find Your Best LLM Overall Rankings

Last updated: July 23, 2026

Choose a model with this week’s evidence

Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.

One email each week. Unsubscribe anytime.