Model comparison
GLM-5 vs o1
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #30 (Supported); o1 #137 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and o1 share 14 comparable benchmark results. 3 of 8 categories are comparable. 35 results are unique to GLM-5; 2 to o1.
Updated July 24, 2026- Shared results
- 14
- GLM-5 only
- 35
- o1 only
- 2
- Comparable categories
- 3 / 8
Pick GLM-5 if you want the stronger benchmark profile. o1 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 14 shared benchmark results across 6 evidence categories; 3 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, 65.59 to 47.39. 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 mathematics, where it averages 56.3 against 9.3. The single biggest benchmark swing on the page is GPQA, 86% to 75.7%. o1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 18.8x on output cost alone. o1 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 |
|---|---|---|---|
| Math | GLM-556.3 | Margin← 47.0 | o19.3 |
| Knowledge | GLM-566.4 | Margin→ 9.3 | o175.7 |
| Inst. Following | GLM-592.6 | Margin← 0.4 | o192.2 |
| Agentic | GLM-556.2 | MarginNo overlap | o1Not measured |
| Coding | GLM-566.3 | MarginNo overlap | o1Not measured |
| Reasoning | GLM-560.8 | MarginNo overlap | o1Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | o1Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 86%B 75.7%Winner: GLM-5Δ 10.3GPQA: GLM-5 scored 86%; o1 scored 75.7%. GLM-5 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 16.434%B 9.310%Winner: GLM-5Δ 7.1FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; o1 scored 9.310%. GLM-5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 92.6%B 92.2%Winner: GLM-5Δ 0.4IFEval: GLM-5 scored 92.6%; o1 scored 92.2%. 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 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | o1$15 input / $60 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | o198 tok/s | o1 has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-51.64 s | o132.29 s | GLM-5 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-5200K | o1200K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | o1 | 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% | 62.6% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
Coding8 benchmarks
| Benchmark | GLM-5 | o1 | 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% | 35.8% | GLM-5 leads |
| AA Coding IndexSource | — | 39.7% | Not comparable |
Reasoning4 benchmarks
Knowledgeo1 wins13 benchmarks
| Benchmark | GLM-5 | o1 | Result |
|---|---|---|---|
| GPQASource | 86% | 75.7% | 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% | 23.4% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 74.7% | GLM-5 leads |
| AA-HLESource | 27.2% | 7.7% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -10.5% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 34.7% | o1 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 69.3% | GLM-5 leads |
| MMLUSource | — | 91.8% | Not comparable |
MathGLM-5 wins8 benchmarks
| Benchmark | GLM-5 | o1 | 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% | 9.310% | GLM-5 leads |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | o1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (4)
Which is better, GLM-5 or o1?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 65.59 to 47.39. The biggest single separator in this matchup is GPQA, where the scores are 86% and 75.7%.
Which is better for knowledge tasks, GLM-5 or o1?
o1 has the edge for knowledge tasks in this comparison, averaging 75.7 versus 66.4. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5 or o1?
GLM-5 has the edge for math in this comparison, averaging 56.3 versus 9.3. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or o1?
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 92.2. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.