Skip to main content

Model comparison

GLM-4.7 vs ZAYA1-74B-Preview

Data verified

Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
No comparison
1 category wins2 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); ZAYA1-74B-Preview unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and ZAYA1-74B-Preview share 3 comparable benchmark results. 3 of 8 categories are comparable. 27 results are unique to GLM-4.7; 4 to ZAYA1-74B-Preview.

Updated July 18, 2026
Shared results
3
GLM-4.7 only
27
ZAYA1-74B-Preview only
4
Comparable categories
3 / 8

Treat this as a split decision. GLM-4.7 makes more sense if coding is the priority; ZAYA1-74B-Preview is the better fit if mathematics is the priority or you need the larger 256K context window.

Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 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 and ZAYA1-74B-Preview finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.

ZAYA1-74B-Preview gives you 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 ZAYA1-74B-Preview
CategoryGLM-4.7ΔZAYA1-74B-Preview
MathGLM-4.71.8Margin 74.6ZAYA1-74B-Preview76.4
CodingGLM-4.775.4Margin 22.2ZAYA1-74B-Preview53.2
KnowledgeGLM-4.751.8Margin 14.3ZAYA1-74B-Preview66.1
AgenticGLM-4.745.7MarginNo overlapZAYA1-74B-PreviewNot measured

Decisive benchmark drivers

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

More
A · GLM-4.7B · ZAYA1-74B-Preview
  1. GPQA

    Knowledge
    Source ↗
    A 85.7%B 57.3%
    Winner: GLM-4.7Δ 28.4
    GPQA: GLM-4.7 scored 85.7%; ZAYA1-74B-Preview scored 57.3%. GLM-4.7 wins this benchmark.
  2. SWE-bench Verified

    Coding
    Source ↗
    A 73.8%B 53.2%
    Winner: GLM-4.7Δ 20.6
    SWE-bench Verified: GLM-4.7 scored 73.8%; ZAYA1-74B-Preview scored 53.2%. GLM-4.7 wins this benchmark.
  3. MMLU-Pro

    Knowledge
    Source ↗
    A 84.3%B 68.1%
    Winner: GLM-4.7Δ 16.2
    MMLU-Pro: GLM-4.7 scored 84.3%; ZAYA1-74B-Preview scored 68.1%. GLM-4.7 wins this benchmark.

Operational comparison

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

MetricGLM-4.7ZAYA1-74B-PreviewComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputZAYA1-74B-Preview$0 input / $0 outputListed prices are equal.
Generation speedtokens per secondGLM-4.782 tok/sZAYA1-74B-PreviewNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sZAYA1-74B-PreviewNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KZAYA1-74B-Preview256KZAYA1-74B-Preview lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkGLM-4.7ZAYA1-74B-PreviewResult
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%Not comparable
Gert LabsSource 39.95%Not comparable
GDPval-AASource 33.3%Not comparable
GDPval-AASource 1165Not comparable
τ²-bench AirlineSource 56.1%Not comparable
CodingGLM-4.7 wins
BenchmarkGLM-4.7ZAYA1-74B-PreviewResult
SWE-bench VerifiedSource 73.8%53.2%GLM-4.7 leads
LiveCodeBenchSource 84.9%Not comparable
SWE-RebenchSource 58.7%Not comparable
AA Coding IndexSource 45.3%Not comparable
AA-SciCodeSource 45.1%Not comparable
AA LiveCodeBenchSource 89.4%Not comparable
LiveCodeBench v6Source 65.7%Not comparable
Reasoning
BenchmarkGLM-4.7ZAYA1-74B-PreviewResult
AA-LCRSource 64.0%Not comparable
CritPtSource 1.7%Not comparable
KnowledgeZAYA1-74B-Preview wins
BenchmarkGLM-4.7ZAYA1-74B-PreviewResult
GPQASource 85.7%57.3%GLM-4.7 leads
MMLU-ProSource 84.3%68.1%GLM-4.7 leads
HLESource 24.8%Not comparable
Artificial Analysis Intelligence IndexSource 33.7%Not comparable
AA-GPQA DiamondSource 85.9%Not comparable
AA-HLESource 25.1%Not comparable
AA-Omniscience IndexSource -34.6%Not comparable
AA-Omniscience AccuracySource 29.3%Not comparable
AA-Omniscience Hallucination RateSource 90.3%Not comparable
GPQA-DSource 57.3%Not comparable
MathZAYA1-74B-Preview wins
BenchmarkGLM-4.7ZAYA1-74B-PreviewResult
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
AIME26Source 76.4%Not comparable
Multimodal
BenchmarkGLM-4.7ZAYA1-74B-PreviewResult
Design Arena WebsiteSource 1258Not comparable
Inst. Following
BenchmarkGLM-4.7ZAYA1-74B-PreviewResult
AA-IFBenchSource 67.9%Not comparable
Frequently Asked Questions (4)

Which is better, GLM-4.7 or ZAYA1-74B-Preview?

GLM-4.7 and ZAYA1-74B-Preview are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.

Which is better for knowledge tasks, GLM-4.7 or ZAYA1-74B-Preview?

ZAYA1-74B-Preview has the edge for knowledge tasks in this comparison, averaging 66.1 versus 51.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-4.7 or ZAYA1-74B-Preview?

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 53.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

Which is better for math, GLM-4.7 or ZAYA1-74B-Preview?

ZAYA1-74B-Preview has the edge for math in this comparison, averaging 76.4 versus 1.8. GLM-4.7 stays close enough that the answer can still flip depending on your workload.

Related Comparisons

Last updated: July 18, 2026

The AI models change fast. We track them for you.

A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.

Free. No spam. Unsubscribe anytime.