GLM 5.2 vs gpt-oss-120b
GLM 5.2 leads gpt-oss-120b on the Artificial Analysis Intelligence Index, 33.7 to 11.6, and gpt-oss-120b costs 8.8x less per output token ($0.50 vs $4.40 per million). Updated October 21, 2026; 64 benchmarks have a result for both.
GLM 5.2 or gpt-oss-120b: which to pick
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding.
Pick gpt-oss-120b for output tokens 8.8x cheaper ($0.50 vs $4.40 per million) and a lower cost per solved task ($0.51 against $0.86).
- Shared results
- 64
- GLM 5.2 only
- 34
- gpt-oss-120b only
- 17
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 11.6.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.2
GLM 5.2 leads, 38.4 against 3.7.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.2
GLM 5.2 leads, 85.6% against 78.5%.
Same board, both measured
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
GLM 5.2
GLM 5.2 leads, 86.7% against 79.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 968.
Same board, both measured
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
GLM 5.2 $0.0036, gpt-oss-120b $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
gpt-oss-120b
GLM 5.2 $0.86, gpt-oss-120b $0.51.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, gpt-oss-120b not benchmarked.
- Widest category
- Web design, 41.1 points apart on average over 6 shared benchmarks.
- Widest single gap
- Vending-Bench 2: GLM 5.2 $8,314 against gpt-oss-120b -$22, 52.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against gpt-oss-120b $0.50 per million output tokens, 8.8x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, gpt-oss-120b 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 5.2 | Bars | gpt-oss-120b | Ties |
|---|---|---|---|---|
| Coding | 8 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 4 | 0 | ||
| Reasoning | 10 | 0 | ||
| Math | 2 | 1 | ||
| Knowledge | 8 | 0 | ||
| Web design | 6 | 0 | ||
| General | 22 | 0 | ||
| All shared boards | 62 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index81
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 41.8% | Only gpt-oss-120b measured |
| SWE-bench Verified | Not measured | 26.0% | Only gpt-oss-120b measured |
| LMArena WebDev | 1604 | Not measured | Only GLM 5.2 measured |
| AA Coding Index | 68.8 | 30.4 | GLM 5.2 |
| LiveCodeBench (Vals AI) | 69.5% | 83.2% | gpt-oss-120b |
| SWE-bench Verified (Vals AI) | 82.8% | 33.6% | GLM 5.2 |
| Vibe Code Bench (Vals AI) | 64.0% | Not measured | Only GLM 5.2 measured |
| Code migration (Vals AI) | 37.9% | Not measured | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | Not measured | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 87.8% | Only gpt-oss-120b measured |
| SciCode | 51.2% | 34.0% | GLM 5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | 23.5% | GLM 5.2 |
| Terminal-Bench | Not measured | 18.7% | Only gpt-oss-120b measured |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | 48.2% | GLM 5.2 |
| ALE-Bench | 1047.0 | 575.6 | GLM 5.2 |
| LMArena Text: coding | 1487 | 1379 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1387 | GLM 5.2 |
| LMArena WebDev: React | 1617 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: HTML | 1565 | Not measured | Only GLM 5.2 measured |
| SciCode | Not measured | 7.7% | Only gpt-oss-120b measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 11.6 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | 139.8 | GLM 5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | 3.7 | GLM 5.2 |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1160 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1178 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1175 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1195 | Not measured | Only GLM 5.2 measured |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | Not measured | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | 31.3% | Not measured | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | Not measured | Only GLM 5.2 measured |
| τ²-Bench Telecom | 99.1% | 65.8% | GLM 5.2 |
| APEX-Agents | 45.2% | 4.4% | GLM 5.2 |
| METR time horizons | Not measured | 56.6% | Only gpt-oss-120b measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | -$22 | GLM 5.2 |
| BrowseComp-Plus | Not measured | 67.0% | Only gpt-oss-120b measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index100
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 78.5% | GLM 5.2 |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 78.2% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 19.6% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | 75.8% | GLM 5.2 |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | 22.1% | GLM 5.2 |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | 2.0% | GLM 5.2 |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | 1364 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1356 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1368 | GLM 5.2 |
