Model comparison
GLM-4.7 vs Laguna S 2.1
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); Laguna S 2.1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Laguna S 2.1 share 1 comparable benchmark result. 2 of 8 categories are comparable. 29 results are unique to GLM-4.7; 5 to Laguna S 2.1.
Updated July 21, 2026- Shared results
- 1
- GLM-4.7 only
- 29
- Laguna S 2.1 only
- 5
- Comparable categories
- 2 / 8
Treat this as a split decision. GLM-4.7 makes more sense if coding is the priority or you want the cheaper token bill; Laguna S 2.1 is the better fit if agentic is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 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 S 2.1 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 S 2.1 is also the more expensive model on tokens at $0.10 input / $0.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. Laguna S 2.1 gives you the larger context window at 1M, 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 S 2.1 |
|---|---|---|---|
| Agentic | GLM-4.745.7 | Margin→ 24.5 | Laguna S 2.170.2 |
| Coding | GLM-4.775.4 | Margin← 16.0 | Laguna S 2.159.4 |
| Knowledge | GLM-4.751.8 | MarginNo overlap | Laguna S 2.1Not measured |
| Math | GLM-4.71.8 | MarginNo overlap | Laguna S 2.1Not 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 70.2%Winner: Laguna S 2.1Δ 29.2Terminal-Bench 2.0: GLM-4.7 scored 41%; Laguna S 2.1 scored 70.2%. Laguna S 2.1 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 S 2.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Laguna S 2.1$0.1 input / $0.2 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.782 tok/s | Laguna S 2.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Laguna S 2.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Laguna S 2.11M | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins9 benchmarks
| Benchmark | GLM-4.7 | Laguna S 2.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 70.2% | Laguna S 2.1 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 |
| Toolathlon-VerifiedSource | — | 49.7% | Not comparable |
CodingGLM-4.7 wins10 benchmarks
| Benchmark | GLM-4.7 | Laguna S 2.1 | Result |
|---|---|---|---|
| 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 |
| AA-SciCodeSource | 45.1% | — | Not comparable |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 70.2% | Not comparable |
| SWE MultilingualSource | — | 78.5% | Not comparable |
| SWE-bench ProSource | — | 59.4% | Not comparable |
| deepSweSource | — | 40.4% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | Laguna S 2.1 | 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 S 2.1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1255 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Laguna S 2.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-4.7 or Laguna S 2.1?
GLM-4.7 and Laguna S 2.1 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 S 2.1?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 59.4. Laguna S 2.1 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GLM-4.7 or Laguna S 2.1?
Laguna S 2.1 has the edge for agentic tasks in this comparison, averaging 70.2 versus 45.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.