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Model comparison

GLM-4.7 vs Qwen3.6-27B

Data verified

Head-to-head evidence from 22 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
Margin
7.3pts
← winning
53.82/100
0 category wins4 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and Qwen3.6-27B share 22 comparable benchmark results. 4 of 8 categories are comparable. 8 results are unique to GLM-4.7; 32 to Qwen3.6-27B.

Updated July 21, 2026
Shared results
22
GLM-4.7 only
8
Qwen3.6-27B only
32
Comparable categories
4 / 8

Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if mathematics is the priority or you need the larger 262K context window.

Confidence note. This is a partial-evidence comparison with 22 shared benchmark results across 5 evidence categories; 4 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 is clearly ahead on the BenchAlign aggregate, 61.16 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.6-27B 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 Qwen3.6-27B
CategoryGLM-4.7ΔQwen3.6-27B
MathGLM-4.71.8Margin→ 87.4Qwen3.6-27B89.2
AgenticGLM-4.745.7Margin→ 13.6Qwen3.6-27B59.3
CodingGLM-4.775.4Margin→ 2.1Qwen3.6-27B77.5
KnowledgeGLM-4.751.8Margin→ 1.5Qwen3.6-27B53.3
MultimodalGLM-4.7Not measuredMarginNo overlapQwen3.6-27B76.7

Decisive benchmark drivers

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

More
A · GLM-4.7B · Qwen3.6-27B

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 59.3%
    Winner: Qwen3.6-27BΔ 18.3
    Terminal-Bench 2.0: GLM-4.7 scored 41%; Qwen3.6-27B scored 59.3%. Qwen3.6-27B wins this benchmark.

  2. SWE-bench Verified

    Coding
    Source ↗
    A 73.8%B 77.2%
    Winner: Qwen3.6-27BΔ 3.4
    SWE-bench Verified: GLM-4.7 scored 73.8%; Qwen3.6-27B scored 77.2%. Qwen3.6-27B wins this benchmark.

  3. GPQA

    Knowledge
    Source ↗
    A 85.7%B 87.8%
    Winner: Qwen3.6-27BΔ 2.1
    GPQA: GLM-4.7 scored 85.7%; Qwen3.6-27B scored 87.8%. Qwen3.6-27B wins this benchmark.

  4. MMLU-Pro

    Knowledge
    Source ↗
    A 84.3%B 86.2%
    Winner: Qwen3.6-27BΔ 1.9
    MMLU-Pro: GLM-4.7 scored 84.3%; Qwen3.6-27B scored 86.2%. Qwen3.6-27B wins this benchmark.

  5. LiveCodeBench

    Coding
    Source ↗
    A 84.9%B 83.9%
    Winner: GLM-4.7Δ 1
    LiveCodeBench: GLM-4.7 scored 84.9%; Qwen3.6-27B scored 83.9%. GLM-4.7 wins this benchmark.

Operational comparison

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

MetricGLM-4.7Qwen3.6-27BComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputQwen3.6-27B$0 input / $0 outputListed prices are equal.
Generation speedtokens per secondGLM-4.782 tok/sQwen3.6-27BNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sQwen3.6-27BNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KQwen3.6-27B262KQwen3.6-27B lists the larger context window.

