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Model comparison

GLM-4.7 vs K-Exaone

Data verified

Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

60.98/100
Margin
12.7pts
← winning
LG AI Research
48.32/100
0 category wins0 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); K-Exaone #123 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and K-Exaone share 12 comparable benchmark results. 0 of 8 categories are comparable. 19 results are unique to GLM-4.7; 0 to K-Exaone.

Updated July 17, 2026
Shared results
12
GLM-4.7 only
19
K-Exaone only
0
Comparable categories
0 / 8

Benchmark data for GLM-4.7 and K-Exaone is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

K-Exaone has the larger context window at 256K, 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 K-Exaone
CategoryGLM-4.7ΔK-Exaone
AgenticGLM-4.745.7MarginNo overlapK-ExaoneNot measured
CodingGLM-4.775.4MarginNo overlapK-ExaoneNot measured
KnowledgeGLM-4.752.1MarginNo overlapK-ExaoneNot measured
MathGLM-4.71.8MarginNo overlapK-ExaoneNot measured

Operational comparison

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

MetricGLM-4.7K-ExaoneComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputK-ExaoneNot availableA complete price comparison is not available.
Generation speedtokens per secondGLM-4.782 tok/sK-ExaoneNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sK-ExaoneNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KK-Exaone256KK-Exaone lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkGLM-4.7K-ExaoneResult
Terminal-Bench 2.0Source 41%Not comparable
BrowseCompSource 52%Not comparable
VITA-BenchSource 15.5%Not comparable
AA Agentic IndexSource 25.4%Not comparable
τ²-bench resultsSource 95.9%74.3%GLM-4.7 leads
Gert LabsSource 39.95%Not comparable
GDPval-AASource 33.3%Not comparable
GDPval-AASource 1165Not comparable
Coding
BenchmarkGLM-4.7K-ExaoneResult
SWE-bench VerifiedSource 73.8%Not comparable
LiveCodeBenchSource 84.9%Not comparable
SWE-RebenchSource 58.7%Not comparable
AA Coding IndexSource 45.3%Not comparable
Terminal-Bench HardSource 31.8%22.7%GLM-4.7 leads
AA-SciCodeSource 45.1%35.6%GLM-4.7 leads
AA LiveCodeBenchSource 89.4%Not comparable
Reasoning
BenchmarkGLM-4.7K-ExaoneResult
AA-LCRSource 64.0%55.7%GLM-4.7 leads
CritPtSource 1.7%1.1%GLM-4.7 leads
Knowledge
BenchmarkGLM-4.7K-ExaoneResult
GPQASource 85.7%Not comparable
MMLU-ProSource 84.3%Not comparable
HLESource 24.8%Not comparable
Artificial Analysis Intelligence IndexSource 33.7%24.7%GLM-4.7 leads
AA-GPQA DiamondSource 85.9%78.3%GLM-4.7 leads
AA-HLESource 25.1%13.1%GLM-4.7 leads
AA-Omniscience IndexSource -34.6%-57.9%GLM-4.7 leads
AA-Omniscience AccuracySource 29.3%16.5%GLM-4.7 leads
AA-Omniscience Hallucination RateSource 90.3%89.1%K-Exaone leads
Math
BenchmarkGLM-4.7K-ExaoneResult
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
Multimodal
BenchmarkGLM-4.7K-ExaoneResult
Design Arena WebsiteSource 1260Not comparable
Inst. Following
BenchmarkGLM-4.7K-ExaoneResult
AA-IFBenchSource 67.9%64.7%GLM-4.7 leads
Frequently Asked Questions (3)

Can I compare GLM-4.7 and K-Exaone on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for GLM-4.7 and K-Exaone today?

GLM-4.7: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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Last updated: July 17, 2026

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