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Model comparison

GLM-5 vs Qwen3.6 Plus

Data verified

Head-to-head evidence from 41 shared benchmark results across 8 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

Z.AI
66.06/100
Margin
0.9pts
← winning
65.2/100
2 category wins5 category wins

Public leaderboard positions: GLM-5 #28 (Supported); Qwen3.6 Plus #30 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5 and Qwen3.6 Plus share 41 comparable benchmark results. 7 of 8 categories are comparable. 8 results are unique to GLM-5; 19 to Qwen3.6 Plus.

Updated July 23, 2026
Shared results
41
GLM-5 only
8
Qwen3.6 Plus only
19
Comparable categories
7 / 8

Pick GLM-5 if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if agentic is the priority or you need the larger 1M context window.

Confidence note. This is a partial-evidence comparison with 41 shared benchmark results across 8 evidence categories; 7 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 has the cleaner BenchAlign overall profile here, landing at 66.06 versus 65.2. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

GLM-5's sharpest advantage is in instruction following, where it averages 92.6 against 82.3. The single biggest benchmark swing on the page is HLE, 50.4% to 28.8%. Qwen3.6 Plus does hit back in agentic, so the answer changes if that is the part of the workload you care about most.

Qwen3.6 Plus 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. Qwen3.6 Plus 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 Qwen3.6 Plus
CategoryGLM-5ΔQwen3.6 Plus
Inst. FollowingGLM-592.6Margin 10.3Qwen3.6 Plus82.3
KnowledgeGLM-566.4Margin 9.3Qwen3.6 Plus57.1
AgenticGLM-556.2Margin 5.4Qwen3.6 Plus61.6
MathGLM-556.3Margin 4.2Qwen3.6 Plus60.5
CodingGLM-566.3Margin 4.0Qwen3.6 Plus70.3
MultilingualGLM-583.1Margin 1.6Qwen3.6 Plus84.7
ReasoningGLM-560.8Margin 1.2Qwen3.6 Plus62.0
MultimodalGLM-5Not measuredMarginNo overlapQwen3.6 Plus79.8

Decisive benchmark drivers

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

More
A · GLM-5B · Qwen3.6 Plus
  1. HLE

    Knowledge
    Source ↗
    A 50.4%B 28.8%
    Winner: GLM-5Δ 21.6
    HLE: GLM-5 scored 50.4%; Qwen3.6 Plus scored 28.8%. GLM-5 wins this benchmark.
  2. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 16.434%B 26.207%
    Winner: Qwen3.6 PlusΔ 9.8
    FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; Qwen3.6 Plus scored 26.207%. Qwen3.6 Plus wins this benchmark.
  3. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 2.100%B 8.333%
    Winner: Qwen3.6 PlusΔ 6.2
    FrontierMath v2 (Tier 4): GLM-5 scored 2.100%; Qwen3.6 Plus scored 8.333%. Qwen3.6 Plus wins this benchmark.
  4. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 56.2%B 61.6%
    Winner: Qwen3.6 PlusΔ 5.4
    Terminal-Bench 2.0: GLM-5 scored 56.2%; Qwen3.6 Plus scored 61.6%. Qwen3.6 Plus wins this benchmark.
  5. SuperGPQA

    Knowledge
    Source ↗
    A 66.8%B 71.6%
    Winner: Qwen3.6 PlusΔ 4.8
    SuperGPQA: GLM-5 scored 66.8%; Qwen3.6 Plus scored 71.6%. Qwen3.6 Plus wins this benchmark.

Operational comparison

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

MetricGLM-5Qwen3.6 PlusComparison
Input / output priceUSD per 1M tokensGLM-5$1 input / $3.2 outputQwen3.6 PlusNot availableA complete price comparison is not available.
Generation speedtokens per secondGLM-574 tok/sQwen3.6 PlusNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-51.64 sQwen3.6 PlusNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-5200KQwen3.6 Plus1MQwen3.6 Plus lists the larger context window.

