Model comparison
GLM-5.2 vs SWE-1.7
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-5.2 #37 (Estimated); SWE-1.7 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.2 and SWE-1.7 share 2 comparable benchmark results. 1 of 8 categories are comparable. 41 results are unique to GLM-5.2; 2 to SWE-1.7.
Updated July 23, 2026- Shared results
- 2
- GLM-5.2 only
- 41
- SWE-1.7 only
- 2
- Comparable categories
- 1 / 8
Treat this as a split decision. GLM-5.2 makes more sense if you need the larger 1M context window; SWE-1.7 is the better fit if agentic is the priority.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 2 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5.2 and SWE-1.7 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-5.2 gives you the larger context window at 1M, compared with 256K for SWE-1.7.
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-5.2 | Δ | SWE-1.7 |
|---|---|---|---|
| Agentic | GLM-5.281.0 | Margin→ 0.5 | SWE-1.781.5 |
| Coding | GLM-5.262.1 | MarginNo overlap | SWE-1.7Not measured |
| Knowledge | GLM-5.259.6 | MarginNo overlap | SWE-1.7Not measured |
| Math | GLM-5.295.9 | MarginNo overlap | SWE-1.7Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 81%B 81.5%Winner: SWE-1.7Δ 0.5Terminal-Bench 2.0: GLM-5.2 scored 81%; SWE-1.7 scored 81.5%. SWE-1.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.2 | SWE-1.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.2$1.4 input / $4.4 output | SWE-1.7Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-5.2Not available | SWE-1.7Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.2Not available | SWE-1.7Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.21M | SWE-1.7256K | GLM-5.2 lists the larger context window. |
Benchmark Deep Dive
AgenticSWE-1.7 wins17 benchmarks
| Benchmark | GLM-5.2 | SWE-1.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 81% | 81.5% | SWE-1.7 leads |
| MCP AtlasSource | 76.8% | — | Not comparable |
| ToolathlonSource | 48.2% | — | Not comparable |
| AA Agentic IndexSource | 43.1% | — | Not comparable |
| τ²-bench resultsSource | 99.1% | — | Not comparable |
| GDPval-AASource | 50.7% | — | Not comparable |
| GDPval-AASource | 1514 | — | Not comparable |
| APEX-Agents-AASource | 33.7% | — | Not comparable |
| ResearchClawBenchSource | 20.7% | — | Not comparable |
| AA BriefcaseSource | 1260 | — | Not comparable |
| AA AutomationBenchSource | 27.8% | — | Not comparable |
| AA EnterpriseOps-GymSource | 42.7% | — | Not comparable |
| AA Harvey LABSource | 91.0% | — | Not comparable |
| AA ITBenchSource | 42.7% | — | Not comparable |
| AA Tau3 BankingSource | 26.8% | — | Not comparable |
| terminalBenchHardSource | 50.8% | — | Not comparable |
| aaTerminalBench21Source | 77.9% | — | Not comparable |
Coding9 benchmarks
| Benchmark | GLM-5.2 | SWE-1.7 | Result |
|---|---|---|---|
| SWE-bench ProSource | 62.1% | — | Not comparable |
| NL2RepoSource | 48.9% | — | Not comparable |
| Terminal-Bench 2.0Source | 81.0% | 81.5% | SWE-1.7 leads |
| ProgramBenchSource | 63.7% | — | Not comparable |
| cursorBench32Source | 55.0% | — | Not comparable |
| AA Coding IndexSource | 68.8% | — | Not comparable |
| AA-SciCodeSource | 50.5% | — | Not comparable |
| FrontierCode 1.1 MainSource | — | 42.3% | Not comparable |
| SWE MultilingualSource | — | 77.8% | Not comparable |
Reasoning2 benchmarks
Knowledge11 benchmarks
| Benchmark | GLM-5.2 | SWE-1.7 | Result |
|---|---|---|---|
| GPQASource | 91.2% | — | Not comparable |
| GPQA-DSource | 91.2% | — | Not comparable |
| HLESource | 54.7% | — | Not comparable |
| HLE w/o toolsSource | 40.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 51.1% | — | Not comparable |
| AA-GPQA DiamondSource | 89.5% | — | Not comparable |
| AA-HLESource | 40.1% | — | Not comparable |
| AA-Omniscience IndexSource | 4.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 25.1% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 28.1% | — | Not comparable |
| AA Openness IndexSource | 44.4% | — | Not comparable |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5.2 | SWE-1.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1340 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-5.2 | SWE-1.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.3% | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-5.2 or SWE-1.7?
GLM-5.2 and SWE-1.7 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 agentic tasks, GLM-5.2 or SWE-1.7?
SWE-1.7 has the edge for agentic tasks in this comparison, averaging 81.5 versus 81. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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