Model comparison
GLM-4.6V vs gpt-oss-120b
GLM-4.6V is the stronger model overall, scoring 40.9 to 36.0 on the Noometry Index. gpt-oss-120b costs 6.4× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 6 categories and gpt-oss-120b in 1 category; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.8 to 46.7.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
- gpt-oss-120b accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.6V | gpt-oss-120b | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 40.9 | 36.0 |
| Released | 2025-12-08 | 2025-08-05 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 33K | 41K |
| Input $ / M tokens | $0.30 | $0.037 |
| Output $ / M tokens | $0.90 | $0.17 |
| Results tracked | 12 | 48 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.2 (#128), gpt-oss-120b: 33.2 (#251)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Coding | 1390 | 1380 |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| SciCode | — | 36% |
| WeirdML | — | 48.2% |
| ALE-Bench | — | 575.62 |
| AlgoTune | — | 1.41 |
Agentic & Tool Use Not comparable
GLM-4.6V: —, gpt-oss-120b: 12.1 (#145)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning GLM-4.6V leads
GLM-4.6V: 26.5 (#117), gpt-oss-120b: 19.5 (#236)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1364 |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| 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 Not comparable
GLM-4.6V: —, gpt-oss-120b: 50.6 (#43)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 88.9% |
| Omni-MATH | — | 68.8% |
| LMArena Math | — | 1388 |
Knowledge gpt-oss-120b leads
GLM-4.6V: 37.4 (#148), gpt-oss-120b: 42.3 (#89)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Expert | 1372 | 1356 |
| GPQA Diamond | — | 75.8% |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| Vectara Hallucination Rate | — | 14.2% |
| GPQA (HELM) | — | 68.4% |
Multimodal Not comparable
GLM-4.6V: 34.2 (#83), gpt-oss-120b: —
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Too close to call
GLM-4.6V: 48.8 (#135), gpt-oss-120b: 48.2 (#142)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1359 | 1350 |
| LMArena Chinese | 1426 | 1384 |
| LMArena Russian | 1341 | 1343 |
| LMArena French | — | 1370 |
| LMArena German | — | 1355 |
| LMArena Japanese | — | 1332 |
| LMArena Korean | — | 1283 |
| LMArena Spanish | — | 1388 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.6 (#145), gpt-oss-120b: 69.5 (#168)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1351 | 1318 |
| IFEval | — | 83.6% |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#138), gpt-oss-120b: 31.4 (#273)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1358 | 1319 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.8 (#132), gpt-oss-120b: 46.7 (#213)
| Benchmark | GLM-4.6V | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1377 | 1366 |
| LMArena Creative Writing | 1347 | 1274 |
| LMArena Multi-Turn | 1359 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| EQ-Bench Creative Writing | — | 961 |
| WildBench | — | 84.5% |
Frequently asked questions
Is GLM-4.6V better than gpt-oss-120b?
GLM-4.6V is the stronger model overall, scoring 40.9 to 36.0 on the Noometry Index. gpt-oss-120b costs 6.4× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6V 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-4.6V lists at $0.30 and $0.90.
Is GLM-4.6V or gpt-oss-120b better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.2 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
gpt-oss-120b does, with 131K tokens against 128K.
How many benchmarks do GLM-4.6V and gpt-oss-120b share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and gpt-oss-120b has 48.