GLM 4.7 Flash vs Qwen-Plus
Updated October 21, 2026. 24 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.7 Flash has the edge where output tokens cost 1.9x less, and where the benchmark that matters is LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 24
- GLM 4.7 Flash only
- 19
- Qwen-Plus only
- 4
- Like-for-like categories
- 5 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.7 Flash has a AA Intelligence Index result, 14.9.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 Flash has a Design Arena: websites result, 1194.
Directional only
Long documents
Listed context window
Qwen-Plus
Qwen-Plus takes 1M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, Qwen-Plus $0.0006 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, Qwen-Plus not benchmarked.
- Widest category
- Math, 17.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GLM 4.7 Flash 58.3% against Qwen-Plus 17.8%, 40.6 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against Qwen-Plus $0.78 per million output tokens, 1.9x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, Qwen-Plus 1M.
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 | Qwen-Plus | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 3 | 1 | ||
| General | 11 | 0 | ||
| All shared boards | 23 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: coding | 1382 | 1328 | GLM 4.7 Flash |
| LMArena Text: software and IT | 1389 | 1343 | GLM 4.7 Flash |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | Not measured | Only GLM 4.7 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | Not measured | Only GLM 4.7 Flash measured |
| GPQA Diamond (Epoch AI run) | 60.5% | 48.1% | GLM 4.7 Flash |
| LMArena Text: hard prompts | 1356 | 1317 | GLM 4.7 Flash |
| LMArena Text: expert | 1357 | 1328 | GLM 4.7 Flash |
| LMArena Text: hard prompts in English | 1369 | 1333 | GLM 4.7 Flash |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | 17.8% | GLM 4.7 Flash |
| MATH level 5 | Not measured | 65.3% | Only Qwen-Plus measured |
| LMArena Text: math | 1355 | 1326 | GLM 4.7 Flash |
| LMArena Text: mathematical work | 1369 | 1332 | GLM 4.7 Flash |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| LMArena Text: science | 1358 | 1352 | GLM 4.7 Flash |
| LMArena Text: medicine and healthcare | 1343 | 1350 | Qwen-Plus |
| LMArena Text: legal and government | 1357 | 1349 | GLM 4.7 Flash |
| LMArena Text: business and finance | 1343 | 1303 | GLM 4.7 Flash |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: UI components | 1208 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: game dev | 1135 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: data viz | 1123 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: 3D | 1121 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | 1178 | Not measured | Only GLM 4.7 Flash measured |
| 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 index110
| Benchmark | GLM 4.7 Flash | Qwen-Plus | Leader |
|---|---|---|---|
| IFBench | 60.8% | Not measured | Only GLM 4.7 Flash measured |
| Long context reasoning (AA-LCR) | 41.7% | Not measured | Only GLM 4.7 Flash measured |
| DTBench | Not measured | 81.1% | Only Qwen-Plus measured |
| LMCA | Not measured | 24.0% | Only Qwen-Plus measured |
| LMArena Text | 1351 | 1326 | GLM 4.7 Flash |
| LMArena Text: instruction following | 1327 | 1303 | GLM 4.7 Flash |
| LMArena Text: creative writing | 1296 | 1292 | GLM 4.7 Flash |
| LMArena Text: English | 1373 | 1339 | GLM 4.7 Flash |
| LMArena Text: non-English | 1330 | 1310 | GLM 4.7 Flash |
| LMArena Text: Chinese | 1403 | 1347 | GLM 4.7 Flash |
| LMArena Text: German | 1336 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Japanese | Not measured | 1251 | Only Qwen-Plus measured |
| LMArena Text: Korean | 1283 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: French | 1332 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Spanish | 1350 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Russian | 1333 | 1323 | GLM 4.7 Flash |
| LMArena Text: Polish | 1291 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | 1342 | 1336 | GLM 4.7 Flash |
| LMArena Text: longer queries | 1345 | 1324 | GLM 4.7 Flash |
| LMArena Text: writing and language | 1320 | 1296 | GLM 4.7 Flash |
| LMArena Text: entertainment and media | 1304 | 1270 | GLM 4.7 Flash |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen-Plus | 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 | Qwen-Plus |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | February 1, 2025 |
| Context window | 200K | 1M |
| Input $/Mtok | $0.0653 | $0.26 |
| Output $/Mtok | $0.40 | $0.78 |
| 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 Qwen-Plus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 1.9x less, and where the benchmark that matters is LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, GLM 4.7 Flash or Qwen-Plus?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen-Plus costs $0.26 and $0.78. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or Qwen-Plus?
Qwen-Plus, with 1M tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs Qwen-Plus comparison based on?
24 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 Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.