Model comparison

GLM-5 vs Qwen3 235B 2507

Data verified July 22, 2026

Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

GLM-5

Z.AI

66.06/100

Margin

10.0pts

← winning

Qwen3 235B 2507

Alibaba

56.02/100

1 category wins1 category wins

Public leaderboard positions: GLM-5 #28 (Supported); Qwen3 235B 2507 #78 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5 and Qwen3 235B 2507 share 4 comparable benchmark results. 2 of 8 categories are comparable. 45 results are unique to GLM-5; 0 to Qwen3 235B 2507.

Updated July 22, 2026

Shared results: 4
GLM-5 only: 45
Qwen3 235B 2507 only: 0
Comparable categories: 2 / 8

Pick GLM-5 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GLM-5 is clearly ahead on the BenchAlign aggregate, 66.06 to 56.02. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GLM-5's sharpest advantage is in multilingual, where it averages 83.1 against 79.4. The single biggest benchmark swing on the page is GPQA, 86% to 77.5%. Qwen3 235B 2507 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.

GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507. That is roughly Infinityx on output cost alone. GLM-5 gives you the larger context window at 200K, compared with 128K for Qwen3 235B 2507.

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 Qwen3 235B 2507
Category	GLM-5	Δ	Qwen3 235B 2507
Knowledge	GLM-566.4	Margin→ 12.5	Qwen3 235B 250778.9
Multilingual	GLM-583.1	Margin← 3.7	Qwen3 235B 250779.4
Agentic	GLM-556.2	MarginNo overlap	Qwen3 235B 2507Not measured
Coding	GLM-566.3	MarginNo overlap	Qwen3 235B 2507Not measured
Reasoning	GLM-560.8	MarginNo overlap	Qwen3 235B 2507Not measured
Math	GLM-556.3	MarginNo overlap	Qwen3 235B 2507Not measured
Inst. Following	GLM-592.6	MarginNo overlap	Qwen3 235B 2507Not measured

Decisive benchmark drivers

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

A · GLM-5B · Qwen3 235B 2507

GPQA
Knowledge
Source ↗
A 86%B 77.5%
Winner: GLM-5Δ 8.5
GPQA: GLM-5 scored 86%; Qwen3 235B 2507 scored 77.5%. GLM-5 wins this benchmark.
SuperGPQA
Knowledge
Source ↗
A 66.8%B 62.6%
Winner: GLM-5Δ 4.2
SuperGPQA: GLM-5 scored 66.8%; Qwen3 235B 2507 scored 62.6%. GLM-5 wins this benchmark.
MMLU-ProX
Multilingual
Source ↗
A 83.1%B 79.4%
Winner: GLM-5Δ 3.7
MMLU-ProX: GLM-5 scored 83.1%; Qwen3 235B 2507 scored 79.4%. GLM-5 wins this benchmark.
MMLU-Pro
Knowledge
Source ↗
A 85.7%B 83%
Winner: GLM-5Δ 2.7
MMLU-Pro: GLM-5 scored 85.7%; Qwen3 235B 2507 scored 83%. 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	Qwen3 235B 2507	Comparison
Input / output priceUSD per 1M tokens	GLM-5$1 input / $3.2 output	Qwen3 235B 2507$0 input / $0 output	Qwen3 235B 2507 has the lower combined listed price.
Generation speedtokens per second	GLM-574 tok/s	Qwen3 235B 2507Not available	A complete speed comparison is not available.
First-answer latencyseconds to first token	GLM-51.64 s	Qwen3 235B 2507Not available	A complete latency comparison is not available.
Context windowmaximum listed tokens	GLM-5200K	Qwen3 235B 2507128K	GLM-5 lists the larger context window.

Benchmark Deep Dive

Agentic

13 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	Result
Terminal-Bench 2.0Source	56.2%	—	Not comparable
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%	—	Not comparable
MCP AtlasSource	31.1%	—	Not comparable
MCP-TasksSource	60.8%	—	Not comparable
WideResearchSource	69.8%	—	Not comparable
τ²-bench resultsSource	98.2%	—	Not comparable
CyberGymSource	43.2%	—	Not comparable
APEX-Agents-AASource	14.5%	—	Not comparable
Gert LabsSource	50.99%	—	Not comparable

Coding

7 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	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%	—	Not comparable

Reasoning

4 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	Result
LongBench v2Source	60.8%	—	Not comparable
AI-NeedleSource	63.3%	—	Not comparable
AA-LCRSource	63.3%	—	Not comparable
CritPtSource	2.0%	—	Not comparable

KnowledgeQwen3 235B 2507 wins

12 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	Result
GPQASource	86%	77.5%	GLM-5 leads
GPQA-DSource	86.0%	—	Not comparable
SuperGPQASource	66.8%	62.6%	GLM-5 leads
MMLU-ProSource	85.7%	83%	GLM-5 leads
MMLU-Pro (Arcee)Source	85.8%	—	Not comparable
HLESource	50.4%	—	Not comparable
Artificial Analysis Intelligence IndexSource	39.5%	—	Not comparable
AA-GPQA DiamondSource	82.0%	—	Not comparable
AA-HLESource	27.2%	—	Not comparable
AA-Omniscience IndexSource	2.0%	—	Not comparable
AA-Omniscience AccuracySource	26.9%	—	Not comparable
AA-Omniscience Hallucination RateSource	34.0%	—	Not comparable

Math

8 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	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%	—	Not comparable
FrontierMath v2 (Tier 4)Source	2.100%	—	Not comparable

MultilingualGLM-5 wins

2 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	Result
MMLU-ProXSource	83.1%	79.4%	GLM-5 leads
NOVA-63Source	55.1%	—	Not comparable

Multimodal

1 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	Result
Design Arena WebsiteSource	1278	—	Not comparable

Inst. Following

2 benchmarks

Benchmark	GLM-5	Qwen3 235B 2507	Result
IFEvalSource	92.6%	—	Not comparable
AA-IFBenchSource	72.3%	—	Not comparable

Frequently Asked Questions (3)

Which is better, GLM-5 or Qwen3 235B 2507?

GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 56.02. The biggest single separator in this matchup is GPQA, where the scores are 86% and 77.5%.

Which is better for knowledge tasks, GLM-5 or Qwen3 235B 2507?

Qwen3 235B 2507 has the edge for knowledge tasks in this comparison, averaging 78.9 versus 66.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.

Which is better for multilingual tasks, GLM-5 or Qwen3 235B 2507?

GLM-5 has the edge for multilingual tasks in this comparison, averaging 83.1 versus 79.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.

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