Kimi K2 0905 vs Qwen3 14B
Kimi K2 0905 leads Qwen3 14B on the Artificial Analysis Intelligence Index, 15.3 to 6.7, and Qwen3 14B costs 10.9x less per output token ($0.23 vs $2.50 per million). Updated October 22, 2026; 13 benchmarks have a result for both.
Kimi K2 0905 or Qwen3 14B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Kimi K2 0905 for a larger context window (262K against 41K tokens) and work measured by AA Math Index or AIME 2025 (Artificial Analysis run), where it leads.
- Shared results
- 13
- Kimi K2 0905 only
- 4
- Qwen3 14B only
- 7
- 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
Kimi K2 0905
Kimi K2 0905 leads, 15.3 against 6.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only Kimi K2 0905 has a Design Arena: websites result, 1088.
Directional only
Long documents
Listed context window
Kimi K2 0905
Kimi K2 0905 takes 262K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
Kimi K2 0905 $0.0019, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- Kimi K2 0905 not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- Agentic work, 38.9 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Kimi K2 0905 53.0% against Qwen3 14B 0.0%, 53.0 points apart on a 0 to 100 scale.
- Output price
- Kimi K2 0905 $2.50 against Qwen3 14B $0.23 per million output tokens, 10.9x apart.
- Context window
- Kimi K2 0905 takes 262K 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 | Kimi K2 0905 | Bars | Qwen3 14B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 11 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 61.0% | 52.3% | Kimi K2 0905 |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | 5.3% | Kimi K2 0905 |
| WeirdML | 36.7% | Not measured | Only Kimi K2 0905 measured |
| ALE-Bench | 267.1 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.3 | 6.7 | Kimi K2 0905 |
| 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 | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 73.4% | 34.5% | Kimi K2 0905 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.0% | 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 index30
| Benchmark | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.7% | 60.4% | Kimi K2 0905 |
| Humanity's Last Exam (Artificial Analysis run) | 6.4% | 4.5% | Kimi K2 0905 |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| Kagi LLM Benchmark | 52.2% | 49.1% | Kimi K2 0905 |
| Neither model has a result on the other 18 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | 57.3 | 58.0 | Qwen3 14B |
| AIME 2025 (Artificial Analysis run) | 57.3% | 58.0% | Qwen3 14B |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.9% | 77.4% | Kimi K2 0905 |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| Design Arena: websites | 1088 | Not measured | Only Kimi K2 0905 measured |
| Design Arena: all code categories | 1098 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 7 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Kimi K2 0905 | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | 41.7% | 40.5% | Kimi K2 0905 |
| Long context reasoning (AA-LCR) | 53.0% | 0.0% | Kimi K2 0905 |
| DTBench | Not measured | 64.0% | Only Qwen3 14B measured |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | 66.7% | 62.5% | Kimi K2 0905 |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Kimi K2 0905 | 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 | MoonshotAI Kimi K2 0905 | Qwen3 14B |
|---|---|---|
| Maker | Moonshot AI | Qwen |
| Weights | Open weights | Open weights |
| Released | September 4, 2025 | April 28, 2025 |
| Context window | 262K | 41K |
| Input $/Mtok | $0.60 | $0.11 |
| Output $/Mtok | $2.50 | $0.23 |
| 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 Kimi K2 0905 better than Qwen3 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Kimi K2 0905 for a larger context window (262K against 41K tokens) and work measured by AA Math Index or AIME 2025 (Artificial Analysis run), where it leads.
Which is cheaper, Kimi K2 0905 or Qwen3 14B?
Kimi K2 0905 costs $0.60 per million input tokens and $2.50 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, Kimi K2 0905 or Qwen3 14B?
Kimi K2 0905, with 262K tokens against 41K.
How many benchmarks is the Kimi K2 0905 vs Qwen3 14B comparison based on?
13 benchmarks have a result for both models as of October 22, 2026. Kimi K2 0905's coding index rests on 0 independent benchmarks and Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.