GLM 4.7 vs GLM 5.1
Updated October 21, 2026. 71 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.1 for coding: it scores 44.0 on the Vibe Coding Index against 33.6 for GLM 4.7.
Pick GLM 4.7 when output tokens cost 2.2x less, or when a solved task costs $0.29 against $0.60.
- Shared results
- 71
- GLM 4.7 only
- 9
- GLM 5.1 only
- 18
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GLM 5.1
GLM 5.1 scores 44.0 against 33.6.
One benchmark behind each score
Intelligence
AA Intelligence Index
GLM 5.1
GLM 5.1 leads, 26.1 against 22.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.1 has a AA Agentic Index result, 23.9.
Directional only
Reasoning
GPQA Diamond
GLM 5.1
GLM 5.1 leads, 84.5% against 80.0%.
Same board, both measured
Math
AIME
GLM 4.7
GLM 4.7 leads, 93.3% against 91.9%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.1
GLM 5.1 leads, 86.9% against 82.7%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.1
GLM 5.1 leads, 1290 against 1226.
Same board, both measured
Long documents
Listed context window
No call
Both take 205K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0015, GLM 5.1 $0.0036 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
GLM 4.7 $0.29, GLM 5.1 $0.60.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 4.7 33.6 from 1 benchmark, GLM 5.1 44.0 from 1 benchmark.
- Widest category
- Web design, 10.9 points apart on average over 8 shared benchmarks.
- Widest single gap
- Design Arena: data viz: GLM 4.7 1195 against GLM 5.1 1366, 21.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $1.98 against GLM 5.1 $4.40 per million output tokens, 2.2x apart.
- Context window
- Both take 205K tokens.
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 | GLM 5.1 | Ties |
|---|---|---|---|---|
| Coding | 1 | 9 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 5 | ||
| Reasoning | 1 | 8 | ||
| Math | 1 | 4 | ||
| Knowledge | 1 | 10 | ||
| Web design | 1 | 7 | ||
| General | 1 | 19 | ||
| All shared boards | 7 | 64 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index19
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | 1508 | GLM 5.1 |
| AA Coding Index | 45.3 | 55.8 | GLM 5.1 |
| LiveCodeBench (Vals AI) | 82.2% | 81.4% | GLM 4.7 |
| SWE-bench Verified (Vals AI) | 69.4% | 76.4% | GLM 5.1 |
| Vibe Code Bench (Vals AI) | Not measured | 31.5% | Only GLM 5.1 measured |
| Code migration (Vals AI) | Not measured | 25.8% | Only GLM 5.1 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only GLM 5.1 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 56.9% | Only GLM 5.1 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 44.8% | Only GLM 5.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 43.2% | GLM 5.1 |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| WeirdML | Not measured | 57.1% | Only GLM 5.1 measured |
| ALE-Bench | 399.5 | 887.1 | GLM 5.1 |
| LMArena Text: coding | 1454 | 1485 | GLM 5.1 |
| LMArena Text: software and IT | 1453 | 1484 | GLM 5.1 |
| LMArena WebDev: React | 1420 | 1505 | GLM 5.1 |
| LMArena WebDev: HTML | 1447 | 1508 | GLM 5.1 |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 26.1 | GLM 5.1 |
| Epoch Capabilities Index | 143.6 | 149.8 | GLM 5.1 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 23.9 | Only GLM 5.1 measured |
| Design Arena: agents building web apps | Not measured | 1175 | Only GLM 5.1 measured |
| Design Arena: agents building full-stack apps | 1050 | 1167 | GLM 5.1 |
| Design Arena: agents building mobile apps | 1105 | 1162 | GLM 5.1 |
| Design Arena: agents building Android apps | 1118 | 1186 | GLM 5.1 |
| Design Arena: agents building Godot games | 1058 | 1053 | GLM 4.7 |
| Design Arena: agents building games | Not measured | 1164 | Only GLM 5.1 measured |
| Design Arena: HTML slides | Not measured | 1170 | Only GLM 5.1 measured |
| Design Arena: PowerPoint slides | Not measured | 1189 | Only GLM 5.1 measured |
| Design Arena: spreadsheets | Not measured | 1315 | Only GLM 5.1 measured |
| Finance Agent (Vals AI) | Not measured | 44.8% | Only GLM 5.1 measured |
| Legal Research Bench (Vals AI) | Not measured | 27.9% | Only GLM 5.1 measured |
| τ²-Bench Telecom | 95.9% | 97.7% | GLM 5.1 |
| APEX-Agents | Not measured | 40.9% | Only GLM 5.1 measured |
| Vending-Bench 2 | $2,377 | $5,634 | GLM 5.1 |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index18
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 84.5% | GLM 5.1 |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 86.8% | GLM 5.1 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 30.1% | GLM 5.1 |
