GLM 4.7 Flash vs GLM 5.2
GLM 5.2 leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 33.7 to 14.9, and GLM 4.7 Flash costs 11.0x less per output token ($0.40 vs $4.40 per million). Updated October 21, 2026; 43 benchmarks have a result for both.
GLM 4.7 Flash or GLM 5.2: which to pick
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 11.0x cheaper ($0.40 vs $4.40 per million).
- Shared results
- 43
- GLM 4.7 Flash only
- 0
- GLM 5.2 only
- 55
- 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 14.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1194.
Same board, both measured
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, GLM 5.2 $0.0036 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, GLM 5.2 61.1 from 4 benchmarks.
- Widest category
- Reasoning, 26.2 points apart on average over 6 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against GLM 5.2 78.3%, 36.7 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against GLM 5.2 $4.40 per million output tokens, 11.0x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, GLM 5.2 1.05M.
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 Flash | Bars | GLM 5.2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 43 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1604 | Only GLM 5.2 measured |
| AA Coding Index | Not measured | 68.8 | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | Not measured | 69.5% | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | Not measured | 82.8% | Only GLM 5.2 measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.0% | Only GLM 5.2 measured |
| Code migration (Vals AI) | Not measured | 37.9% | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.8% | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | Not measured | 45.1% | Only GLM 5.2 measured |
| SciCode | Not measured | 51.2% | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 50.8% | GLM 5.2 |
| FrontierCode | Not measured | 24.5% | Only GLM 5.2 measured |
| WeirdML | Not measured | 70.1% | Only GLM 5.2 measured |
| ALE-Bench | Not measured | 1047.0 | Only GLM 5.2 measured |
| LMArena Text: coding | 1382 | 1487 | GLM 5.2 |
| LMArena Text: software and IT | 1389 | 1486 | GLM 5.2 |
| LMArena WebDev: React | Not measured | 1617 | Only GLM 5.2 measured |
| LMArena WebDev: HTML | Not measured | 1565 | Only GLM 5.2 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 33.7 | GLM 5.2 |
| Epoch Capabilities Index | Not measured | 151.9 | Only GLM 5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 38.4 | Only GLM 5.2 measured |
| Design Arena: agents building web apps | Not measured | 1222 | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | Not measured | 1241 | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | Not measured | 1160 | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1178 | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GLM 5.2 measured |
| Design Arena: agents building games | Not measured | 1206 | Only GLM 5.2 measured |
| Design Arena: HTML slides | Not measured | 1175 | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | Not measured | 1195 | Only GLM 5.2 measured |
| SREBench (Vals AI) | Not measured | 0.0% | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | Not measured | 49.7% | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.3% | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 61.5% | Only GLM 5.2 measured |
| τ²-Bench Telecom | 98.8% | 99.1% | GLM 5.2 |
| APEX-Agents | Not measured | 45.2% | Only GLM 5.2 measured |
| PostTrainBench | Not measured | 31.7% | Only GLM 5.2 measured |
| Vending-Bench 2 | Not measured | $8,314 | Only GLM 5.2 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 85.6% | Only GLM 5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 58.1% | 89.5% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 41.1% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 60.5% | 91.9% | GLM 5.2 |
| ARC-AGI | Not measured | 77.0% | Only GLM 5.2 measured |
| ARC-AGI-2 | Not measured | 22.8% | Only GLM 5.2 measured |
| SimpleBench | Not measured | 58.8% | Only GLM 5.2 measured |
| Chess puzzles | Not measured | 22.0% | Only GLM 5.2 measured |
| Mystery game puzzles | Not measured | 19.0% | Only GLM 5.2 measured |
