Qwen3 14B vs Qwen3.6 35B A3B
Qwen3.6 35B A3B leads Qwen3 14B on the Artificial Analysis Intelligence Index, 18.2 to 6.7, and Qwen3 14B costs 4.3x less per output token ($0.23 vs $1.00 per million). Updated October 22, 2026; 13 benchmarks have a result for both.
Qwen3 14B or Qwen3.6 35B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 14B for output tokens 4.3x cheaper ($0.23 vs $1.00 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 13
- Qwen3 14B only
- 7
- Qwen3.6 35B A3B only
- 7
- Like-for-like categories
- 6 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
Qwen3.6 35B A3B
Qwen3.6 35B A3B leads, 18.2 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 35B A3B has a AA Agentic Index result, 13.1.
Directional only
Long documents
Listed context window
Qwen3.6 35B A3B
Qwen3.6 35B A3B takes 262K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
Qwen3 14B $0.0002, Qwen3.6 35B A3B $0.0006 per turn.
List prices
At a glance
- Coding index
- Qwen3 14B not benchmarked, Qwen3.6 35B A3B not benchmarked.
- Widest category
- Agentic work, 60.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Qwen3 14B 0.0% against Qwen3.6 35B A3B 71.7%, 71.7 points apart on a 0 to 100 scale.
- Output price
- Qwen3 14B $0.23 against Qwen3.6 35B A3B $1.00 per million output tokens, 4.3x apart.
- Context window
- Qwen3 14B takes 41K tokens, Qwen3.6 35B A3B 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 | Qwen3.6 35B A3B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| General | 0 | 4 | ||
| All shared boards | 0 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 41.9 | Only Qwen3.6 35B A3B measured |
| LiveCodeBench (Artificial Analysis run) | 52.3% | Not measured | Only Qwen3 14B measured |
| SciCode | 30.7% | 36.6% | Qwen3.6 35B A3B |
| Terminal-Bench Hard (Artificial Analysis run) | 5.3% | 34.8% | Qwen3.6 35B A3B |
| Terminal-Bench | Not measured | 24.6% | Only Qwen3.6 35B A3B measured |
| WeirdML | Not measured | 34.5% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 18.2 | Qwen3.6 35B A3B |
| Epoch Capabilities Index | 138.2 | 143.7 | Qwen3.6 35B A3B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 13.1 | Only Qwen3.6 35B A3B measured |
| τ²-Bench Telecom | 34.5% | 95.3% | Qwen3.6 35B A3B |
| Berkeley Function Calling Leaderboard (BFCL) | 41.0% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 60.4% | 84.1% | Qwen3.6 35B A3B |
| Humanity's Last Exam (Artificial Analysis run) | 4.5% | 22.2% | Qwen3.6 35B A3B |
| GPQA Diamond (Epoch AI run) | 63.8% | 84.8% | Qwen3.6 35B A3B |
| Mystery game puzzles | Not measured | 22.0% | Only Qwen3.6 35B A3B measured |
| Kagi LLM Benchmark | 49.1% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 18 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | 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% | 86.7% | Qwen3.6 35B A3B |
| FrontierMath, tiers 1 to 3 | Not measured | 20.4% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 77.4% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 19 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | 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 | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| IFBench | 40.5% | 64.4% | Qwen3.6 35B A3B |
| Long context reasoning (AA-LCR) | 0.0% | 71.7% | Qwen3.6 35B A3B |
| DTBench | 64.0% | 73.9% | Qwen3.6 35B A3B |
| LMCA | 18.2% | 29.7% | Qwen3.6 35B A3B |
| Fiction.LiveBench | 62.5% | Not measured | Only Qwen3 14B measured |
| Surface Evolver Bench | Not measured | 44.4% | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 14B | Qwen3.6 35B A3B | 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 | Qwen3.6 35B A3B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | April 28, 2025 | April 27, 2026 |
| Context window | 41K | 262K |
| Input $/Mtok | $0.11 | $0.10 |
| Output $/Mtok | $0.23 | $1.00 |
| 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 Qwen3.6 35B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 14B for output tokens 4.3x cheaper ($0.23 vs $1.00 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Qwen3 14B or Qwen3.6 35B A3B?
Qwen3 14B costs $0.11 per million input tokens and $0.23 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 14B or Qwen3.6 35B A3B?
Qwen3.6 35B A3B, with 262K tokens against 41K.
How many benchmarks is the Qwen3 14B vs Qwen3.6 35B A3B comparison based on?
13 benchmarks have a result for both models as of October 22, 2026. Qwen3 14B's coding index rests on 0 independent benchmarks and Qwen3.6 35B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.