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Model comparison

GLM-4.7 vs MAI-Thinking-1

Data verified

Head-to-head evidence from 5 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
No comparison
N/A
1 category wins3 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); MAI-Thinking-1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and MAI-Thinking-1 share 5 comparable benchmark results. 4 of 8 categories are comparable. 25 results are unique to GLM-4.7; 8 to MAI-Thinking-1.

Updated July 23, 2026
Shared results
5
GLM-4.7 only
25
MAI-Thinking-1 only
8
Comparable categories
4 / 8

Treat this as a split decision. GLM-4.7 makes more sense if coding is the priority; MAI-Thinking-1 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 5 shared benchmark results across 4 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 and MAI-Thinking-1 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.

MAI-Thinking-1 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 MAI-Thinking-1
CategoryGLM-4.7ΔMAI-Thinking-1
MathGLM-4.71.8Margin→ 87.9MAI-Thinking-189.7
KnowledgeGLM-4.751.8Margin→ 20.7MAI-Thinking-172.5
CodingGLM-4.775.4Margin← 9.9MAI-Thinking-165.5
AgenticGLM-4.745.7Margin→ 0.3MAI-Thinking-146.0
Inst. FollowingGLM-4.7Not measuredMarginNo overlapMAI-Thinking-185.0

Decisive benchmark drivers

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

More
A · GLM-4.7B · MAI-Thinking-1

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 46%
    Winner: MAI-Thinking-1Δ 5
    Terminal-Bench 2.0: GLM-4.7 scored 41%; MAI-Thinking-1 scored 46%. MAI-Thinking-1 wins this benchmark.

  2. GPQA

    Knowledge
    Source ↗
    A 85.7%B 84.2%
    Winner: GLM-4.7Δ 1.5
    GPQA: GLM-4.7 scored 85.7%; MAI-Thinking-1 scored 84.2%. GLM-4.7 wins this benchmark.

  3. MMLU-Pro

    Knowledge
    Source ↗
    A 84.3%B 85%
    Winner: MAI-Thinking-1Δ 0.7
    MMLU-Pro: GLM-4.7 scored 84.3%; MAI-Thinking-1 scored 85%. MAI-Thinking-1 wins this benchmark.

  4. SWE-bench Verified

    Coding
    Source ↗
    A 73.8%B 73.5%
    Winner: GLM-4.7Δ 0.3
    SWE-bench Verified: GLM-4.7 scored 73.8%; MAI-Thinking-1 scored 73.5%. GLM-4.7 wins this benchmark.

Operational comparison

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

MetricGLM-4.7MAI-Thinking-1Comparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputMAI-Thinking-1Not availableA complete price comparison is not available.
Generation speedtokens per secondGLM-4.782 tok/sMAI-Thinking-1Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sMAI-Thinking-1Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KMAI-Thinking-1256KMAI-Thinking-1 lists the larger context window.

Benchmark Deep Dive

AgenticMAI-Thinking-1 wins
BenchmarkGLM-4.7MAI-Thinking-1Result
Terminal-Bench 2.0Source 41%46%MAI-Thinking-1 leads
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 1165—Not comparable
CodingGLM-4.7 wins
BenchmarkGLM-4.7MAI-Thinking-1Result
SWE-bench VerifiedSource 73.8%73.5%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
SWE-bench ProSource —52.8%Not comparable
Terminal-Bench 2.0Source —46.0%Not comparable
Reasoning
BenchmarkGLM-4.7MAI-Thinking-1Result
AA-LCRSource 64.0%—Not comparable
CritPtSource 1.7%—Not comparable
Graphwalks BFS 128KSource —90%Not comparable
KnowledgeMAI-Thinking-1 wins
BenchmarkGLM-4.7MAI-Thinking-1Result
GPQASource 85.7%84.2%GLM-4.7 leads
MMLU-ProSource 84.3%85%MAI-Thinking-1 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 —84.2%Not comparable
SimpleQASource —31%Not comparable
MathMAI-Thinking-1 wins
BenchmarkGLM-4.7MAI-Thinking-1Result
AIME 2025Source 95.7%97%MAI-Thinking-1 leads
FrontierMath v2 (Tiers 1-3)Source 2.439%—Not comparable
FrontierMath v2 (Tier 4)Source 0.000%—Not comparable
AIME26Source —94.5%Not comparable
HMMT Feb 2026Source —84.9%Not comparable
Multimodal
BenchmarkGLM-4.7MAI-Thinking-1Result
Design Arena WebsiteSource 1255—Not comparable
Inst. Following
BenchmarkGLM-4.7MAI-Thinking-1Result
AA-IFBenchSource 67.9%—Not comparable
IFBenchSource —85%Not comparable
Frequently Asked Questions (5)

Which is better, GLM-4.7 or MAI-Thinking-1?

GLM-4.7 and MAI-Thinking-1 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 MAI-Thinking-1?

MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 72.5 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 MAI-Thinking-1?

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 65.5. 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 MAI-Thinking-1?

MAI-Thinking-1 has the edge for math in this comparison, averaging 89.7 versus 1.8. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-4.7 or MAI-Thinking-1?

MAI-Thinking-1 has the edge for agentic tasks in this comparison, averaging 46 versus 45.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

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