Benchmark Deep Dive

AgenticQwen3.6 Plus wins
BenchmarkGLM-5Qwen3.6 PlusResult
Terminal-Bench 2.0Source 56.2%61.6%Qwen3.6 Plus leads
Claw-EvalSource 57.7%58.8%Qwen3.6 Plus leads
QwenClawBenchSource 54.1%57.2%Qwen3.6 Plus leads
τ³-bench resultsSource 65.6%70.7%Qwen3.6 Plus leads
DeepPlanningSource 14.6%41.5%Qwen3.6 Plus leads
ToolathlonSource 38%39.8%Qwen3.6 Plus leads
MCP AtlasSource 31.1%48.2%Qwen3.6 Plus leads
MCP-TasksSource 60.8%74.1%Qwen3.6 Plus leads
WideResearchSource 69.8%74.3%Qwen3.6 Plus leads
τ²-bench resultsSource 98.2%97.7%GLM-5 leads
CyberGymSource 43.2%Not comparable
APEX-Agents-AASource 14.5%Not comparable
Gert LabsSource 50.99%50.60%GLM-5 leads
VITA-BenchSource 44.3%Not comparable
AA Agentic IndexSource 27.6%Not comparable
GDPval-AASource 31.8%Not comparable
GDPval-AASource 1135Not comparable
ResearchClawBenchSource 18.0%Not comparable
CodingQwen3.6 Plus wins
BenchmarkGLM-5Qwen3.6 PlusResult
SWE-bench VerifiedSource 77.8%78.8%Qwen3.6 Plus leads
SWE-bench Verified*Source 72.8%Not comparable
SWE-bench ProSource 55.1%56.6%Qwen3.6 Plus leads
SWE MultilingualSource 73.3%73.8%Qwen3.6 Plus leads
SWE-RebenchSource 62.8%Not comparable
React Native EvalsSource 74.8%Not comparable
AA-SciCodeSource 46.2%40.7%GLM-5 leads
LiveCodeBench v6Source 87.1%Not comparable
Vibe Code BenchSource 25.56%Not comparable
AA Coding IndexSource 54.5%Not comparable
ReasoningQwen3.6 Plus wins
BenchmarkGLM-5Qwen3.6 PlusResult
LongBench v2Source 60.8%62%Qwen3.6 Plus leads
AI-NeedleSource 63.3%68.3%Qwen3.6 Plus leads
AA-LCRSource 63.3%69.7%Qwen3.6 Plus leads
CritPtSource 2.0%2.9%Qwen3.6 Plus leads
KnowledgeGLM-5 wins
BenchmarkGLM-5Qwen3.6 PlusResult
GPQASource 86%90.4%Qwen3.6 Plus leads
GPQA-DSource 86.0%Not comparable
SuperGPQASource 66.8%71.6%Qwen3.6 Plus leads
MMLU-ProSource 85.7%88.5%Qwen3.6 Plus leads
MMLU-Pro (Arcee)Source 85.8%Not comparable
HLESource 50.4%28.8%GLM-5 leads
Artificial Analysis Intelligence IndexSource 39.5%39.6%Qwen3.6 Plus leads
AA-GPQA DiamondSource 82.0%88.2%Qwen3.6 Plus leads
AA-HLESource 27.2%25.7%GLM-5 leads
AA-Omniscience IndexSource 2.0%2.7%Qwen3.6 Plus leads
AA-Omniscience AccuracySource 26.9%26.2%GLM-5 leads
AA-Omniscience Hallucination RateSource 34.0%32.0%Qwen3.6 Plus leads
MMLU-ReduxSource 94.5%Not comparable
C-EvalSource 93.3%Not comparable
MathQwen3.6 Plus wins
BenchmarkGLM-5Qwen3.6 PlusResult
AIME26Source 95.8%95.3%GLM-5 leads
AIME25 (Arcee)Source 93.3%Not comparable
HMMT Feb 2025Source 97.5%96.7%GLM-5 leads
HMMT Nov 2025Source 96.9%94.6%GLM-5 leads
HMMT Feb 2026Source 86.4%87.8%Qwen3.6 Plus leads
MMAnswerBenchSource 82.5%83.8%Qwen3.6 Plus leads
FrontierMath v2 (Tiers 1-3)Source 16.434%26.207%Qwen3.6 Plus leads
FrontierMath v2 (Tier 4)Source 2.100%8.333%Qwen3.6 Plus leads
MultilingualQwen3.6 Plus wins
BenchmarkGLM-5Qwen3.6 PlusResult
MMLU-ProXSource 83.1%84.7%Qwen3.6 Plus leads
NOVA-63Source 55.1%57.9%Qwen3.6 Plus leads
Multimodal
BenchmarkGLM-5Qwen3.6 PlusResult
Design Arena WebsiteSource 12781249GLM-5 leads
MMMUSource 86.0%Not comparable
MMMU-ProSource 78.8%Not comparable
MathVisionSource 88.0%Not comparable
VideoMMMUSource 84.0%Not comparable
ScreenSpot ProSource 68.2%Not comparable
CharXivSource 81.5%Not comparable
V*Source 96.9%Not comparable
AA-MMMU-ProSource 78.0%Not comparable
Inst. FollowingGLM-5 wins
BenchmarkGLM-5Qwen3.6 PlusResult
IFEvalSource 92.6%94.3%Qwen3.6 Plus leads
AA-IFBenchSource 72.3%75.2%Qwen3.6 Plus leads
IFBenchSource 75.8%Not comparable
Frequently Asked Questions (8)

Which is better, GLM-5 or Qwen3.6 Plus?

GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 65.2. The biggest single separator in this matchup is HLE, where the scores are 50.4% and 28.8%.

Which is better for knowledge tasks, GLM-5 or Qwen3.6 Plus?

GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 57.1. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for coding in this comparison, averaging 70.3 versus 66.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.

Which is better for math, GLM-5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for math in this comparison, averaging 60.5 versus 56.3. 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 Qwen3.6 Plus?

Qwen3.6 Plus has the edge for reasoning in this comparison, averaging 62 versus 60.8. Inside this category, AA-LCR is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for agentic tasks in this comparison, averaging 61.6 versus 56.2. Inside this category, DeepPlanning is the benchmark that creates the most daylight between them.

Which is better for instruction following, GLM-5 or Qwen3.6 Plus?

GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 82.3. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.

Which is better for multilingual tasks, GLM-5 or Qwen3.6 Plus?

Qwen3.6 Plus has the edge for multilingual tasks in this comparison, averaging 84.7 versus 83.1. Inside this category, NOVA-63 is the benchmark that creates the most daylight between them.

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Last updated: July 23, 2026

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