Model comparison
GLM-4.5 vs gpt-oss-120b
GLM-4.5 is the stronger model overall, scoring 41.4 to 36.0 on the Noometry Index. gpt-oss-120b costs 14× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. GLM-4.5 scores higher in 6 categories and gpt-oss-120b in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 36.3.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 54.2% for GLM-4.5 and 26% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
Side by side
| GLM-4.5 | gpt-oss-120b | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 36.0 |
| Released | 2025-07-27 | 2025-08-05 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 98K | 41K |
| Input $ / M tokens | $0.60 | $0.037 |
| Output $ / M tokens | $2.20 | $0.17 |
| Results tracked | 27 | 48 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.1 (#121), gpt-oss-120b: 33.2 (#251)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| SWE-bench Verified (bash only) | 54.2% | 26% |
| WeirdML | 40.6% | 48.2% |
| LMArena Coding | 1434 | 1380 |
| ALE-Bench | 344.82 | 575.62 |
| AlgoTune | 1.52 | 1.41 |
| Aider Polyglot | — | 41.8% |
| SciCode | — | 36% |
Agentic & Tool Use Not comparable
GLM-4.5: —, gpt-oss-120b: 12.1 (#145)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning GLM-4.5 leads
GLM-4.5: 27.7 (#99), gpt-oss-120b: 19.5 (#236)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| Kagi LLM Benchmark | 57.9% | 58.6% |
| LMArena Hard Prompts | 1430 | 1364 |
| SimpleBench | — | 22.1% |
| CritPt | — | 1.1% |
| Chess Puzzles | — | 20% |
| Mystery Game Puzzles | — | 2% |
| DTBench | — | 76.3% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
| Epoch Capabilities Index | — | 139.94 |
Math gpt-oss-120b leads
GLM-4.5: 36.3 (#119), gpt-oss-120b: 50.6 (#43)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| LMArena Math | 1427 | 1388 |
| OTIS Mock AIME 2024-2025 | — | 88.9% |
| Omni-MATH | — | 68.8% |
Knowledge gpt-oss-120b leads
GLM-4.5: 35.5 (#172), gpt-oss-120b: 42.3 (#89)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| Confabulations | 11.3% | 15.7% |
| LMArena Expert | 1433 | 1356 |
| GPQA Diamond | — | 75.8% |
| Humanity's Last Exam | 8.3% | — |
| MMLU-Pro | — | 79.5% |
| Vectara Hallucination Rate | — | 14.2% |
| GPQA (HELM) | — | 68.4% |
Multilingual GLM-4.5 leads
GLM-4.5: 53.0 (#74), gpt-oss-120b: 48.2 (#142)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1417 | 1350 |
| LMArena Chinese | 1465 | 1384 |
| LMArena French | 1423 | 1370 |
| LMArena German | 1406 | 1355 |
| LMArena Japanese | 1416 | 1332 |
| LMArena Korean | 1382 | 1283 |
| LMArena Russian | 1414 | 1343 |
| LMArena Spanish | 1454 | 1388 |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.3 (#100), gpt-oss-120b: 69.5 (#168)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1405 | 1318 |
| IFEval | — | 83.6% |
Long Context GLM-4.5 leads
GLM-4.5: 38.2 (#196), gpt-oss-120b: 31.4 (#273)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| Fiction.LiveBench | 58.3% | 44.4% |
| LMArena Longer Query | 1413 | 1319 |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.7 (#121), gpt-oss-120b: 46.7 (#213)
| Benchmark | GLM-4.5 | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1430 | 1366 |
| LMArena Creative Writing | 1395 | 1274 |
| Short-Story Creative Writing | 73.4% | 77.1% |
| EQ-Bench Creative Writing | 1343 | 961 |
| LMArena Multi-Turn | 1415 | 1340 |
| WildBench | — | 84.5% |
Frequently asked questions
Is GLM-4.5 better than gpt-oss-120b?
GLM-4.5 is the stronger model overall, scoring 41.4 to 36.0 on the Noometry Index. gpt-oss-120b costs 14× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5 or gpt-oss-120b?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.
Is GLM-4.5 or gpt-oss-120b better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.1 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do GLM-4.5 and gpt-oss-120b share?
26 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and gpt-oss-120b has 48.