GLM 5V Turbo vs Qwen3 14B
GLM 5V Turbo leads Qwen3 14B on the Artificial Analysis Intelligence Index, 23.5 to 6.7, and Qwen3 14B costs 17.4x less per output token ($0.23 vs $4.00 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GLM 5V Turbo or Qwen3 14B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for a larger context window (203K against 41K tokens).
- Shared results
- 7
- GLM 5V Turbo only
- 58
- Qwen3 14B only
- 13
- Like-for-like categories
- 5 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 5V Turbo
GLM 5V Turbo leads, 23.5 against 6.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5V Turbo has a Design Arena: websites result, 1230.
Directional only
Long documents
Listed context window
GLM 5V Turbo
GLM 5V Turbo takes 203K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
GLM 5V Turbo $0.0032, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- GLM 5V Turbo not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- Agentic work, 64.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 5V Turbo 70.3% against Qwen3 14B 0.0%, 70.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5V Turbo $4.00 against Qwen3 14B $0.23 per million output tokens, 17.4x apart.
- Context window
- GLM 5V Turbo takes 203K tokens, Qwen3 14B 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 5V Turbo | Bars | Qwen3 14B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 7 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| LMArena WebDev | 1402 | Not measured | Only GLM 5V Turbo measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 52.3% | Only Qwen3 14B measured |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | 5.3% | GLM 5V Turbo |
| LMArena Text: coding | 1466 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: software and IT | 1460 | Not measured | Only GLM 5V Turbo measured |
| LMArena WebDev: React | 1386 | Not measured | Only GLM 5V Turbo measured |
| LMArena WebDev: HTML | 1356 | Not measured | Only GLM 5V Turbo measured |
| LMArena WebDev: from an image | 1425 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.5 | 6.7 | GLM 5V Turbo |
| Epoch Capabilities Index | Not measured | 138.2 | Only Qwen3 14B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1124 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building full-stack apps | 1149 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building mobile apps | 1134 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building Android apps | 1267 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building Godot games | 957 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building games | 1102 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: HTML slides | 1070 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: PowerPoint slides | 1127 | Not measured | Only GLM 5V Turbo measured |
| τ²-Bench Telecom | 98.5% | 34.5% | GLM 5V Turbo |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.0% | Only Qwen3 14B measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 80.9% | 60.4% | GLM 5V Turbo |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 4.5% | GLM 5V Turbo |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: expert | 1453 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: hard prompts in English | 1456 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: diagrams | 1286 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: homework | 1273 | Not measured | Only GLM 5V Turbo measured |
| Kagi LLM Benchmark | Not measured | 49.1% | Only Qwen3 14B measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 58.0 | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 58.0% | Only Qwen3 14B measured |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B measured |
| LMArena Text: math | 1442 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: mathematical work | 1447 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 77.4% | Only Qwen3 14B measured |
| LMArena Text: science | 1448 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: medicine and healthcare | 1433 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: legal and government | 1436 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: business and finance | 1435 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| Design Arena: websites | 1230 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: UI components | 1210 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: game dev | 1218 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: data viz | 1202 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: 3D | 1208 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: all code categories | 1228 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: SVG | 1151 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: ASCII art | 1117 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5V Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | 61.1% | 40.5% | GLM 5V Turbo |
| Long context reasoning (AA-LCR) | 70.3% | 0.0% | GLM 5V Turbo |
| DTBench | Not measured | 64.0% | Only Qwen3 14B measured |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| LMArena Text | 1437 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: instruction following | 1423 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: creative writing | 1417 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision | 1264 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: English | 1454 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: non-English | 1420 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Chinese | 1491 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: German | 1422 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Korean | 1397 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: French | 1443 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Spanish | 1452 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Russian | 1432 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: Polish | 1432 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: multi-turn | 1431 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: longer queries | 1438 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: writing and language | 1420 | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: entertainment and media | 1407 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: English | 1274 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: Chinese | 1329 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: reading text | 1270 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: humor | 1268 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: captioning | 1264 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: recognizing things | 1241 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: creative writing | 1252 | Not measured | Only GLM 5V Turbo measured |
| LMArena Document | 1416 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5V Turbo | Qwen3 14B | 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
Qwen3 14B costs less
Repository review
50K input + 3K output tokens
Qwen3 14B costs less
Agent loop
200K input + 10K output tokens
Qwen3 14B 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 5V Turbo | Qwen3 14B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 1, 2026 | April 28, 2025 |
| Context window | 203K | 41K |
| Input $/Mtok | $1.20 | $0.11 |
| Output $/Mtok | $4.00 | $0.23 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5V Turbo better than Qwen3 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5V Turbo for a larger context window (203K against 41K tokens).
Which is cheaper, GLM 5V Turbo or Qwen3 14B?
GLM 5V Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 5V Turbo or Qwen3 14B?
GLM 5V Turbo, with 203K tokens against 41K.
How many benchmarks is the GLM 5V Turbo vs Qwen3 14B comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. GLM 5V Turbo's coding index rests on 1 independent benchmark and Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.