GLM 4.7 Flash vs Qwen3.5 Plus 2026-02-15
GLM 4.7 Flash costs 3.9x less per output token than Qwen3.5 Plus 2026-02-15 ($0.40 vs $1.56 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GLM 4.7 Flash or Qwen3.5 Plus 2026-02-15: 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 3.9x cheaper ($0.40 vs $1.56 per million) and work measured by Design Arena: data viz or Design Arena: SVG, where it leads.
- Shared results
- 7
- GLM 4.7 Flash only
- 36
- Qwen3.5 Plus 2026-02-15 only
- 1
- Like-for-like categories
- 1 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
No call
Only GLM 4.7 Flash has a AA Intelligence Index result, 14.9.
Directional only
Web design
Design Arena: websites
GLM 4.7 Flash
GLM 4.7 Flash leads, 1194 against 1187.
Same board, both measured
Long documents
Listed context window
Qwen3.5 Plus 2026-02-15
Qwen3.5 Plus 2026-02-15 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, Qwen3.5 Plus 2026-02-15 $0.0010 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, Qwen3.5 Plus 2026-02-15 not benchmarked.
- Widest category
- Web design, 2.9 points apart on average over 7 shared benchmarks.
- Widest single gap
- Design Arena: SVG: GLM 4.7 Flash 1037 against Qwen3.5 Plus 2026-02-15 1107, 8.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against Qwen3.5 Plus 2026-02-15 $1.56 per million output tokens, 3.9x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, Qwen3.5 Plus 2026-02-15 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 | Qwen3.5 Plus 2026-02-15 | Ties |
|---|---|---|---|---|
| Web design | 5 | 2 | ||
| All shared boards | 5 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | Leader |
|---|---|---|---|
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: coding | 1382 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: software and IT | 1389 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | 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 | Qwen3.5 Plus 2026-02-15 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | 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% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts | 1356 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: expert | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts in English | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: math | 1355 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: mathematical work | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | Leader |
|---|---|---|---|
| LMArena Text: science | 1358 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: medicine and healthcare | 1343 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: legal and government | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: business and finance | 1343 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 14 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | 1187 | GLM 4.7 Flash |
| Design Arena: UI components | 1205 | 1175 | GLM 4.7 Flash |
| Design Arena: game dev | 1134 | 1105 | GLM 4.7 Flash |
| Design Arena: data viz | 1123 | 1135 | Qwen3.5 Plus 2026-02-15 |
| Design Arena: 3D | 1120 | 1118 | GLM 4.7 Flash |
| Design Arena: all code categories | 1177 | 1166 | GLM 4.7 Flash |
| Design Arena: SVG | 1037 | 1107 | Qwen3.5 Plus 2026-02-15 |
| Design Arena: ASCII art | Not measured | 1094 | Only Qwen3.5 Plus 2026-02-15 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | 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 |
| LMArena Text | 1351 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: instruction following | 1327 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: creative writing | 1296 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: English | 1373 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: non-English | 1330 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Chinese | 1403 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: German | 1336 | Not measured | Only GLM 4.7 Flash 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 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Polish | 1291 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | 1342 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: longer queries | 1345 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: writing and language | 1320 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: entertainment and media | 1304 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | Qwen3.5 Plus 2026-02-15 | 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 | Qwen3.5 Plus 2026-02-15 |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | February 16, 2026 |
| Context window | 200K | 1M |
| Input $/Mtok | $0.0653 | $0.26 |
| Output $/Mtok | $0.40 | $1.56 |
| 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 Qwen3.5 Plus 2026-02-15 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 3.9x cheaper ($0.40 vs $1.56 per million) and work measured by Design Arena: data viz or Design Arena: SVG, where it leads.
Which is cheaper, GLM 4.7 Flash or Qwen3.5 Plus 2026-02-15?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen3.5 Plus 2026-02-15 costs $0.26 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or Qwen3.5 Plus 2026-02-15?
Qwen3.5 Plus 2026-02-15, with 1M tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs Qwen3.5 Plus 2026-02-15 comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Qwen3.5 Plus 2026-02-15's on 0. Where only one model has a result, the page says so instead of naming a winner.