Model comparison
GLM-4.7 vs o3-pro
GLM-4.7 and o3-pro score almost the same on the Noometry Index (42.0 vs 42.9), so choose on price, context window or the category you care about most.
Last verified . 2 shared benchmarks.
Summary
- They share 2 benchmarks with published results for both. GLM-4.7 scores higher in 3 categories and o3-pro in 2 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 42.8.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 11.7% for GLM-4.7 and 23.3% for o3-pro.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $20 / $80 for o3-pro.
- GLM-4.7 accepts more context: 205K tokens versus 200K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | o3-pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 42.0 | 42.9 |
| Released | 2025-12-22 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | 205K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.60 | $20 |
| Output $ / M tokens | $2.20 | $80 |
| Results tracked | 36 | 12 |
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Category by category
Coding o3-pro leads
GLM-4.7: 44.0 (#79), o3-pro: 55.5 (#24)
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| Aider Polyglot | — | 84.9% |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| WeirdML | — | 58.2% |
| LMArena Coding | 1454 | — |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Not comparable
GLM-4.7: 26.5 (#103), o3-pro: —
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning Too close to call
GLM-4.7: 24.3 (#164), o3-pro: 23.8 (#171)
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| Epoch Capabilities Index | 143.51 | 147.42 |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | 47.7% | — |
| Kagi LLM Benchmark | — | 72.1% |
| ARC-AGI-1 | — | 59.3% |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| LMArena Hard Prompts | 1443 | — |
| DTBench | — | 86.9% |
| LMCA | — | 38.5% |
Math Not comparable
GLM-4.7: 38.6 (#135), o3-pro: —
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| LMArena Math | 1423 | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), o3-pro: 29.5 (#238)
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| Vectara Hallucination Rate | 11.7% | 23.3% |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Confabulations | — | 14.2% |
| LMArena Expert | 1424 | — |
Multilingual Not comparable
GLM-4.7: 52.8 (#79), o3-pro: —
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1432 | — |
| LMArena German | 1424 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1434 | — |
Instruction Following Not comparable
GLM-4.7: 74.4 (#95), o3-pro: —
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| LMArena Instruction Following | 1411 | — |
Long Context o3-pro leads
GLM-4.7: 42.8 (#116), o3-pro: 72.2 (#1)
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
| LMArena Longer Query | 1432 | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), o3-pro: 57.1 (#133)
| Benchmark | GLM-4.7 | o3-pro |
|---|---|---|
| LMArena Text | 1435 | — |
| LMArena Creative Writing | 1401 | — |
| Short-Story Creative Writing | — | 84.4% |
| EQ-Bench Creative Writing | 1413 | — |
| LMArena Multi-Turn | 1446 | — |
Frequently asked questions
Is GLM-4.7 better than o3-pro?
GLM-4.7 and o3-pro score almost the same on the Noometry Index (42.0 vs 42.9), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-4.7 or o3-pro?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; o3-pro lists at $20 and $80.
Is GLM-4.7 or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 200K.
How many benchmarks do GLM-4.7 and o3-pro share?
2 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and o3-pro has 12.