Model comparison
GLM-5 vs o1-pro
Head-to-head evidence from 2 shared benchmark results across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #30 (Supported); o1-pro #148 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and o1-pro share 2 comparable benchmark results. 1 of 8 categories are comparable. 47 results are unique to GLM-5; 0 to o1-pro.
Updated July 24, 2026- Shared results
- 2
- GLM-5 only
- 47
- o1-pro only
- 0
- Comparable categories
- 1 / 8
Pick GLM-5 if you want the stronger benchmark profile. o1-pro only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 1 evidence category; 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 is clearly ahead on the BenchAlign aggregate, 65.29 to 45.03. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-pro is also the more expensive model on tokens at $150.00 input / $600.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 187.5x on output cost alone. o1-pro 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.
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 | o1-pro |
|---|---|---|---|
| Knowledge | GLM-566.4 | Margin→ 12.6 | o1-pro79.0 |
| Agentic | GLM-556.2 | MarginNo overlap | o1-proNot measured |
| Coding | GLM-566.3 | MarginNo overlap | o1-proNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | o1-proNot measured |
| Math | GLM-556.3 | MarginNo overlap | o1-proNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | o1-proNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | o1-proNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 86%B 79%Winner: GLM-5Δ 7GPQA: GLM-5 scored 86%; o1-pro scored 79%. 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 | o1-pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | o1-pro$150 input / $600 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | o1-proNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | o1-proNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | o1-pro200K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | o1-pro | Result |
|---|---|---|---|
| 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 | GLM-5 | o1-pro | 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 |
Reasoning4 benchmarks
Knowledgeo1-pro wins12 benchmarks
| Benchmark | GLM-5 | o1-pro | Result |
|---|---|---|---|
| GPQASource | 86% | 79% | GLM-5 leads |
| 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% | 18.9% | GLM-5 leads |
| 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 | o1-pro | 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 | o1-pro | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-5 or o1-pro?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 65.29 to 45.03. The biggest single separator in this matchup is GPQA, where the scores are 86% and 79%.
Which is better for knowledge tasks, GLM-5 or o1-pro?
o1-pro has the edge for knowledge tasks in this comparison, averaging 79 versus 66.4. Inside this category, Artificial Analysis Intelligence Index is the benchmark that creates the most daylight between them.