Model comparison
GLM-5.1 vs Laguna S 2.1
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-5.1 #18 (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-5.1 and Laguna S 2.1 share 2 comparable benchmark results. 2 of 8 categories are comparable. 34 results are unique to GLM-5.1; 4 to Laguna S 2.1.
Updated July 21, 2026- Shared results
- 2
- GLM-5.1 only
- 34
- Laguna S 2.1 only
- 4
- Comparable categories
- 2 / 8
Treat this as a split decision. GLM-5.1 makes more sense if coding is the priority; Laguna S 2.1 is the better fit if agentic is the priority or you want the cheaper token bill.
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-5.1 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.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.10 input / $0.20 output per 1M tokens for Laguna S 2.1. That is roughly 22.0x on output cost alone. Laguna S 2.1 gives you the larger context window at 1M, compared with 203K for GLM-5.1.
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-5.1 | Δ | Laguna S 2.1 |
|---|---|---|---|
| Agentic | GLM-5.165.4 | Margin→ 4.8 | Laguna S 2.170.2 |
| Coding | GLM-5.161.3 | Margin← 1.9 | Laguna S 2.159.4 |
| Knowledge | GLM-5.152.3 | MarginNo overlap | Laguna S 2.1Not measured |
| Math | GLM-5.162.0 | 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 63.5%B 70.2%Winner: Laguna S 2.1Δ 6.7Terminal-Bench 2.0: GLM-5.1 scored 63.5%; Laguna S 2.1 scored 70.2%. Laguna S 2.1 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.4%B 59.4%Winner: Laguna S 2.1Δ 1SWE-bench Pro: GLM-5.1 scored 58.4%; Laguna S 2.1 scored 59.4%. 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-5.1 | Laguna S 2.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | Laguna S 2.1$0.1 input / $0.2 output | Laguna S 2.1 has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.1Not available | Laguna S 2.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | Laguna S 2.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | Laguna S 2.11M | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins13 benchmarks
| Benchmark | GLM-5.1 | Laguna S 2.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | 70.2% | Laguna S 2.1 leads |
| BrowseCompSource | 68% | — | Not comparable |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| AA Agentic IndexSource | 29.9% | — | Not comparable |
| τ²-bench resultsSource | 97.7% | — | Not comparable |
| GDPval-AASource | 37.8% | — | Not comparable |
| Gert LabsSource | 60.11% | — | Not comparable |
| GDPval-AASource | 1257 | — | Not comparable |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
| Toolathlon-VerifiedSource | — | 49.7% | Not comparable |
CodingGLM-5.1 wins9 benchmarks
| Benchmark | GLM-5.1 | Laguna S 2.1 | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | 59.4% | Laguna S 2.1 leads |
| NL2RepoSource | 42.7% | — | Not comparable |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | — | Not comparable |
| AA Coding IndexSource | 55.8% | — | Not comparable |
| AA-SciCodeSource | 43.8% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 70.2% | Not comparable |
| SWE MultilingualSource | — | 78.5% | Not comparable |
| deepSweSource | — | 40.4% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GLM-5.1 | Laguna S 2.1 | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | — | Not comparable |
| HLESource | 52.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 40.2% | — | Not comparable |
| AA-GPQA DiamondSource | 86.8% | — | Not comparable |
| AA-HLESource | 28.0% | — | Not comparable |
| AA-Omniscience IndexSource | 1.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 24.2% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 29.4% | — | Not comparable |
Math6 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5.1 | Laguna S 2.1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1305 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | Laguna S 2.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-5.1 or Laguna S 2.1?
GLM-5.1 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-5.1 or Laguna S 2.1?
GLM-5.1 has the edge for coding in this comparison, averaging 61.3 versus 59.4. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or Laguna S 2.1?
Laguna S 2.1 has the edge for agentic tasks in this comparison, averaging 70.2 versus 65.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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