Model comparison
GLM-4.7 vs Laguna M.1
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #46 (Supported); Laguna M.1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Laguna M.1 share 2 comparable benchmark results. 2 of 8 categories are comparable. 28 results are unique to GLM-4.7; 3 to Laguna M.1.
Updated July 27, 2026- Shared results
- 2
- GLM-4.7 only
- 28
- Laguna M.1 only
- 3
- Comparable categories
- 2 / 8
Treat this as a split decision. GLM-4.7 makes more sense if coding is the priority; Laguna M.1 is the better fit if agentic is the priority or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 2 evidence categories; 2 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 Laguna M.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.
Laguna M.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 | Laguna M.1 |
|---|---|---|---|
| Coding | GLM-4.775.4 | Margin← 10.6 | Laguna M.164.8 |
| Agentic | GLM-4.745.7 | Margin→ 0.1 | Laguna M.145.8 |
| Knowledge | GLM-4.751.8 | MarginNo overlap | Laguna M.1Not measured |
| Math | GLM-4.71.8 | MarginNo overlap | Laguna M.1Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 41%B 45.8%Winner: Laguna M.1Δ 4.8Terminal-Bench 2.0: GLM-4.7 scored 41%; Laguna M.1 scored 45.8%. Laguna M.1 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.8%B 74.6%Winner: Laguna M.1Δ 0.8SWE-bench Verified: GLM-4.7 scored 73.8%; Laguna M.1 scored 74.6%. Laguna M.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Laguna M.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Laguna M.1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.782 tok/s | Laguna M.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Laguna M.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Laguna M.1256K | Laguna M.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna M.1 wins8 benchmarks
| Benchmark | GLM-4.7 | Laguna M.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 45.8% | Laguna M.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 | 1166 | — | Not comparable |
CodingGLM-4.7 wins9 benchmarks
| Benchmark | GLM-4.7 | Laguna M.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 74.6% | Laguna M.1 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 MultilingualSource | — | 63.1% | Not comparable |
| SWE-bench ProSource | — | 49.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 45.8% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | Laguna M.1 | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| 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 |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Laguna M.1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1251 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Laguna M.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-4.7 or Laguna M.1?
GLM-4.7 and Laguna M.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 coding, GLM-4.7 or Laguna M.1?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 64.8. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-4.7 or Laguna M.1?
Laguna M.1 has the edge for agentic tasks in this comparison, averaging 45.8 versus 45.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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