GLM 4.7 vs gpt-oss-20b
GLM 4.7 leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 22.2 to 10.0, and gpt-oss-20b costs 14.7x less per output token ($0.15 vs $2.20 per million). Updated October 21, 2026; 55 benchmarks have a result for both.
GLM 4.7 or gpt-oss-20b: which to pick
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; gpt-oss-20b has none, so the two cannot be ranked on coding.
Pick gpt-oss-20b for output tokens 14.7x cheaper ($0.15 vs $2.20 per million) and work measured by ALE-Bench or MGSM (Vals AI), where it leads.
- Shared results
- 55
- GLM 4.7 only
- 25
- gpt-oss-20b only
- 5
- 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 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7
GLM 4.7 leads, 22.2 against 10.0.
Same board, both measured
Reasoning
GPQA Diamond
GLM 4.7
GLM 4.7 leads, 80.0% against 68.9%.
Same board, both measured
Math
AIME
GLM 4.7
GLM 4.7 leads, 93.3% against 86.0%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.7
GLM 4.7 leads, 82.7% against 71.6%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 against 852.
Same board, both measured
Long documents
Listed context window
GLM 4.7
GLM 4.7 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
GLM 4.7 $0.0017, gpt-oss-20b $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, gpt-oss-20b not benchmarked.
- Widest category
- Web design, 39.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- Design Arena: websites: GLM 4.7 1226 against gpt-oss-20b 852, 46.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against gpt-oss-20b $0.15 per million output tokens, 14.7x apart.
- Context window
- GLM 4.7 takes 205K tokens, gpt-oss-20b 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 4.7 | Bars | gpt-oss-20b | Ties |
|---|---|---|---|---|
| Coding | 6 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 6 | 1 | ||
| Knowledge | 10 | 1 | ||
| Web design | 2 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 52 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index61
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | Not measured | Only GLM 4.7 measured |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | 80.4% | GLM 4.7 |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | 77.7% | GLM 4.7 |
| SciCode | Not measured | 38.9% | Only gpt-oss-20b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 10.6% | GLM 4.7 |
| Terminal-Bench | 33.4% | 3.4% | GLM 4.7 |
| WeirdML | Not measured | 40.9% | Only gpt-oss-20b measured |
| ALE-Bench | 399.5 | 566.0 | gpt-oss-20b |
| LMArena Text: coding | 1454 | 1307 | GLM 4.7 |
| LMArena Text: software and IT | 1453 | 1311 | GLM 4.7 |
| LMArena WebDev: React | 1420 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: HTML | 1447 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 10.0 | GLM 4.7 |
| Epoch Capabilities Index | 143.6 | 137.7 | GLM 4.7 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1112 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| τ²-Bench Telecom | 95.9% | 60.2% | GLM 4.7 |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 68.9% | GLM 4.7 |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 68.8% | GLM 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 11.0% | GLM 4.7 |
| GPQA Diamond (Epoch AI run) | 83.3% | 60.8% | GLM 4.7 |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | 1274 | GLM 4.7 |
| LMArena Text: expert | 1424 | 1258 | GLM 4.7 |
| LMArena Text: hard prompts in English | 1452 | 1292 | GLM 4.7 |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| AIME | 93.3% | 86.0% | GLM 4.7 |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only gpt-oss-20b measured |
| MGSM (Vals AI) | 88.2% | 89.0% | gpt-oss-20b |
| AA Math Index | 95.0 | 89.3 | GLM 4.7 |
| AIME 2025 (Artificial Analysis run) | 95.0% | 89.3% | GLM 4.7 |
| OTIS Mock AIME | 83.3% | 65.3% | GLM 4.7 |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | 1317 | GLM 4.7 |
| LMArena Text: mathematical work | 1411 | 1305 | GLM 4.7 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index101
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 71.6% | GLM 4.7 |
| LegalBench (Vals AI) | 83.4% | 70.8% | GLM 4.7 |
| CaseLaw (Vals AI) | 54.9% | 43.8% | GLM 4.7 |
| CorpFin (Vals AI) | 46.4% | 53.1% | gpt-oss-20b |
| TaxEval (Vals AI) | 68.8% | 63.7% | GLM 4.7 |
| MedQA (Vals AI) | 93.7% | 82.9% | GLM 4.7 |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | 74.8% | GLM 4.7 |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | 1296 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1310 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1280 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1284 | GLM 4.7 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 852 | GLM 4.7 |
| Design Arena: UI components | 1199 | Not measured | Only GLM 4.7 measured |
| Design Arena: game dev | 1189 | Not measured | Only GLM 4.7 measured |
| Design Arena: data viz | 1195 | 933 | GLM 4.7 |
| Design Arena: 3D | 1191 | Not measured | Only GLM 4.7 measured |
| Design Arena: all code categories | 1217 | Not measured | Only GLM 4.7 measured |
| Design Arena: SVG | 1143 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | GLM 4.7 | gpt-oss-20b | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | 65.1% | GLM 4.7 |
| Long context reasoning (AA-LCR) | 71.0% | 34.7% | GLM 4.7 |
| DTBench | Not measured | 68.0% | Only gpt-oss-20b measured |
| LMCA | Not measured | 14.5% | Only gpt-oss-20b measured |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1288 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1236 | GLM 4.7 |
| LMArena Text: creative writing | 1401 | 1201 | GLM 4.7 |
| LMArena Text: English | 1453 | 1303 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1269 | GLM 4.7 |
| LMArena Text: Chinese | 1495 | 1315 | GLM 4.7 |
| LMArena Text: German | 1426 | 1255 | GLM 4.7 |
| LMArena Text: Japanese | 1439 | 1244 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1236 | GLM 4.7 |
| LMArena Text: French | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Spanish | 1434 | 1267 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1278 | GLM 4.7 |
| LMArena Text: Polish | 1435 | 1252 | GLM 4.7 |
| LMArena Text: multi-turn | 1446 | 1268 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1250 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1238 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1216 | GLM 4.7 |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | gpt-oss-20b | 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-20b costs less
Repository review
50K input + 3K output tokens
gpt-oss-20b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-20b 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 4.7 | OpenAI gpt-oss-20b |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | August 5, 2025 |
| Context window | 205K | 131K |
| Input $/Mtok | $0.60 | $0.03 |
| Output $/Mtok | $2.20 | $0.15 |
| Coding index | 55.1 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than gpt-oss-20b for coding?
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; gpt-oss-20b has none, so the two cannot be ranked on coding. Pick gpt-oss-20b for output tokens 14.7x cheaper ($0.15 vs $2.20 per million) and work measured by ALE-Bench or MGSM (Vals AI), where it leads.
Which is cheaper, GLM 4.7 or gpt-oss-20b?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; gpt-oss-20b costs $0.03 and $0.15. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or gpt-oss-20b?
GLM 4.7, with 205K tokens against 131K.
How many benchmarks is the GLM 4.7 vs gpt-oss-20b comparison based on?
55 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and gpt-oss-20b's on 0. Where only one model has a result, the page says so instead of naming a winner.