Benchmark Deep Dive

AgenticQwen3.6-27B wins
BenchmarkGLM-4.7Qwen3.6-27BResult
Terminal-Bench 2.0Source 41%59.3%Qwen3.6-27B leads
BrowseCompSource 52%—Not comparable
VITA-BenchSource 15.5%—Not comparable
AA Agentic IndexSource 25.4%27.0%Qwen3.6-27B leads
τ²-bench resultsSource 95.9%94.2%GLM-4.7 leads
Gert LabsSource 39.95%54.84%Qwen3.6-27B leads
GDPval-AASource 33.3%32.0%GLM-4.7 leads
GDPval-AASource 11651140GLM-4.7 leads
Claw-EvalSource —72.4%Not comparable
QwenClawBenchSource —53.4%Not comparable
QwenWebBenchSource —1487Not comparable
AndroidWorldSource —70.3%Not comparable
CodingQwen3.6-27B wins
BenchmarkGLM-4.7Qwen3.6-27BResult
SWE-bench VerifiedSource 73.8%77.2%Qwen3.6-27B leads
LiveCodeBenchSource 84.9%83.9%GLM-4.7 leads
SWE-RebenchSource 58.7%—Not comparable
AA Coding IndexSource 45.3%53.7%Qwen3.6-27B leads
AA-SciCodeSource 45.1%39.8%GLM-4.7 leads
AA LiveCodeBenchSource 89.4%—Not comparable
SWE MultilingualSource —71.3%Not comparable
SWE-bench ProSource —53.5%Not comparable
Terminal-Bench 2.0Source —59.3%Not comparable
NL2RepoSource —36.2%Not comparable
Reasoning
BenchmarkGLM-4.7Qwen3.6-27BResult
AA-LCRSource 64.0%68.7%Qwen3.6-27B leads
CritPtSource 1.7%1.1%GLM-4.7 leads
KnowledgeQwen3.6-27B wins
BenchmarkGLM-4.7Qwen3.6-27BResult
GPQASource 85.7%87.8%Qwen3.6-27B leads
MMLU-ProSource 84.3%86.2%Qwen3.6-27B leads
HLESource 24.8%24%GLM-4.7 leads
Artificial Analysis Intelligence IndexSource 33.7%37.0%Qwen3.6-27B leads
AA-GPQA DiamondSource 85.9%84.2%GLM-4.7 leads
AA-HLESource 25.1%21.6%GLM-4.7 leads
AA-Omniscience IndexSource -34.6%-19.8%Qwen3.6-27B leads
AA-Omniscience AccuracySource 29.3%19.2%GLM-4.7 leads
AA-Omniscience Hallucination RateSource 90.3%48.3%Qwen3.6-27B leads
MMLU-ReduxSource —93.5%Not comparable
SuperGPQASource —66%Not comparable
C-EvalSource —91.4%Not comparable
MathQwen3.6-27B wins
BenchmarkGLM-4.7Qwen3.6-27BResult
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
HMMT Feb 2025Source —93.8%Not comparable
HMMT Nov 2025Source —90.7%Not comparable
HMMT Feb 2026Source —84.3%Not comparable
MMAnswerBenchSource —80.8%Not comparable
AIME26Source —94.1%Not comparable
Multimodal
BenchmarkGLM-4.7Qwen3.6-27BResult
Design Arena WebsiteSource 1255—Not comparable
MMMUSource —82.9%Not comparable
MMMU-ProSource —75.8%Not comparable
RealWorldQASource —84.1%Not comparable
DynaMathSource —85.6%Not comparable
MStarSource —81.4%Not comparable
SimpleVQASource —56.1%Not comparable
CharXivSource —78.4%Not comparable
CC-OCRSource —81.2%Not comparable
CountBenchSource —97.8%Not comparable
RefCOCO (avg)Source —92.5%Not comparable
ERQASource —62.5%Not comparable
Video-MME (with subtitle)Source —87.7%Not comparable
VideoMMMUSource —84.4%Not comparable
MLVU (M-Avg)Source —86.6%Not comparable
V*Source —94.7%Not comparable
AA-MMMU-ProSource —74.6%Not comparable
Inst. Following
BenchmarkGLM-4.7Qwen3.6-27BResult
AA-IFBenchSource 67.9%67.6%GLM-4.7 leads
Frequently Asked Questions (5)

Which is better, GLM-4.7 or Qwen3.6-27B?

GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 53.82. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 59.3%.

Which is better for knowledge tasks, GLM-4.7 or Qwen3.6-27B?

Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 53.3 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 Qwen3.6-27B?

Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 75.4. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for math, GLM-4.7 or Qwen3.6-27B?

Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 1.8. GLM-4.7 stays close enough that the answer can still flip depending on your workload.

Which is better for agentic tasks, GLM-4.7 or Qwen3.6-27B?

Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-4.7
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even—
Model the full break-even

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