Model comparison
GLM-4.7 vs MAI-Thinking-1
Head-to-head evidence from 5 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-4.7 #32; MAI-Thinking-1 #28
BenchAlign evidence: GLM-4.7 supported; MAI-Thinking-1 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. 26 results are unique to GLM-4.7; 9 to MAI-Thinking-1.
Updated July 16, 2026- Shared results
- 5
- GLM-4.7 only
- 26
- MAI-Thinking-1 only
- 9
- Comparable categories
- 4 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice 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 is clearly ahead on the provisional aggregate, 63 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 75.4 against 65.5. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 46%. MAI-Thinking-1 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
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 | GLM-4.7 | Δ | MAI-Thinking-1 |
|---|---|---|---|
| Math | GLM-4.71.8 | Margin→ 87.9 | MAI-Thinking-189.7 |
| Knowledge | GLM-4.752.1 | Margin→ 20.4 | MAI-Thinking-172.5 |
| Coding | GLM-4.775.4 | Margin← 9.9 | MAI-Thinking-165.5 |
| Agentic | GLM-4.745.7 | Margin→ 0.3 | MAI-Thinking-146.0 |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | MAI-Thinking-185.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 41%B 46%Winner: MAI-Thinking-1Δ 5Terminal-Bench 2.0: GLM-4.7 scored 41%; MAI-Thinking-1 scored 46%. MAI-Thinking-1 wins this benchmark. - Source ↗
GPQA
KnowledgeA 85.7%B 84.2%Winner: GLM-4.7Δ 1.5GPQA: GLM-4.7 scored 85.7%; MAI-Thinking-1 scored 84.2%. GLM-4.7 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 84.3%B 85%Winner: MAI-Thinking-1Δ 0.7MMLU-Pro: GLM-4.7 scored 84.3%; MAI-Thinking-1 scored 85%. MAI-Thinking-1 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.8%B 73.5%Winner: GLM-4.7Δ 0.3SWE-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.
| Metric | GLM-4.7 | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | MAI-Thinking-1256K | MAI-Thinking-1 lists the larger context window. |
Benchmark Deep Dive
AgenticMAI-Thinking-1 wins8 benchmarks
| Benchmark | GLM-4.7 | MAI-Thinking-1 | Result |
|---|---|---|---|
| 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 wins10 benchmarks
| Benchmark | GLM-4.7 | MAI-Thinking-1 | Result |
|---|---|---|---|
| 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 |
| Terminal-Bench HardSource | 31.8% | — | Not comparable |
| AA-SciCodeSource | 45.1% | — | Not comparable |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| LiveCodeBench v6Source | — | 87.7% | Not comparable |
| SWE-bench ProSource | — | 52.8% | Not comparable |
| Terminal-Bench 2.0Source | — | 46.0% | Not comparable |
Reasoning3 benchmarks
KnowledgeMAI-Thinking-1 wins11 benchmarks
| Benchmark | GLM-4.7 | MAI-Thinking-1 | Result |
|---|---|---|---|
| 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 wins5 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | MAI-Thinking-1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | — | Not comparable |
Frequently Asked Questions (5)
Which is better, GLM-4.7 or MAI-Thinking-1?
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 63 to 58. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 46%.
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 52.1. 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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