Model comparison
GLM-5 vs Laguna S 2.1
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (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 and Laguna S 2.1 share 3 comparable benchmark results. 2 of 8 categories are comparable. 46 results are unique to GLM-5; 3 to Laguna S 2.1.
Updated July 21, 2026- Shared results
- 3
- GLM-5 only
- 46
- Laguna S 2.1 only
- 3
- Comparable categories
- 2 / 8
Treat this as a split decision. GLM-5 makes more sense if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model; 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 3 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 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 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.10 input / $0.20 output per 1M tokens for Laguna S 2.1. That is roughly 16.0x on output cost alone. Laguna S 2.1 is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Laguna S 2.1 gives you the larger context window at 1M, compared with 200K for GLM-5.
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 | Δ | Laguna S 2.1 |
|---|---|---|---|
| Agentic | GLM-556.2 | Margin→ 14.0 | Laguna S 2.170.2 |
| Coding | GLM-566.3 | Margin← 6.9 | Laguna S 2.159.4 |
| Reasoning | GLM-560.8 | MarginNo overlap | Laguna S 2.1Not measured |
| Knowledge | GLM-566.4 | MarginNo overlap | Laguna S 2.1Not measured |
| Math | GLM-556.3 | MarginNo overlap | Laguna S 2.1Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Laguna S 2.1Not measured |
| Inst. Following | GLM-592.6 | 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 56.2%B 70.2%Winner: Laguna S 2.1Δ 14Terminal-Bench 2.0: GLM-5 scored 56.2%; Laguna S 2.1 scored 70.2%. Laguna S 2.1 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 59.4%Winner: Laguna S 2.1Δ 4.3SWE-bench Pro: GLM-5 scored 55.1%; 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 | Laguna S 2.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 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-574 tok/s | Laguna S 2.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Laguna S 2.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Laguna S 2.11M | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins14 benchmarks
| Benchmark | GLM-5 | Laguna S 2.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 70.2% | Laguna S 2.1 leads |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | — | Not comparable |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| Toolathlon-VerifiedSource | — | 49.7% | Not comparable |
CodingGLM-5 wins9 benchmarks
| Benchmark | GLM-5 | Laguna S 2.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 59.4% | Laguna S 2.1 leads |
| SWE MultilingualSource | 73.3% | 78.5% | Laguna S 2.1 leads |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 70.2% | Not comparable |
| deepSweSource | — | 40.4% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Laguna S 2.1 | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | — | Not comparable |
| AA-GPQA DiamondSource | 82.0% | — | Not comparable |
| AA-HLESource | 27.2% | — | Not comparable |
| AA-Omniscience IndexSource | 2.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.0% | — | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | Laguna S 2.1 | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | Laguna S 2.1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-5 or Laguna S 2.1?
GLM-5 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 or Laguna S 2.1?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 59.4. Inside this category, SWE Multilingual is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or Laguna S 2.1?
Laguna S 2.1 has the edge for agentic tasks in this comparison, averaging 70.2 versus 56.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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