GLM 4.7 Flash vs Qwen3 30B A3B
GLM 4.7 Flash leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 14.9 to 7.6, and GLM 4.7 Flash costs 1.3x less per output token ($0.40 vs $0.50 per million). Updated October 21, 2026; 41 benchmarks have a result for both.
GLM 4.7 Flash or Qwen3 30B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and a larger context window (200K against 41K tokens).
- Shared results
- 41
- GLM 4.7 Flash only
- 2
- Qwen3 30B A3B only
- 11
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7 Flash
GLM 4.7 Flash leads, 14.9 against 7.6.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7 Flash
GLM 4.7 Flash leads, 1194 against 955.
Same board, both measured
Long documents
Listed context window
GLM 4.7 Flash
GLM 4.7 Flash takes 200K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, Qwen3 30B A3B $0.0004 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, Qwen3 30B A3B not benchmarked.
- Widest category
- Agentic work, 72.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.7 Flash 98.8% against Qwen3 30B A3B 26.0%, 72.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against Qwen3 30B A3B $0.50 per million output tokens, 1.3x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, Qwen3 30B A3B 41K.
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 | Qwen3 30B A3B | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 4 | 2 | ||
| Math | 2 | 1 | ||
| Knowledge | 4 | 0 | ||
| Web design | 5 | 0 | ||
| General | 16 | 2 | ||
| All shared boards | 36 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 50.6% | Only Qwen3 30B A3B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 6.8% | GLM 4.7 Flash |
| WeirdML | Not measured | 29.8% | Only Qwen3 30B A3B measured |
| LMArena Text: coding | 1382 | 1337 | GLM 4.7 Flash |
| LMArena Text: software and IT | 1389 | 1340 | GLM 4.7 Flash |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 7.6 | GLM 4.7 Flash |
| Epoch Capabilities Index | Not measured | 137.3 | Only Qwen3 30B A3B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | 26.0% | GLM 4.7 Flash |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | 61.6% | Qwen3 30B A3B |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 6.2% | GLM 4.7 Flash |
| GPQA Diamond (Epoch AI run) | 60.5% | 61.7% | Qwen3 30B A3B |
| LMArena Text: hard prompts | 1356 | 1314 | GLM 4.7 Flash |
| LMArena Text: expert | 1357 | 1314 | GLM 4.7 Flash |
| LMArena Text: hard prompts in English | 1369 | 1329 | GLM 4.7 Flash |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 72.3 | Only Qwen3 30B A3B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 72.3% | Only Qwen3 30B A3B measured |
| OTIS Mock AIME | 58.3% | 62.8% | Qwen3 30B A3B |
| LMArena Text: math | 1355 | 1355 | GLM 4.7 Flash |
| LMArena Text: mathematical work | 1369 | 1363 | GLM 4.7 Flash |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 77.7% | Only Qwen3 30B A3B measured |
| LMArena Text: science | 1358 | 1321 | GLM 4.7 Flash |
| LMArena Text: medicine and healthcare | 1343 | 1318 | GLM 4.7 Flash |
| LMArena Text: legal and government | 1357 | 1314 | GLM 4.7 Flash |
| LMArena Text: business and finance | 1343 | 1309 | GLM 4.7 Flash |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | 955 | GLM 4.7 Flash |
| Design Arena: UI components | 1208 | 946 | GLM 4.7 Flash |
| Design Arena: game dev | 1135 | 899 | GLM 4.7 Flash |
| Design Arena: data viz | 1123 | 967 | GLM 4.7 Flash |
| Design Arena: 3D | 1121 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | 1178 | 942 | GLM 4.7 Flash |
| Design Arena: SVG | 1038 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index162
| Benchmark | GLM 4.7 Flash | Qwen3 30B A3B | Leader |
|---|---|---|---|
| IFBench | 60.8% | 41.5% | GLM 4.7 Flash |
| Long context reasoning (AA-LCR) | 41.7% | 0.0% | GLM 4.7 Flash |
| DTBench | Not measured | 60.3% | Only Qwen3 30B A3B measured |
| LMCA | Not measured | 15.8% | Only Qwen3 30B A3B measured |
| Fiction.LiveBench | Not measured | 40.6% | Only Qwen3 30B A3B measured |
| Creative writing (Lech Mazur) | Not measured | 75.3% | Only Qwen3 30B A3B measured |
| LMArena Text | 1351 | 1317 | GLM 4.7 Flash |
| LMArena Text: instruction following | 1327 | 1283 | GLM 4.7 Flash |
| LMArena Text: creative writing | 1296 | 1271 | GLM 4.7 Flash |
| LMArena Text: English | 1373 | 1335 | GLM 4.7 Flash |
| LMArena Text: non-English | 1330 | 1295 | GLM 4.7 Flash |
| LMArena Text: Chinese | 1403 | 1347 | GLM 4.7 Flash |
| LMArena Text: German | 1336 | 1307 | GLM 4.7 Flash |
| LMArena Text: Japanese | Not measured | 1254 | Only Qwen3 30B A3B measured |
| LMArena Text: Korean | 1283 | 1261 | GLM 4.7 Flash |
| LMArena Text: French | 1332 | 1352 | Qwen3 30B A3B |
| LMArena Text: Spanish | 1350 | 1317 | GLM 4.7 Flash |
| LMArena Text: Russian | 1333 | 1291 | GLM 4.7 Flash |
| LMArena Text: Polish | 1291 | 1299 | Qwen3 30B A3B |
| LMArena Text: multi-turn | 1342 | 1307 | GLM 4.7 Flash |
| LMArena Text: longer queries | 1345 | 1312 | GLM 4.7 Flash |
| LMArena Text: writing and language | 1320 | 1290 | GLM 4.7 Flash |
| LMArena Text: entertainment and media | 1304 | 1258 | GLM 4.7 Flash |
| 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 | Qwen3 30B A3B | 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 | Qwen3 30B A3B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | January 19, 2026 | April 28, 2025 |
| Context window | 200K | 41K |
| Input $/Mtok | $0.0653 | $0.12 |
| Output $/Mtok | $0.40 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 Flash better than Qwen3 30B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and a larger context window (200K against 41K tokens).
Which is cheaper, GLM 4.7 Flash or Qwen3 30B A3B?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or Qwen3 30B A3B?
GLM 4.7 Flash, with 200K tokens against 41K.
How many benchmarks is the GLM 4.7 Flash vs Qwen3 30B A3B comparison based on?
41 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 Qwen3 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.