Qwen3 14B vs Qwen3 30B A3B Instruct 2507
Qwen3 30B A3B Instruct 2507 leads Qwen3 14B on the Artificial Analysis Intelligence Index, 7.5 to 6.7, and Qwen3 14B costs 1.3x less per output token ($0.23 vs $0.30 per million). Updated October 22, 2026; 17 benchmarks have a result for both.
Qwen3 14B or Qwen3 30B A3B Instruct 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 14B for output tokens 1.3x cheaper ($0.23 vs $0.30 per million) and work measured by LiveCodeBench (Artificial Analysis run) or SciCode, where it leads.
- Shared results
- 17
- Qwen3 14B only
- 3
- Qwen3 30B A3B Instruct 2507 only
- 28
- Like-for-like categories
- 7 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 30B A3B Instruct 2507
Qwen3 30B A3B Instruct 2507 leads, 7.5 against 6.7.
Same board, both measured
Long documents
Listed context window
Qwen3 30B A3B Instruct 2507
Qwen3 30B A3B Instruct 2507 takes 262K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
Qwen3 14B $0.0002, Qwen3 30B A3B Instruct 2507 $0.0002 per turn.
List prices
At a glance
- Coding index
- Qwen3 14B not benchmarked, Qwen3 30B A3B Instruct 2507 not benchmarked.
- Widest category
- General, 19.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): Qwen3 14B 0.0% against Qwen3 30B A3B Instruct 2507 61.3%, 61.3 points apart on a 0 to 100 scale.
- Output price
- Qwen3 14B $0.23 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 1.3x apart.
- Context window
- Qwen3 14B takes 41K tokens, Qwen3 30B A3B Instruct 2507 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 30B A3B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 1 | 2 | ||
| Math | 1 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 4 | ||
| All shared boards | 3 | 14 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 52.3% | 70.7% | Qwen3 30B A3B Instruct 2507 |
| SciCode | 30.7% | 33.0% | Qwen3 30B A3B Instruct 2507 |
| Terminal-Bench Hard (Artificial Analysis run) | 5.3% | 6.1% | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: coding | Not measured | 1416 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: software and IT | Not measured | 1417 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 7.5 | Qwen3 30B A3B Instruct 2507 |
| Epoch Capabilities Index | 138.2 | Not measured | Only Qwen3 14B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 34.5% | 28.1% | Qwen3 14B |
| Berkeley Function Calling Leaderboard (BFCL) | 41.0% | 41.4% | Qwen3 30B A3B Instruct 2507 |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 60.4% | 70.7% | Qwen3 30B A3B Instruct 2507 |
| Humanity's Last Exam (Artificial Analysis run) | 4.5% | 10.3% | Qwen3 30B A3B Instruct 2507 |
| GPQA Diamond (Epoch AI run) | 63.8% | 55.6% | Qwen3 14B |
| LMArena Text: hard prompts | Not measured | 1398 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: expert | Not measured | 1397 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: hard prompts in English | Not measured | 1406 | Only Qwen3 30B A3B Instruct 2507 measured |
| Kagi LLM Benchmark | 49.1% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | 58.0 | 66.3 | Qwen3 30B A3B Instruct 2507 |
| AIME 2025 (Artificial Analysis run) | 58.0% | 66.3% | Qwen3 30B A3B Instruct 2507 |
| OTIS Mock AIME | 66.4% | 62.2% | Qwen3 14B |
| LMArena Text: math | Not measured | 1394 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: mathematical work | Not measured | 1411 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 77.4% | 80.5% | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: science | Not measured | 1393 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: medicine and healthcare | Not measured | 1392 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: legal and government | Not measured | 1387 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: business and finance | Not measured | 1389 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | 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 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| IFBench | 40.5% | 50.7% | Qwen3 30B A3B Instruct 2507 |
| Long context reasoning (AA-LCR) | 0.0% | 61.3% | Qwen3 30B A3B Instruct 2507 |
| DTBench | 64.0% | 67.2% | Qwen3 30B A3B Instruct 2507 |
| LMCA | 18.2% | 22.4% | Qwen3 30B A3B Instruct 2507 |
| Fiction.LiveBench | 62.5% | Not measured | Only Qwen3 14B measured |
| LMArena Text | Not measured | 1384 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: instruction following | Not measured | 1363 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: creative writing | Not measured | 1317 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: English | Not measured | 1391 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: non-English | Not measured | 1372 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Chinese | Not measured | 1432 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: German | Not measured | 1379 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Japanese | Not measured | 1337 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Korean | Not measured | 1330 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: French | Not measured | 1419 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Spanish | Not measured | 1404 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Russian | Not measured | 1369 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Polish | Not measured | 1353 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: multi-turn | Not measured | 1378 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: longer queries | Not measured | 1378 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: writing and language | Not measured | 1336 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: entertainment and media | Not measured | 1334 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 14B | Qwen3 30B A3B Instruct 2507 | 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 30B A3B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 30B A3B Instruct 2507 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 30B A3B Instruct 2507 |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | April 28, 2025 | July 29, 2025 |
| Context window | 41K | 262K |
| Input $/Mtok | $0.11 | $0.09 |
| Output $/Mtok | $0.23 | $0.30 |
| 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 Qwen3 14B better than Qwen3 30B A3B Instruct 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 14B for output tokens 1.3x cheaper ($0.23 vs $0.30 per million) and work measured by LiveCodeBench (Artificial Analysis run) or SciCode, where it leads.
Which is cheaper, Qwen3 14B or Qwen3 30B A3B Instruct 2507?
Qwen3 14B costs $0.11 per million input tokens and $0.23 per million output tokens; Qwen3 30B A3B Instruct 2507 costs $0.09 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 14B or Qwen3 30B A3B Instruct 2507?
Qwen3 30B A3B Instruct 2507, with 262K tokens against 41K.
How many benchmarks is the Qwen3 14B vs Qwen3 30B A3B Instruct 2507 comparison based on?
17 benchmarks have a result for both models as of October 22, 2026. Qwen3 14B's coding index rests on 0 independent benchmarks and Qwen3 30B A3B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.