Kimi K2 0905 vs Qwen3.5-122B-A10B
Qwen3.5-122B-A10B leads Kimi K2 0905 on the Artificial Analysis Intelligence Index, 17.7 to 15.3, and Qwen3.5-122B-A10B costs 1.2x less per output token ($2.08 vs $2.50 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
Kimi K2 0905 or Qwen3.5-122B-A10B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Kimi K2 0905 for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 7
- Kimi K2 0905 only
- 10
- Qwen3.5-122B-A10B 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
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B leads, 17.7 against 15.3.
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
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-122B-A10B
Kimi K2 0905 $0.0019, Qwen3.5-122B-A10B $0.0013 per turn.
List prices
At a glance
- Coding index
- Kimi K2 0905 not benchmarked, Qwen3.5-122B-A10B not benchmarked.
- Widest category
- General, 28.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- IFBench: Kimi K2 0905 41.7% against Qwen3.5-122B-A10B 75.7%, 34.0 points apart on a 0 to 100 scale.
- Output price
- Kimi K2 0905 $2.50 against Qwen3.5-122B-A10B $2.08 per million output tokens, 1.2x apart.
- Context window
- Both take 262K tokens.
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 | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1361 | Only Qwen3.5-122B-A10B measured |
| AA Coding Index | Not measured | 43.3 | Only Qwen3.5-122B-A10B measured |
| LiveCodeBench (Artificial Analysis run) | 61.0% | Not measured | Only Kimi K2 0905 measured |
| SciCode | Not measured | 39.7% | Only Qwen3.5-122B-A10B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | 31.1% | Qwen3.5-122B-A10B |
| WeirdML | 36.7% | Not measured | Only Kimi K2 0905 measured |
| ALE-Bench | 267.1 | Not measured | Only Kimi K2 0905 measured |
| LMArena Text: coding | Not measured | 1436 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: software and IT | Not measured | 1436 | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: React | Not measured | 1353 | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: HTML | Not measured | 1351 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.3 | 17.7 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 73.4% | 93.6% | Qwen3.5-122B-A10B |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.7% | 85.7% | Qwen3.5-122B-A10B |
| Humanity's Last Exam (Artificial Analysis run) | 6.4% | 25.2% | Qwen3.5-122B-A10B |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts | Not measured | 1421 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: expert | Not measured | 1431 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts in English | Not measured | 1431 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: homework | Not measured | 1261 | Only Qwen3.5-122B-A10B measured |
| Kagi LLM Benchmark | 52.2% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Math Index | 57.3 | Not measured | Only Kimi K2 0905 measured |
| AIME 2025 (Artificial Analysis run) | 57.3% | Not measured | Only Kimi K2 0905 measured |
| LMArena Text: math | Not measured | 1431 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: mathematical work | Not measured | 1431 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.9% | Not measured | Only Kimi K2 0905 measured |
| LMArena Text: science | Not measured | 1432 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: medicine and healthcare | Not measured | 1423 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: legal and government | Not measured | 1422 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: business and finance | Not measured | 1414 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | 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 index02
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| IFBench | 41.7% | 75.7% | Qwen3.5-122B-A10B |
| Long context reasoning (AA-LCR) | 53.0% | 76.3% | Qwen3.5-122B-A10B |
| DTBench | Not measured | 84.3% | Only Qwen3.5-122B-A10B measured |
| LMCA | Not measured | 32.2% | Only Qwen3.5-122B-A10B measured |
| Fiction.LiveBench | 66.7% | Not measured | Only Kimi K2 0905 measured |
| LMArena Text | Not measured | 1417 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: instruction following | Not measured | 1399 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: creative writing | Not measured | 1368 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision | Not measured | 1244 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: English | Not measured | 1428 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: non-English | Not measured | 1401 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Chinese | Not measured | 1463 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: German | Not measured | 1425 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Japanese | Not measured | 1367 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Korean | Not measured | 1353 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: French | Not measured | 1443 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Spanish | Not measured | 1425 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Russian | Not measured | 1400 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Polish | Not measured | 1398 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: multi-turn | Not measured | 1416 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: longer queries | Not measured | 1410 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: writing and language | Not measured | 1387 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: entertainment and media | Not measured | 1366 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: English | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: reading text | Not measured | 1251 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: humor | Not measured | 1243 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Kimi K2 0905 | Qwen3.5-122B-A10B | 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
Qwen3.5-122B-A10B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-122B-A10B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-122B-A10B 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 | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | Moonshot AI | Qwen |
| Weights | Open weights | Open weights |
| Released | September 4, 2025 | February 25, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.60 | $0.26 |
| Output $/Mtok | $2.50 | $2.08 |
| 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 Kimi K2 0905 better than Qwen3.5-122B-A10B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Kimi K2 0905 for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Kimi K2 0905 or Qwen3.5-122B-A10B?
Kimi K2 0905 costs $0.60 per million input tokens and $2.50 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.
Which has the bigger context window, Kimi K2 0905 or Qwen3.5-122B-A10B?
Neither: both take 262K tokens.
How many benchmarks is the Kimi K2 0905 vs Qwen3.5-122B-A10B comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. Kimi K2 0905's coding index rests on 0 independent benchmarks and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.