Qwen3 14B vs Qwen3.5-27B
Qwen3.5-27B leads Qwen3 14B on the Artificial Analysis Intelligence Index, 22.9 to 6.7, and Qwen3 14B costs 6.8x less per output token ($0.23 vs $1.56 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
Qwen3 14B or Qwen3.5-27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 14B for output tokens 6.8x cheaper ($0.23 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 9
- Qwen3 14B only
- 11
- Qwen3.5-27B only
- 44
- 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
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 6.7.
Same board, both measured
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
Qwen3 14B $0.0002, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- Qwen3 14B not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Agentic work, 59.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Qwen3 14B 0.0% against Qwen3.5-27B 77.7%, 77.7 points apart on a 0 to 100 scale.
- Output price
- Qwen3 14B $0.23 against Qwen3.5-27B $1.56 per million output tokens, 6.8x apart.
- Context window
- Qwen3 14B takes 41K tokens, Qwen3.5-27B 262K.
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 | Qwen3 14B | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 4 | ||
| All shared boards | 0 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1358 | Only Qwen3.5-27B measured |
| LiveCodeBench (Artificial Analysis run) | 52.3% | Not measured | Only Qwen3 14B measured |
| SciCode | 30.7% | Not measured | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 5.3% | 32.6% | Qwen3.5-27B |
| WeirdML | Not measured | 39.5% | Only Qwen3.5-27B measured |
| ALE-Bench | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | Not measured | 1427 | Only Qwen3.5-27B measured |
| LMArena Text: software and IT | Not measured | 1431 | Only Qwen3.5-27B measured |
| LMArena WebDev: React | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | Not measured | 1386 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 22.9 | Qwen3.5-27B |
| Epoch Capabilities Index | 138.2 | Not measured | Only Qwen3 14B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 34.5% | 93.9% | Qwen3.5-27B |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 41.0% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 60.4% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 4.5% | 23.9% | Qwen3.5-27B |
| GPQA Diamond (Epoch AI run) | 63.8% | Not measured | Only Qwen3 14B measured |
| LMArena Text: hard prompts | Not measured | 1415 | Only Qwen3.5-27B measured |
| LMArena Text: expert | Not measured | 1427 | Only Qwen3.5-27B measured |
| LMArena Text: hard prompts in English | Not measured | 1429 | Only Qwen3.5-27B measured |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Kagi LLM Benchmark | 49.1% | Not measured | 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 | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Math Index | 58.0 | Not measured | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | 58.0% | Not measured | Only Qwen3 14B measured |
| OTIS Mock AIME | 66.4% | Not measured | Only Qwen3 14B measured |
| LMArena Text: math | Not measured | 1429 | Only Qwen3.5-27B measured |
| LMArena Text: mathematical work | Not measured | 1429 | Only Qwen3.5-27B 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 | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 77.4% | Not measured | Only Qwen3 14B measured |
| LMArena Text: science | Not measured | 1437 | Only Qwen3.5-27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1425 | Only Qwen3.5-27B measured |
| LMArena Text: legal and government | Not measured | 1405 | Only Qwen3.5-27B measured |
| LMArena Text: business and finance | Not measured | 1399 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Qwen3 14B | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | 40.5% | 75.6% | Qwen3.5-27B |
| Long context reasoning (AA-LCR) | 0.0% | 77.7% | Qwen3.5-27B |
| DTBench | 64.0% | 82.4% | Qwen3.5-27B |
| LMCA | 18.2% | 34.0% | Qwen3.5-27B |
| Fiction.LiveBench | 62.5% | Not measured | Only Qwen3 14B measured |
| LMArena Text | Not measured | 1409 | Only Qwen3.5-27B measured |
| LMArena Text: instruction following | Not measured | 1393 | Only Qwen3.5-27B measured |
| LMArena Text: creative writing | Not measured | 1362 | Only Qwen3.5-27B measured |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | Not measured | 1425 | Only Qwen3.5-27B measured |
| LMArena Text: non-English | Not measured | 1390 | Only Qwen3.5-27B measured |
| LMArena Text: Chinese | Not measured | 1478 | Only Qwen3.5-27B measured |
| LMArena Text: German | Not measured | 1392 | Only Qwen3.5-27B measured |
| LMArena Text: Japanese | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena Text: Korean | Not measured | 1359 | Only Qwen3.5-27B measured |
| LMArena Text: French | Not measured | 1410 | Only Qwen3.5-27B measured |
| LMArena Text: Spanish | Not measured | 1407 | Only Qwen3.5-27B measured |
| LMArena Text: Russian | Not measured | 1390 | Only Qwen3.5-27B measured |
| LMArena Text: Polish | Not measured | 1375 | Only Qwen3.5-27B measured |
| LMArena Text: multi-turn | Not measured | 1410 | Only Qwen3.5-27B measured |
| LMArena Text: longer queries | Not measured | 1413 | Only Qwen3.5-27B measured |
| LMArena Text: writing and language | Not measured | 1380 | Only Qwen3.5-27B measured |
| LMArena Text: entertainment and media | Not measured | 1363 | Only Qwen3.5-27B measured |
| LMArena Vision: English | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | Only Qwen3.5-27B 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 | Qwen3 14B | Qwen3.5-27B | 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 | Qwen3 14B | Qwen3.5-27B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | April 28, 2025 | February 25, 2026 |
| Context window | 41K | 262K |
| Input $/Mtok | $0.11 | $0.195 |
| Output $/Mtok | $0.23 | $1.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3 14B better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 14B for output tokens 6.8x cheaper ($0.23 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Qwen3 14B or Qwen3.5-27B?
Qwen3 14B costs $0.11 per million input tokens and $0.23 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 14B or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 41K.
How many benchmarks is the Qwen3 14B vs Qwen3.5-27B comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. Qwen3 14B's coding index rests on 0 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.