Model comparison
Agents-A1 vs GLM-5
Head-to-head evidence from 3 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Agents-A1 unranked (Not scored); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Agents-A1 and GLM-5 share 3 comparable benchmark results. 4 of 8 categories are comparable. 3 results are unique to Agents-A1; 46 to GLM-5.
Updated July 18, 2026- Shared results
- 3
- Agents-A1 only
- 3
- GLM-5 only
- 46
- Comparable categories
- 4 / 8
Treat this as a split decision. Agents-A1 makes more sense if agentic is the priority or you need the larger 262K context window; GLM-5 is the better fit if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 3 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Agents-A1 and GLM-5 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.
Agents-A1 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. Agents-A1 gives you the larger context window at 262K, 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 | Agents-A1 | Δ | GLM-5 |
|---|---|---|---|
| Agentic | Agents-A175.5 | Margin← 19.3 | GLM-556.2 |
| Knowledge | Agents-A147.6 | Margin→ 18.8 | GLM-566.4 |
| Inst. Following | Agents-A194.8 | Margin← 2.2 | GLM-592.6 |
| Reasoning | Agents-A160.2 | Margin→ 0.6 | GLM-560.8 |
| Coding | Agents-A1Not measured | MarginNo overlap | GLM-566.3 |
| Math | Agents-A1Not measured | MarginNo overlap | GLM-556.3 |
| Multilingual | Agents-A1Not measured | MarginNo overlap | GLM-583.1 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 47.6%B 50.4%Winner: GLM-5Δ 2.8HLE: Agents-A1 scored 47.6%; GLM-5 scored 50.4%. GLM-5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 94.8%B 92.6%Winner: Agents-A1Δ 2.2IFEval: Agents-A1 scored 94.8%; GLM-5 scored 92.6%. Agents-A1 wins this benchmark. - Source ↗
LongBench v2
ReasoningA 60.2%B 60.8%Winner: GLM-5Δ 0.6LongBench v2: Agents-A1 scored 60.2%; GLM-5 scored 60.8%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Agents-A1 | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Agents-A1Not available | GLM-5$1 input / $3.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | Agents-A1Not available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Agents-A1Not available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Agents-A1262K | GLM-5200K | Agents-A1 lists the larger context window. |
Benchmark Deep Dive
AgenticAgents-A1 wins16 benchmarks
| Benchmark | Agents-A1 | GLM-5 | Result |
|---|---|---|---|
| BrowseCompSource | 75.5% | — | Not comparable |
| HLE w/ toolsSource | 47.6% | — | Not comparable |
| VITA-BenchSource | 38.8% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 56.2% | Not comparable |
| 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 |
Coding7 benchmarks
| Benchmark | Agents-A1 | GLM-5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 77.8% | Not comparable |
| SWE-bench Verified*Source | — | 72.8% | Not comparable |
| SWE-bench ProSource | — | 55.1% | Not comparable |
| SWE MultilingualSource | — | 73.3% | Not comparable |
| SWE-RebenchSource | — | 62.8% | Not comparable |
| React Native EvalsSource | — | 74.8% | Not comparable |
| AA-SciCodeSource | — | 46.2% | Not comparable |
ReasoningGLM-5 wins4 benchmarks
KnowledgeGLM-5 wins12 benchmarks
| Benchmark | Agents-A1 | GLM-5 | Result |
|---|---|---|---|
| HLESource | 47.6% | 50.4% | GLM-5 leads |
| 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 |
| 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 | Agents-A1 | GLM-5 | 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 | Agents-A1 | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1280 | Not comparable |
Frequently Asked Questions (5)
Which is better, Agents-A1 or GLM-5?
Agents-A1 and GLM-5 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 knowledge tasks, Agents-A1 or GLM-5?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 47.6. Inside this category, HLE is the benchmark that creates the most daylight between them.
Which is better for reasoning, Agents-A1 or GLM-5?
GLM-5 has the edge for reasoning in this comparison, averaging 60.8 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Agents-A1 or GLM-5?
Agents-A1 has the edge for agentic tasks in this comparison, averaging 75.5 versus 56.2. GLM-5 stays close enough that the answer can still flip depending on your workload.
Which is better for instruction following, Agents-A1 or GLM-5?
Agents-A1 has the edge for instruction following in this comparison, averaging 94.8 versus 92.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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