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Model comparison

GLM-4.7 vs Qwen3.5-27B

Data verified

Head-to-head evidence from 18 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
Margin
0.5pts
← winning
60.7/100
1 category wins2 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and Qwen3.5-27B share 18 comparable benchmark results. 3 of 8 categories are comparable. 12 results are unique to GLM-4.7; 10 to Qwen3.5-27B.

Updated July 23, 2026
Shared results
18
GLM-4.7 only
12
Qwen3.5-27B only
10
Comparable categories
3 / 8

Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if knowledge is the priority or you need the larger 262K context window.

Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 5 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

GLM-4.7 has the cleaner BenchAlign overall profile here, landing at 61.16 versus 60.7. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

GLM-4.7's sharpest advantage is in coding, where it averages 75.4 against 64.9. The single biggest benchmark swing on the page is BrowseComp, 52% to 61%. Qwen3.5-27B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.

Qwen3.5-27B gives you the larger context window at 262K, compared with 200K for GLM-4.7.

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-4.7 and Qwen3.5-27B
CategoryGLM-4.7ΔQwen3.5-27B
KnowledgeGLM-4.751.8Margin→ 30.9Qwen3.5-27B82.7
CodingGLM-4.775.4Margin← 10.5Qwen3.5-27B64.9
AgenticGLM-4.745.7Margin→ 6.3Qwen3.5-27B52.0
ReasoningGLM-4.7Not measuredMarginNo overlapQwen3.5-27B60.6
MathGLM-4.71.8MarginNo overlapQwen3.5-27BNot measured
MultilingualGLM-4.7Not measuredMarginNo overlapQwen3.5-27B82.2
Inst. FollowingGLM-4.7Not measuredMarginNo overlapQwen3.5-27B95.0

Decisive benchmark drivers

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

More
A · GLM-4.7B · Qwen3.5-27B

  1. BrowseComp

    Agentic
    Source ↗
    A 52%B 61%
    Winner: Qwen3.5-27BΔ 9
    BrowseComp: GLM-4.7 scored 52%; Qwen3.5-27B scored 61%. Qwen3.5-27B wins this benchmark.

  2. MMLU-Pro

    Knowledge
    Source ↗
    A 84.3%B 86.1%
    Winner: Qwen3.5-27BΔ 1.8
    MMLU-Pro: GLM-4.7 scored 84.3%; Qwen3.5-27B scored 86.1%. Qwen3.5-27B wins this benchmark.

  3. SWE-bench Verified

    Coding
    Source ↗
    A 73.8%B 72.4%
    Winner: GLM-4.7Δ 1.4
    SWE-bench Verified: GLM-4.7 scored 73.8%; Qwen3.5-27B scored 72.4%. GLM-4.7 wins this benchmark.

  4. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 41.6%
    Winner: Qwen3.5-27BΔ 0.6
    Terminal-Bench 2.0: GLM-4.7 scored 41%; Qwen3.5-27B scored 41.6%. Qwen3.5-27B wins this benchmark.

  5. GPQA

    Knowledge
    Source ↗
    A 85.7%B 85.5%
    Winner: GLM-4.7Δ 0.2
    GPQA: GLM-4.7 scored 85.7%; Qwen3.5-27B scored 85.5%. GLM-4.7 wins this benchmark.

Operational comparison

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

MetricGLM-4.7Qwen3.5-27BComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputQwen3.5-27B$0 input / $0 outputListed prices are equal.
Generation speedtokens per secondGLM-4.782 tok/sQwen3.5-27BNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sQwen3.5-27BNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KQwen3.5-27B262KQwen3.5-27B lists the larger context window.

Benchmark Deep Dive

AgenticQwen3.5-27B wins
BenchmarkGLM-4.7Qwen3.5-27BResult
Terminal-Bench 2.0Source 41%41.6%Qwen3.5-27B leads
BrowseCompSource 52%61%Qwen3.5-27B leads
VITA-BenchSource 15.5%—Not comparable
AA Agentic IndexSource 25.4%—Not comparable
τ²-bench resultsSource 95.9%93.9%GLM-4.7 leads
Gert LabsSource 39.95%39.41%GLM-4.7 leads
GDPval-AASource 33.3%—Not comparable
GDPval-AASource 1165—Not comparable
OSWorld-VerifiedSource —56.2%Not comparable
CodingGLM-4.7 wins
BenchmarkGLM-4.7Qwen3.5-27BResult
SWE-bench VerifiedSource 73.8%72.4%GLM-4.7 leads
LiveCodeBenchSource 84.9%—Not comparable
SWE-RebenchSource 58.7%58.9%Qwen3.5-27B leads
AA Coding IndexSource 45.3%—Not comparable
AA-SciCodeSource 45.1%39.5%GLM-4.7 leads
AA LiveCodeBenchSource 89.4%—Not comparable
Reasoning
BenchmarkGLM-4.7Qwen3.5-27BResult
AA-LCRSource 64.0%67.3%Qwen3.5-27B leads
CritPtSource 1.7%0.9%GLM-4.7 leads
LongBench v2Source —60.6%Not comparable
KnowledgeQwen3.5-27B wins
BenchmarkGLM-4.7Qwen3.5-27BResult
GPQASource 85.7%85.5%GLM-4.7 leads
MMLU-ProSource 84.3%86.1%Qwen3.5-27B leads
HLESource 24.8%—Not comparable
Artificial Analysis Intelligence IndexSource 33.7%33.8%Qwen3.5-27B leads
AA-GPQA DiamondSource 85.9%85.8%GLM-4.7 leads
AA-HLESource 25.1%22.2%GLM-4.7 leads
AA-Omniscience IndexSource -34.6%-42.0%GLM-4.7 leads
AA-Omniscience AccuracySource 29.3%21.0%GLM-4.7 leads
AA-Omniscience Hallucination RateSource 90.3%79.7%Qwen3.5-27B leads
SuperGPQASource —65.6%Not comparable
Math
BenchmarkGLM-4.7Qwen3.5-27BResult
AIME 2025Source 95.7%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 2.439%—Not comparable
FrontierMath v2 (Tier 4)Source 0.000%—Not comparable
Multilingual
BenchmarkGLM-4.7Qwen3.5-27BResult
MMLU-ProXSource —82.2%Not comparable
Multimodal
BenchmarkGLM-4.7Qwen3.5-27BResult
Design Arena WebsiteSource 1255—Not comparable
MMMUSource —82.3%Not comparable
MMVUSource —73.3%Not comparable
MathVisionSource —86.0%Not comparable
V*Source —93.7%Not comparable
AA-MMMU-ProSource —75.0%Not comparable
Inst. Following
BenchmarkGLM-4.7Qwen3.5-27BResult
AA-IFBenchSource 67.9%75.6%Qwen3.5-27B leads
IFEvalSource —95%Not comparable
Frequently Asked Questions (4)

Which is better, GLM-4.7 or Qwen3.5-27B?

GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 60.7. The biggest single separator in this matchup is BrowseComp, where the scores are 52% and 61%.

Which is better for knowledge tasks, GLM-4.7 or Qwen3.5-27B?

Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 82.7 versus 51.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-4.7 or Qwen3.5-27B?

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 64.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-4.7 or Qwen3.5-27B?

Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 52 versus 45.7. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.

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