| Kagi LLM Benchmark | 62.6% | 58.6% | GLM 5.2 |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| AIME | Not measured | 92.6% | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only gpt-oss-120b measured |
| MGSM (Vals AI) | Not measured | 92.0% | Only gpt-oss-120b measured |
| AA Math Index | Not measured | 93.4 | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 93.4% | Only gpt-oss-120b measured |
| OTIS Mock AIME | 86.4% | 88.9% | gpt-oss-120b |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| LMArena Text: math | 1483 | 1389 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1386 | GLM 5.2 |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 79.2% | GLM 5.2 |
| LegalBench (Vals AI) | 84.1% | 75.9% | GLM 5.2 |
| CaseLaw (Vals AI) | Not measured | 48.8% | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | 66.1% | 58.2% | GLM 5.2 |
| TaxEval (Vals AI) | 73.3% | 71.6% | GLM 5.2 |
| MedQA (Vals AI) | Not measured | 91.4% | Only gpt-oss-120b measured |
| MedCode (Vals AI) | 40.8% | Not measured | Only GLM 5.2 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.8% | Only gpt-oss-120b measured |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| LMArena Text: science | 1489 | 1382 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1388 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1355 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1359 | GLM 5.2 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 968 | GLM 5.2 |
| Design Arena: UI components | 1296 | 927 | GLM 5.2 |
| Design Arena: game dev | 1268 | 992 | GLM 5.2 |
| Design Arena: data viz | 1285 | 987 | GLM 5.2 |
| Design Arena: 3D | 1288 | 901 | GLM 5.2 |
| Design Arena: all code categories | 1288 | 963 | GLM 5.2 |
| Design Arena: SVG | 1214 | Not measured | Only GLM 5.2 measured |
| Design Arena: ASCII art | 1190 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index220
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 83.5% | Not measured | Only GLM 5.2 measured |
| IFBench | 73.3% | 69.0% | GLM 5.2 |
| Long context reasoning (AA-LCR) | 78.3% | 52.0% | GLM 5.2 |
| DTBench | 93.6% | 76.3% | GLM 5.2 |
| LMCA | 45.8% | 22.1% | GLM 5.2 |
| Fiction.LiveBench | Not measured | 44.4% | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | Not measured | 77.1% | Only gpt-oss-120b measured |
| Surface Evolver Bench | 55.6% | 25.0% | GLM 5.2 |
| LMArena Text | 1470 | 1366 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1318 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1274 | GLM 5.2 |
| LMArena Text: English | 1475 | 1375 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1351 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1385 | GLM 5.2 |
| LMArena Text: German | 1470 | 1353 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | 1331 | GLM 5.2 |
| LMArena Text: Korean | 1440 | 1282 | GLM 5.2 |
| LMArena Text: French | 1479 | 1370 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1389 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1343 | GLM 5.2 |
| LMArena Text: Polish | 1456 | 1353 | GLM 5.2 |
| LMArena Text: multi-turn | 1469 | 1340 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1319 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1306 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1292 | GLM 5.2 |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | gpt-oss-120b | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
gpt-oss-120b costs less
Repository review
50K input + 3K output tokens
gpt-oss-120b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-120b costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 5.2 | OpenAI gpt-oss-120b |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | Open weights |
| Released | June 16, 2026 | August 5, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $1.40 | $0.10 |
| Output $/Mtok | $4.40 | $0.50 |
| Coding index | 61.1 | Not benchmarked |
| Independent measurements behind it | 4 | 1 |
| Cost per solved task | $0.86 | $0.51 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than gpt-oss-120b for coding?
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 8.8x cheaper ($0.50 vs $4.40 per million) and a lower cost per solved task ($0.51 against $0.86).
Which is cheaper, GLM 5.2 or gpt-oss-120b?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and gpt-oss-120b $0.51.
Which has the bigger context window, GLM 5.2 or gpt-oss-120b?
GLM 5.2, with 1.05M tokens against 131K.
How many benchmarks is the GLM 5.2 vs gpt-oss-120b comparison based on?
64 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.