Model comparison
GLM-5 vs Laguna XS.2
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #26 (Supported); Laguna XS.2 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Laguna XS.2 share 4 comparable benchmark results. 2 of 8 categories are comparable. 46 results are unique to GLM-5; 1 to Laguna XS.2.
Updated July 17, 2026- Shared results
- 4
- GLM-5 only
- 46
- Laguna XS.2 only
- 1
- Comparable categories
- 2 / 8
Pick GLM-5 if you want the stronger benchmark profile. Laguna XS.2 only becomes the better choice if you want the cheaper token bill or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 4 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 is clearly ahead on the BenchAlign aggregate, 65.98 to 46. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in agentic, where it averages 56.2 against 35.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 35.7%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Laguna XS.2. That is roughly Infinityx on output cost alone. Laguna XS.2 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 XS.2 gives you the larger context window at 256K, 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 XS.2 |
|---|---|---|---|
| Agentic | GLM-556.2 | Margin← 20.5 | Laguna XS.235.7 |
| Coding | GLM-566.3 | Margin← 5.5 | Laguna XS.260.8 |
| Reasoning | GLM-560.8 | MarginNo overlap | Laguna XS.2Not measured |
| Knowledge | GLM-566.6 | MarginNo overlap | Laguna XS.2Not measured |
| Math | GLM-556.3 | MarginNo overlap | Laguna XS.2Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Laguna XS.2Not measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Laguna XS.2Not 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 35.7%Winner: GLM-5Δ 20.5Terminal-Bench 2.0: GLM-5 scored 56.2%; Laguna XS.2 scored 35.7%. GLM-5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 46.3%Winner: GLM-5Δ 8.8SWE-bench Pro: GLM-5 scored 55.1%; Laguna XS.2 scored 46.3%. GLM-5 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 77.8%B 69.9%Winner: GLM-5Δ 7.9SWE-bench Verified: GLM-5 scored 77.8%; Laguna XS.2 scored 69.9%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Laguna XS.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Laguna XS.2$0 input / $0 output | Laguna XS.2 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Laguna XS.2Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Laguna XS.2Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Laguna XS.2256K | Laguna XS.2 lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5 wins13 benchmarks
| Benchmark | GLM-5 | Laguna XS.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 35.7% | GLM-5 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 |
CodingGLM-5 wins9 benchmarks
| Benchmark | GLM-5 | Laguna XS.2 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 69.9% | GLM-5 leads |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 46.3% | GLM-5 leads |
| SWE MultilingualSource | 73.3% | 57.7% | GLM-5 leads |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| Terminal-Bench HardSource | 43.2% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 35.7% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Laguna XS.2 | 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 XS.2 | 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 XS.2 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1282 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-5 or Laguna XS.2?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 65.98 to 46. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 35.7%.
Which is better for coding, GLM-5 or Laguna XS.2?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 60.8. 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 XS.2?
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 35.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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