Model comparison

GLM-5 vs GPT-5.5

Data verified July 22, 2026

Head-to-head evidence from 25 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

GLM-5

Z.AI

66.06/100

Margin

7.5pts

winning →

GPT-5.5

OpenAI

73.51/100

3 category wins2 category wins

Public leaderboard positions: GLM-5 #28 (Supported); GPT-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5 and GPT-5.5 share 25 comparable benchmark results. 5 of 8 categories are comparable. 24 results are unique to GLM-5; 32 to GPT-5.5.

Updated July 22, 2026

Shared results: 25
GLM-5 only: 24
GPT-5.5 only: 32
Comparable categories: 5 / 8

Pick GPT-5.5 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if mathematics is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 25 shared benchmark results across 7 evidence categories; 5 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 66.06. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 56.2. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 16.434% to 51.700%. GLM-5 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.

GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 9.4x on output cost alone. GPT-5.5 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-5.5 gives you the larger context window at 1M, 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-5.5
Category	GLM-5	Δ	GPT-5.5
Agentic	GLM-556.2	Margin→ 25.4	GPT-5.581.6
Reasoning	GLM-560.8	Margin→ 24.2	GPT-5.585.0
Math	GLM-556.3	Margin← 8.7	GPT-5.547.6
Knowledge	GLM-566.4	Margin← 8.6	GPT-5.557.8
Coding	GLM-566.3	Margin← 7.7	GPT-5.558.6
Multilingual	GLM-583.1	MarginNo overlap	GPT-5.5Not measured
Multimodal	GLM-5Not measured	MarginNo overlap	GPT-5.570.4
Inst. Following	GLM-592.6	MarginNo overlap	GPT-5.5Not measured

Decisive benchmark drivers

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

A · GLM-5B · GPT-5.5

FrontierMath v2 (Tiers 1-3)
Math
Source ↗
A 16.434%B 51.700%
Winner: GPT-5.5Δ 35.3
FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; GPT-5.5 scored 51.700%. GPT-5.5 wins this benchmark.
FrontierMath v2 (Tier 4)
Math
Source ↗
A 2.100%B 35.400%
Winner: GPT-5.5Δ 33.3
FrontierMath v2 (Tier 4): GLM-5 scored 2.100%; GPT-5.5 scored 35.400%. GPT-5.5 wins this benchmark.
Terminal-Bench 2.0
Agentic
Source ↗
A 56.2%B 82%
Winner: GPT-5.5Δ 25.8
Terminal-Bench 2.0: GLM-5 scored 56.2%; GPT-5.5 scored 82%. GPT-5.5 wins this benchmark.
GPQA
Knowledge
Source ↗
A 86%B 93.6%
Winner: GPT-5.5Δ 7.6
GPQA: GLM-5 scored 86%; GPT-5.5 scored 93.6%. GPT-5.5 wins this benchmark.
SWE-bench Pro
Coding
Source ↗
A 55.1%B 58.6%
Winner: GPT-5.5Δ 3.5
SWE-bench Pro: GLM-5 scored 55.1%; GPT-5.5 scored 58.6%. GPT-5.5 wins this benchmark.

Operational comparison

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

Metric	GLM-5	GPT-5.5	Comparison
Input / output priceUSD per 1M tokens	GLM-5$1 input / $3.2 output	GPT-5.5$5 input / $30 output	GLM-5 has the lower combined listed price.
Generation speedtokens per second	GLM-574 tok/s	GPT-5.5Not available	A complete speed comparison is not available.
First-answer latencyseconds to first token	GLM-51.64 s	GPT-5.5Not available	A complete latency comparison is not available.
Context windowmaximum listed tokens	GLM-5200K	GPT-5.51M	GPT-5.5 lists the larger context window.

Benchmark Deep Dive

AgenticGPT-5.5 wins

30 benchmarks

Benchmark	GLM-5	GPT-5.5	Result
Terminal-Bench 2.0Source	56.2%	82%	GPT-5.5 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%	55.6%	GPT-5.5 leads
MCP AtlasSource	31.1%	75.3%	GPT-5.5 leads
MCP-TasksSource	60.8%	—	Not comparable
WideResearchSource	69.8%	—	Not comparable
τ²-bench resultsSource	98.2%	93.9%	GLM-5 leads
CyberGymSource	43.2%	81.8%	GPT-5.5 leads
APEX-Agents-AASource	14.5%	37.7%	GPT-5.5 leads
Gert LabsSource	50.99%	72.93%	GPT-5.5 leads
BrowseCompSource	—	84.4%	Not comparable
OSWorld-VerifiedSource	—	78.7%	Not comparable
AA Agentic IndexSource	—	44.9%	Not comparable
GDPval-AASource	—	49.5%	Not comparable
GDPval-AASource	—	1490	Not comparable
ResearchClawBenchSource	—	17.0%	Not comparable
OSWorld 2.0Source	—	13.0%	Not comparable
JobBenchSource	—	42.7%	Not comparable
ExploitGymSource	—	13.4%	Not comparable
AA BriefcaseSource	—	1154	Not comparable
AA AutomationBenchSource	—	42.1%	Not comparable
AA EnterpriseOps-GymSource	—	46.6%	Not comparable
AA Harvey LABSource	—	86.3%	Not comparable
AA ITBenchSource	—	45.8%	Not comparable
AA Tau3 BankingSource	—	31.3%	Not comparable
terminalBenchHardSource	—	60.6%	Not comparable
aaTerminalBench21Source	—	84.3%	Not comparable

