Model comparison
GLM-5 vs o3-pro
GLM-5 is the stronger model overall, scoring 45.9 to 42.7 on the Noometry Index.
Last verified . 6 shared benchmarks.
Summary
- They share 6 benchmarks with published results for both. GLM-5 scores higher in 3 categories and o3-pro in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.1 to 44.6.
- The biggest single-benchmark swing is ARC-AGI-1: 44.7% for GLM-5 and 59.3% for o3-pro.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $20 / $80 for o3-pro.
- GLM-5 accepts more context: 205K tokens versus 200K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | o3-pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 45.9 | 42.7 |
| Released | 2026-02-11 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | 205K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $1 | $20 |
| Output $ / M tokens | $3.20 | $80 |
| Results tracked | 45 | 12 |
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Category by category
Coding o3-pro leads
GLM-5: 49.0 (#47), o3-pro: 55.1 (#22)
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| WeirdML | 48.2% | 58.2% |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| Aider Polyglot | — | 84.9% |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| LMArena Coding | 1461 | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Not comparable
GLM-5: 30.9 (#65), o3-pro: —
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#100), o3-pro: 24.1 (#152)
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| ARC-AGI-2 | 4.9% | 4.9% |
| Kagi LLM Benchmark | 75% | 72.1% |
| ARC-AGI-1 | 44.7% | 59.3% |
| Epoch Capabilities Index | 145.84 | 147.43 |
| SimpleBench | 53.2% | — |
| NYT Connections (extended) | 74.8% | — |
| Chess Puzzles | 10% | — |
| LMArena Hard Prompts | 1451 | — |
| DTBench | — | 86.9% |
| LMCA | — | 38.5% |
| ForecastBench | 61 | — |
Math Not comparable
GLM-5: 44.8 (#66), o3-pro: —
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| LMArena Math | 1440 | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.2 (#59), o3-pro: 29.0 (#234)
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| Vectara Hallucination Rate | 10.1% | 23.3% |
| GPQA Diamond | 87.8% | — |
| Confabulations | — | 14.2% |
| LMArena Expert | 1454 | — |
Multilingual Not comparable
GLM-5: 53.9 (#57), o3-pro: —
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| LMArena Non-English | 1430 | — |
| LMArena Chinese | 1511 | — |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1421 | — |
| LMArena Russian | 1436 | — |
| LMArena Spanish | 1452 | — |
Instruction Following Not comparable
GLM-5: 75.4 (#64), o3-pro: —
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| LMArena Instruction Following | 1427 | — |
Long Context o3-pro leads
GLM-5: 44.6 (#58), o3-pro: 72.1 (#1)
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| CL-bench | 18.7% | — |
| LMArena Longer Query | 1446 | — |
Writing & Preference GLM-5 leads
GLM-5: 66.2 (#38), o3-pro: 57.1 (#128)
| Benchmark | GLM-5 | o3-pro |
|---|---|---|
| LMArena Text | 1446 | — |
| LMArena Creative Writing | 1438 | — |
| Short-Story Creative Writing | — | 84.4% |
| EQ-Bench Creative Writing | 1601 | — |
| LMArena Multi-Turn | 1456 | — |
Frequently asked questions
Is GLM-5 better than o3-pro?
GLM-5 is the stronger model overall, scoring 45.9 to 42.7 on the Noometry Index.
Which is cheaper, GLM-5 or o3-pro?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; o3-pro lists at $20 and $80.
Is GLM-5 or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.1 versus 49.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5 does, with 205K tokens against 200K.
How many benchmarks do GLM-5 and o3-pro share?
6 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and o3-pro has 12.