Model comparison
GLM-4.7-Flash vs gpt-oss-120b
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 36.3 on the Noometry Index. gpt-oss-120b costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-4.7-Flash scores higher in 5 categories and gpt-oss-120b in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 36.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 88.9% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.06 / $0.40 for GLM-4.7-Flash.
- GLM-4.7-Flash accepts more context: 200K tokens versus 131K.
Side by side
| GLM-4.7-Flash | gpt-oss-120b | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.8 | 36.3 |
| Released | 2026-01-19 | 2025-08-05 |
| Weights | Open | Open |
| Context window | 200K | 131K |
| Max output | 131K | 41K |
| Input $ / M tokens | $0.06 | $0.037 |
| Output $ / M tokens | $0.40 | $0.17 |
| Results tracked | 21 | 48 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), gpt-oss-120b: 33.5 (#256)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Coding | 1383 | 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-4.7-Flash: —, gpt-oss-120b: 12.2 (#153)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning Too close to call
GLM-4.7-Flash: 20.9 (#229), gpt-oss-120b: 20.0 (#245)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| Chess Puzzles | 0% | 20% |
| LMArena Hard Prompts | 1356 | 1364 |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| CritPt | — | 1.1% |
| Mystery Game Puzzles | — | 2% |
| DTBench | — | 76.3% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
| Epoch Capabilities Index | — | 139.93 |
Math gpt-oss-120b leads
GLM-4.7-Flash: 36.1 (#173), gpt-oss-120b: 52.5 (#50)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 88.9% |
| LMArena Math | 1355 | 1389 |
| Omni-MATH | — | 68.8% |
Knowledge gpt-oss-120b leads
GLM-4.7-Flash: 35.5 (#184), gpt-oss-120b: 42.4 (#96)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 60.5% | 75.8% |
| Vectara Hallucination Rate | 9.3% | 14.2% |
| LMArena Expert | 1357 | 1356 |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| GPQA (HELM) | — | 68.4% |
Multilingual gpt-oss-120b leads
GLM-4.7-Flash: 46.5 (#158), gpt-oss-120b: 48.0 (#147)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1330 | 1351 |
| LMArena Chinese | 1403 | 1385 |
| LMArena French | 1332 | 1369 |
| LMArena German | 1337 | 1353 |
| LMArena Korean | 1283 | 1282 |
| LMArena Russian | 1332 | 1343 |
| LMArena Spanish | 1350 | 1389 |
| LMArena Japanese | — | 1331 |
Instruction Following Too close to call
GLM-4.7-Flash: 70.1 (#167), gpt-oss-120b: 69.3 (#173)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1327 | 1318 |
| IFEval | — | 83.6% |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#148), gpt-oss-120b: 31.4 (#278)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1345 | 1319 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference Too close to call
GLM-4.7-Flash: 47.4 (#210), gpt-oss-120b: 46.5 (#217)
| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1351 | 1365 |
| LMArena Creative Writing | 1297 | 1275 |
| EQ-Bench Creative Writing | 1125 | 961 |
| LMArena Multi-Turn | 1342 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| WildBench | — | 84.5% |
Frequently asked questions
Is GLM-4.7-Flash better than gpt-oss-120b?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 36.3 on the Noometry Index. gpt-oss-120b costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash 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-4.7-Flash lists at $0.06 and $0.40.
Is GLM-4.7-Flash or gpt-oss-120b better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7-Flash does, with 200K tokens against 131K.
How many benchmarks do GLM-4.7-Flash and gpt-oss-120b share?
21 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and gpt-oss-120b has 48.