| EBR-bench | Not measured | 9.5% | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1356 | 1481 | GLM 5.2 |
| LMArena Text: expert | 1357 | 1487 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1369 | 1481 | GLM 5.2 |
| Kagi LLM Benchmark | Not measured | 62.6% | Only GLM 5.2 measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | 86.4% | GLM 5.2 |
| FrontierMath, tiers 1 to 3 | Not measured | 59.2% | Only GLM 5.2 measured |
| FrontierMath, tier 4 | Not measured | 29.3% | Only GLM 5.2 measured |
| ProofBench | Not measured | 35.0% | Only GLM 5.2 measured |
| LMArena Text: math | 1355 | 1483 | GLM 5.2 |
| LMArena Text: mathematical work | 1369 | 1489 | GLM 5.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GLM 5.2 measured |
| LegalBench (Vals AI) | Not measured | 84.1% | Only GLM 5.2 measured |
| CorpFin (Vals AI) | Not measured | 66.1% | Only GLM 5.2 measured |
| TaxEval (Vals AI) | Not measured | 73.3% | Only GLM 5.2 measured |
| MedCode (Vals AI) | Not measured | 40.8% | Only GLM 5.2 measured |
| SimpleQA Verified | Not measured | 34.2% | Only GLM 5.2 measured |
| LMArena Text: science | 1358 | 1489 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1343 | 1475 | GLM 5.2 |
| LMArena Text: legal and government | 1357 | 1486 | GLM 5.2 |
| LMArena Text: business and finance | 1343 | 1451 | GLM 5.2 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | 1285 | GLM 5.2 |
| Design Arena: UI components | 1208 | 1296 | GLM 5.2 |
| Design Arena: game dev | 1135 | 1268 | GLM 5.2 |
| Design Arena: data viz | 1123 | 1285 | GLM 5.2 |
| Design Arena: 3D | 1121 | 1288 | GLM 5.2 |
| Design Arena: all code categories | 1178 | 1288 | GLM 5.2 |
| Design Arena: SVG | 1038 | 1214 | GLM 5.2 |
| Design Arena: ASCII art | Not measured | 1190 | 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 index018
| Benchmark | GLM 4.7 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.5% | Only GLM 5.2 measured |
| IFBench | 60.8% | 73.3% | GLM 5.2 |
| Long context reasoning (AA-LCR) | 41.7% | 78.3% | GLM 5.2 |
| DTBench | Not measured | 93.6% | Only GLM 5.2 measured |
| LMCA | Not measured | 45.8% | Only GLM 5.2 measured |
| Surface Evolver Bench | Not measured | 55.6% | Only GLM 5.2 measured |
| LMArena Text | 1351 | 1470 | GLM 5.2 |
| LMArena Text: instruction following | 1327 | 1466 | GLM 5.2 |
| LMArena Text: creative writing | 1296 | 1463 | GLM 5.2 |
| LMArena Text: English | 1373 | 1475 | GLM 5.2 |
| LMArena Text: non-English | 1330 | 1459 | GLM 5.2 |
| LMArena Text: Chinese | 1403 | 1522 | GLM 5.2 |
| LMArena Text: German | 1336 | 1470 | GLM 5.2 |
| LMArena Text: Japanese | Not measured | 1450 | Only GLM 5.2 measured |
| LMArena Text: Korean | 1283 | 1440 | GLM 5.2 |
| LMArena Text: French | 1332 | 1479 | GLM 5.2 |
| LMArena Text: Spanish | 1350 | 1478 | GLM 5.2 |
| LMArena Text: Russian | 1333 | 1468 | GLM 5.2 |
| LMArena Text: Polish | 1291 | 1456 | GLM 5.2 |
| LMArena Text: multi-turn | 1342 | 1469 | GLM 5.2 |
| LMArena Text: longer queries | 1345 | 1480 | GLM 5.2 |
| LMArena Text: writing and language | 1320 | 1463 | GLM 5.2 |
| LMArena Text: entertainment and media | 1304 | 1450 | GLM 5.2 |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | GLM 5.2 | 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 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 Flash 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 Flash | Z.ai GLM 5.2 |
|---|---|---|
| Maker | Z.ai | Z.ai |
| Weights | Open weights | Open weights |
| Released | January 19, 2026 | June 16, 2026 |
| Context window | 200K | 1.05M |
| Input $/Mtok | $0.0653 | $1.40 |
| Output $/Mtok | $0.40 | $4.40 |
| Coding index | Not benchmarked | 61.1 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | $0.86 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 Flash better than GLM 5.2 for coding?
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 11.0x cheaper ($0.40 vs $4.40 per million).
Which is cheaper, GLM 4.7 Flash or GLM 5.2?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, GLM 4.7 Flash or GLM 5.2?
GLM 5.2, with 1.05M tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs GLM 5.2 comparison based on?
43 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.