| GPQA Diamond (Epoch AI run) | 83.3% | 89.9% | GLM 5.1 |
| SimpleBench | 47.7% | 55.1% | GLM 5.1 |
| Chess puzzles | 21.0% | 20.0% | GLM 4.7 |
| LMArena Text: hard prompts | 1443 | 1473 | GLM 5.1 |
| LMArena Text: expert | 1424 | 1478 | GLM 5.1 |
| LMArena Text: hard prompts in English | 1452 | 1482 | GLM 5.1 |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| AIME | 93.3% | 91.9% | GLM 4.7 |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | 93.3% | GLM 5.1 |
| FrontierMath, tiers 1 to 3 | Not measured | 36.8% | Only GLM 5.1 measured |
| ProofBench | 6.0% | 22.2% | GLM 5.1 |
| LMArena Text: math | 1424 | 1473 | GLM 5.1 |
| LMArena Text: mathematical work | 1411 | 1470 | GLM 5.1 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 86.9% | GLM 5.1 |
| LegalBench (Vals AI) | 83.4% | 84.4% | GLM 5.1 |
| CaseLaw (Vals AI) | 54.9% | 51.5% | GLM 4.7 |
| CorpFin (Vals AI) | 46.4% | 64.5% | GLM 5.1 |
| TaxEval (Vals AI) | 68.8% | 71.2% | GLM 5.1 |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | 41.6% | GLM 5.1 |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 34.0% | GLM 5.1 |
| LMArena Text: science | 1457 | 1478 | GLM 5.1 |
| LMArena Text: medicine and healthcare | 1469 | 1474 | GLM 5.1 |
| LMArena Text: legal and government | 1444 | 1465 | GLM 5.1 |
| LMArena Text: business and finance | 1429 | 1452 | GLM 5.1 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 1290 | GLM 5.1 |
| Design Arena: UI components | 1199 | 1292 | GLM 5.1 |
| Design Arena: game dev | 1189 | 1265 | GLM 5.1 |
| Design Arena: data viz | 1195 | 1366 | GLM 5.1 |
| Design Arena: 3D | 1191 | 1336 | GLM 5.1 |
| Design Arena: all code categories | 1217 | 1268 | GLM 5.1 |
| Design Arena: SVG | 1142 | 1217 | GLM 5.1 |
| Design Arena: ASCII art | 1165 | 1142 | GLM 4.7 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index119
| Benchmark | GLM 4.7 | GLM 5.1 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 61.8% | Only GLM 5.1 measured |
| MedScribe (Vals AI) | 68.6% | 72.3% | GLM 5.1 |
| IFBench | 67.9% | 76.3% | GLM 5.1 |
| Long context reasoning (AA-LCR) | 71.0% | 73.7% | GLM 5.1 |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| ExploitBench | Not measured | 18.1% | Only GLM 5.1 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1463 | GLM 5.1 |
| LMArena Text: instruction following | 1412 | 1453 | GLM 5.1 |
| LMArena Text: creative writing | 1401 | 1453 | GLM 5.1 |
| LMArena Text: English | 1453 | 1476 | GLM 5.1 |
| LMArena Text: non-English | 1417 | 1448 | GLM 5.1 |
| LMArena Text: Chinese | 1495 | 1515 | GLM 5.1 |
| LMArena Text: German | 1426 | 1467 | GLM 5.1 |
| LMArena Text: Japanese | 1439 | 1435 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1424 | GLM 5.1 |
| LMArena Text: French | 1432 | 1470 | GLM 5.1 |
| LMArena Text: Spanish | 1434 | 1468 | GLM 5.1 |
| LMArena Text: Russian | 1423 | 1454 | GLM 5.1 |
| LMArena Text: Polish | 1435 | 1449 | GLM 5.1 |
| LMArena Text: multi-turn | 1446 | 1473 | GLM 5.1 |
| LMArena Text: longer queries | 1432 | 1467 | GLM 5.1 |
| LMArena Text: writing and language | 1411 | 1454 | GLM 5.1 |
| LMArena Text: entertainment and media | 1400 | 1442 | GLM 5.1 |
| 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 | GLM 5.1 | 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
GLM 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 | Z.ai GLM 5.1 |
|---|---|---|
| Maker | Z.ai | Z.ai |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | April 7, 2026 |
| Context window | 205K | 205K |
| Input $/Mtok | $0.54 | $1.40 |
| Output $/Mtok | $1.98 | $4.40 |
| Coding index | 33.6 | 44.0 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.29 | $0.60 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than GLM 5.1 for coding?
Pick GLM 5.1 for coding: it scores 44.0 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 2.2x less, or when a solved task costs $0.29 against $0.60.
Which is cheaper, GLM 4.7 or GLM 5.1?
GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; GLM 5.1 costs $1.40 and $4.40. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and GLM 5.1 $0.60.
Which has the bigger context window, GLM 4.7 or GLM 5.1?
Neither: both take 205K tokens.
How many benchmarks is the GLM 4.7 vs GLM 5.1 comparison based on?
71 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and GLM 5.1's on 1. Where only one model has a result, the page says so instead of naming a winner.