Model comparison
GLM-4.7-Flash vs o1
o1 is the stronger model overall, scoring 40.6 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 181× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and o1 in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where o1 leads 50.3 to 40.9.
- The biggest single-benchmark swing is GPQA Diamond: 60.5% for GLM-4.7-Flash and 76.8% for o1.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $15 / $60 for o1.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | o1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.4 | 40.6 |
| Released | 2026-01-19 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 200K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.06 | $15 |
| Output $ / M tokens | $0.40 | $60 |
| Results tracked | 21 | 52 |
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Category by category
Coding o1 leads
GLM-4.7-Flash: 40.0 (#133), o1: 45.7 (#64)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| LMArena Coding | 1383 | 1367 |
| Aider Polyglot | — | 61.7% |
| WeirdML | — | 47.6% |
| LiveBench Coding | — | 69.7% |
| CadEval | — | 56% |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, o1: 24.3 (#111)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| Cybench | — | 10% |
| METR Time Horizons | — | 51.1% |
Reasoning o1 leads
GLM-4.7-Flash: 20.2 (#224), o1: 27.5 (#103)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| Chess Puzzles | 0% | 15% |
| LMArena Hard Prompts | 1356 | 1372 |
| SimpleBench | — | 41.7% |
| ARC-AGI-1 | — | 30.7% |
| EnigmaEval | — | 5.7% |
| LiveBench Reasoning | — | 91.6% |
| DTBench | — | 74.7% |
| LiveBench Data Analysis | — | 65.5% |
| LMCA | — | 22.3% |
| Epoch Capabilities Index | — | 141.92 |
| LiveBench | — | 75.7% |
Math Too close to call
GLM-4.7-Flash: 34.4 (#159), o1: 34.9 (#150)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 73.3% |
| LMArena Math | 1354 | 1388 |
| FrontierMath (Tiers 1-3) | — | 14.7% |
| LiveBench Math | — | 80.3% |
| MATH Level 5 | — | 94.7% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
Knowledge o1 leads
GLM-4.7-Flash: 35.4 (#177), o1: 41.5 (#101)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| GPQA Diamond | 60.5% | 76.8% |
| LMArena Expert | 1357 | 1362 |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| Confabulations | — | 11.7% |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal Not comparable
GLM-4.7-Flash: —, o1: 33.6 (#87)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| LMArena Vision | — | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |
Multilingual o1 leads
GLM-4.7-Flash: 46.8 (#152), o1: 48.8 (#137)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| LMArena Non-English | 1331 | 1359 |
| LMArena Chinese | 1403 | 1394 |
| LMArena French | 1332 | 1343 |
| LMArena German | 1336 | 1337 |
| LMArena Korean | 1284 | 1396 |
| LMArena Russian | 1333 | 1356 |
| LMArena Spanish | 1351 | 1345 |
| LMArena Japanese | — | 1346 |
Instruction Following o1 leads
GLM-4.7-Flash: 70.3 (#162), o1: 75.0 (#82)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| LMArena Instruction Following | 1327 | 1368 |
| LiveBench Instruction Following | — | 81.5% |
Long Context o1 leads
GLM-4.7-Flash: 40.9 (#143), o1: 50.3 (#9)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| LMArena Longer Query | 1345 | 1378 |
| Fiction.LiveBench | — | 83.3% |
Writing & Preference o1 leads
GLM-4.7-Flash: 47.7 (#206), o1: 55.8 (#139)
| Benchmark | GLM-4.7-Flash | o1 |
|---|---|---|
| LMArena Text | 1351 | 1366 |
| LMArena Creative Writing | 1297 | 1347 |
| LMArena Multi-Turn | 1342 | 1368 |
| Short-Story Creative Writing | — | 70.2% |
| EQ-Bench Creative Writing | 1125 | — |
| LiveBench Language | — | 65.4% |
Frequently asked questions
Is GLM-4.7-Flash better than o1?
o1 is the stronger model overall, scoring 40.6 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 181× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or o1?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; o1 lists at $15 and $60.
Is GLM-4.7-Flash or o1 better for coding?
o1 scores higher on coding benchmarks: 45.7 versus 40.0 in the Noometry coding category.
Which has the bigger context window?
Both accept 200K tokens.
How many benchmarks do GLM-4.7-Flash and o1 share?
19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and o1 has 52.