Model comparison
GLM-4.7 vs o3-mini
GLM-4.7 is the stronger model overall, scoring 42.0 to 36.7 on the Noometry Index.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. GLM-4.7 scores higher in 7 categories and o3-mini in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.7 leads 60.9 to 50.3.
- The biggest single-benchmark swing is SimpleBench: 47.7% for GLM-4.7 and 22.8% for o3-mini.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- 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-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 42.0 | 36.7 |
| Released | 2025-12-22 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 205K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.60 | $1.10 |
| Output $ / M tokens | $2.20 | $4.40 |
| Results tracked | 36 | 51 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), o3-mini: 40.8 (#132)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| SciCode | 45.1% | 39.8% |
| LMArena Coding | 1454 | 1378 |
| Aider Polyglot | — | 60.4% |
| LMArena WebDev | 1435 | — |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use o3-mini leads
GLM-4.7: 26.5 (#103), o3-mini: 29.6 (#84)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Cybench | — | 22.5% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), o3-mini: 16.3 (#305)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| SimpleBench | 47.7% | 22.8% |
| CritPt | 1.7% | 0.3% |
| Chess Puzzles | 6% | 17% |
| LMArena Hard Prompts | 1443 | 1366 |
| Epoch Capabilities Index | 143.51 | 140.34 |
| ARC-AGI-2 | — | 3% |
| ARC-AGI-1 | — | 34.5% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), o3-mini: 28.1 (#244)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 76.9% |
| LMArena Math | 1423 | 1396 |
| FrontierMath (Feb 2025 set) | 2.4% | 12.4% |
| FrontierMath Tier 4 (v1) | 0% | 4.2% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| ProofBench | 6% | — |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), o3-mini: 38.3 (#146)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| GPQA Diamond | 83.3% | 77% |
| SimpleQA Verified | 32.2% | 15.3% |
| LMArena Expert | 1424 | 1364 |
| Confabulations | — | 17.9% |
| Vectara Hallucination Rate | 11.7% | — |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), o3-mini: 45.7 (#164)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| LMArena Non-English | 1417 | 1319 |
| LMArena Chinese | 1495 | 1379 |
| LMArena French | 1432 | 1334 |
| LMArena German | 1424 | 1303 |
| LMArena Japanese | 1439 | 1286 |
| LMArena Korean | 1399 | 1314 |
| LMArena Russian | 1423 | 1304 |
| LMArena Spanish | 1434 | 1321 |
Instruction Following Too close to call
GLM-4.7: 74.4 (#95), o3-mini: 75.1 (#72)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1411 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), o3-mini: 33.8 (#256)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| LMArena Longer Query | 1432 | 1343 |
| Fiction.LiveBench | — | 50% |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), o3-mini: 50.3 (#182)
| Benchmark | GLM-4.7 | o3-mini |
|---|---|---|
| LMArena Text | 1435 | 1337 |
| LMArena Creative Writing | 1401 | 1286 |
| LMArena Multi-Turn | 1446 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 1413 | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GLM-4.7 better than o3-mini?
GLM-4.7 is the stronger model overall, scoring 42.0 to 36.7 on the Noometry Index.
Which is cheaper, GLM-4.7 or o3-mini?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is GLM-4.7 or o3-mini better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 40.8 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-mini share?
27 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and o3-mini has 51.