CodingGLM-5 wins

13 benchmarks

Benchmark	GLM-5	GPT-5.5	Result
SWE-bench VerifiedSource	77.8%	—	Not comparable
SWE-bench Verified*Source	72.8%	—	Not comparable
SWE-bench ProSource	55.1%	58.6%	GPT-5.5 leads
SWE MultilingualSource	73.3%	—	Not comparable
SWE-RebenchSource	62.8%	—	Not comparable
React Native EvalsSource	74.8%	84.7%	GPT-5.5 leads
AA-SciCodeSource	46.2%	56.1%	GPT-5.5 leads
Terminal-Bench 2.0Source	—	82.0%	Not comparable
Vibe Code BenchSource	—	69.85%	Not comparable
cursorBench31Source	—	59.2%	Not comparable
cursorBench32Source	—	58.4%	Not comparable
AA Coding IndexSource	—	74.9%	Not comparable
FrontierCode 1.1 MainSource	—	43.0%	Not comparable

ReasoningGPT-5.5 wins

7 benchmarks

Benchmark	GLM-5	GPT-5.5	Result
LongBench v2Source	60.8%	—	Not comparable
AI-NeedleSource	63.3%	—	Not comparable
AA-LCRSource	63.3%	74.3%	GPT-5.5 leads
CritPtSource	2.0%	27.1%	GPT-5.5 leads
MRCR v2 64K-128KSource	—	83.1%	Not comparable
MRCR v2 128K-256KSource	—	87.5%	Not comparable
ARC-AGI-2Source	—	85%	Not comparable

KnowledgeGLM-5 wins

13 benchmarks

Benchmark	GLM-5	GPT-5.5	Result
GPQASource	86%	93.6%	GPT-5.5 leads
GPQA-DSource	86.0%	93.6%	GPT-5.5 leads
SuperGPQASource	66.8%	—	Not comparable
MMLU-ProSource	85.7%	—	Not comparable
MMLU-Pro (Arcee)Source	85.8%	—	Not comparable
HLESource	50.4%	52.2%	GPT-5.5 leads
Artificial Analysis Intelligence IndexSource	39.5%	54.8%	GPT-5.5 leads
AA-GPQA DiamondSource	82.0%	93.5%	GPT-5.5 leads
AA-HLESource	27.2%	44.3%	GPT-5.5 leads
AA-Omniscience IndexSource	2.0%	20.1%	GPT-5.5 leads
AA-Omniscience AccuracySource	26.9%	56.9%	GPT-5.5 leads
AA-Omniscience Hallucination RateSource	34.0%	85.5%	GLM-5 leads
HLE w/o toolsSource	—	41.4%	Not comparable

MathGLM-5 wins

9 benchmarks

Benchmark	GLM-5	GPT-5.5	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%	51.700%	GPT-5.5 leads
FrontierMath v2 (Tier 4)Source	2.100%	35.400%	GPT-5.5 leads
FrontierMath (legacy)Source	—	51.7%	Not comparable

Multilingual

2 benchmarks

Benchmark	GLM-5	GPT-5.5	Result
MMLU-ProXSource	83.1%	—	Not comparable
NOVA-63Source	55.1%	—	Not comparable

Multimodal

5 benchmarks

Benchmark	GLM-5	GPT-5.5	Result
Design Arena WebsiteSource	1278	1282	GPT-5.5 leads
MMMU-ProSource	—	81.2%	Not comparable
MMMU-Pro w/ PythonSource	—	83.2%	Not comparable
OfficeQA ProSource	—	54.1%	Not comparable
AA-MMMU-ProSource	—	79.9%	Not comparable

Inst. Following

2 benchmarks

Benchmark	GLM-5	GPT-5.5	Result
IFEvalSource	92.6%	—	Not comparable
AA-IFBenchSource	72.3%	75.9%	GPT-5.5 leads

Frequently Asked Questions (6)

Which is better, GLM-5 or GPT-5.5?

GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 66.06. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 16.434% and 51.700%.

Which is better for knowledge tasks, GLM-5 or GPT-5.5?

GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 57.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-5 or GPT-5.5?

GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 58.6. Inside this category, React Native Evals is the benchmark that creates the most daylight between them.

Which is better for math, GLM-5 or GPT-5.5?

GLM-5 has the edge for math in this comparison, averaging 56.3 versus 47.6. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.

Which is better for reasoning, GLM-5 or GPT-5.5?

GPT-5.5 has the edge for reasoning in this comparison, averaging 85 versus 60.8. Inside this category, CritPt is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5 or GPT-5.5?

GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.

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