Model comparison
GLM-4.7-Flash vs o3-mini
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 36.7 on the Noometry Index.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 4 categories and o3-mini in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.7-Flash leads 36.1 to 28.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 76.9% for o3-mini.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | o3-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.8 | 36.7 |
| Released | 2026-01-19 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 200K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.06 | $1.10 |
| Output $ / M tokens | $0.40 | $4.40 |
| Results tracked | 21 | 51 |
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Category by category
Coding Too close to call
GLM-4.7-Flash: 40.6 (#135), o3-mini: 40.8 (#132)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| LMArena Coding | 1383 | 1378 |
| Aider Polyglot | — | 60.4% |
| SciCode | — | 39.8% |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, o3-mini: 29.6 (#84)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| Cybench | — | 22.5% |
Reasoning GLM-4.7-Flash leads
GLM-4.7-Flash: 20.9 (#229), o3-mini: 16.3 (#305)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| Chess Puzzles | 0% | 17% |
| LMArena Hard Prompts | 1356 | 1366 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| ARC-AGI-1 | — | 34.5% |
| CritPt | — | 0.3% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Epoch Capabilities Index | — | 140.34 |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), o3-mini: 28.1 (#244)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 76.9% |
| LMArena Math | 1355 | 1396 |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge o3-mini leads
GLM-4.7-Flash: 35.5 (#184), o3-mini: 38.3 (#146)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| GPQA Diamond | 60.5% | 77% |
| LMArena Expert | 1357 | 1364 |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
| Vectara Hallucination Rate | 9.3% | — |
Multilingual Too close to call
GLM-4.7-Flash: 46.5 (#158), o3-mini: 45.7 (#164)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| LMArena Non-English | 1330 | 1319 |
| LMArena Chinese | 1403 | 1379 |
| LMArena French | 1332 | 1334 |
| LMArena German | 1337 | 1303 |
| LMArena Korean | 1283 | 1314 |
| LMArena Russian | 1332 | 1304 |
| LMArena Spanish | 1350 | 1321 |
| LMArena Japanese | — | 1286 |
Instruction Following o3-mini leads
GLM-4.7-Flash: 70.1 (#167), o3-mini: 75.1 (#72)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1327 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#148), o3-mini: 33.8 (#256)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| LMArena Longer Query | 1345 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference o3-mini leads
GLM-4.7-Flash: 47.4 (#210), o3-mini: 50.3 (#182)
| Benchmark | GLM-4.7-Flash | o3-mini |
|---|---|---|
| LMArena Text | 1351 | 1337 |
| LMArena Creative Writing | 1297 | 1286 |
| LMArena Multi-Turn | 1342 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 1125 | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GLM-4.7-Flash better than o3-mini?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 36.7 on the Noometry Index.
Which is cheaper, GLM-4.7-Flash or o3-mini?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is GLM-4.7-Flash or o3-mini better for coding?
They score almost the same on coding (40.6 vs 40.8); test both on your own repository before choosing.
Which has the bigger context window?
Both accept 200K tokens.
How many benchmarks do GLM-4.7-Flash and o3-mini share?
19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and o3-mini has 51.