Kimi K2 0711 vs Qwen3 32B
Updated October 21, 2026. 14 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Kimi K2 0711 for coding: it scores 64.7 on the Vibe Coding Index against 40.0 for Qwen3 32B.
Pick Qwen3 32B when output tokens cost 8.2x less, or when what matters is AA Math Index or AIME 2025 (Artificial Analysis run), where it leads.
- Shared results
- 14
- Kimi K2 0711 only
- 22
- Qwen3 32B only
- 28
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
Kimi K2 0711
Kimi K2 0711 scores 64.7 against 40.0.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Kimi K2 0711
Kimi K2 0711 leads, 12.7 against 7.3.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Kimi K2 0711 has a GPQA Diamond result, 71.5%.
Directional only
Math
AIME
No call
Only Kimi K2 0711 has a AIME result, 62.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K2 0711 has a MMLU Pro result, 79.4%.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2 0711 has a Design Arena: websites result, 1050.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
Kimi K2 0711 $0.0017, Qwen3 32B $0.0002 per turn.
List prices
At a glance
- Coding index
- Kimi K2 0711 64.7 from 2 benchmarks, Qwen3 32B 40.0 from 1 benchmark.
- Widest category
- Agentic work, 31.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Kimi K2 0711 53.0% against Qwen3 32B 0.0%, 53.0 points apart on a 0 to 100 scale.
- Output price
- Kimi K2 0711 $2.30 against Qwen3 32B $0.28 per million output tokens, 8.2x apart.
- Context window
- Both take 131K 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 0711 | Bars | Qwen3 32B | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 0 | 1 tie | |
| Math | 0 | 2 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 1 | ||
| All shared boards | 10 | 3 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index30
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | 59.1% | 40.0% | Kimi K2 0711 |
| SWE-bench Verified | 71.2% | Not measured | Only Kimi K2 0711 measured |
| LiveCodeBench (Vals AI) | 70.5% | Not measured | Only Kimi K2 0711 measured |
| LiveCodeBench (Artificial Analysis run) | 55.6% | 54.6% | Kimi K2 0711 |
| SciCode | Not measured | 36.0% | Only Qwen3 32B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | 3.0% | Kimi K2 0711 |
| Terminal-Bench | 27.8% | Not measured | Only Kimi K2 0711 measured |
| GSO-Bench | 4.9% | Not measured | Only Kimi K2 0711 measured |
| WeirdML | 39.4% | Not measured | Only Kimi K2 0711 measured |
| LMArena Text: coding | Not measured | 1357 | Only Qwen3 32B measured |
| LMArena Text: software and IT | Not measured | 1366 | Only Qwen3 32B measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.7 | 7.3 | Kimi K2 0711 |
| Epoch Capabilities Index | 140.0 | 138.4 | Kimi K2 0711 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 61.1% | 29.8% | Kimi K2 0711 |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond | 71.5% | Not measured | Only Kimi K2 0711 measured |
| GPQA Diamond (Artificial Analysis run) | 76.6% | 66.8% | Kimi K2 0711 |
| Humanity's Last Exam (Artificial Analysis run) | 7.4% | 7.4% | Tie |
| GPQA Diamond (Epoch AI run) | Not measured | 65.7% | Only Qwen3 32B measured |
| SimpleBench | 26.3% | Not measured | Only Kimi K2 0711 measured |
| LMArena Text: hard prompts | Not measured | 1334 | Only Qwen3 32B measured |
| LMArena Text: expert | Not measured | 1362 | Only Qwen3 32B measured |
| LMArena Text: hard prompts in English | Not measured | 1350 | Only Qwen3 32B measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| AIME | 62.7% | Not measured | Only Kimi K2 0711 measured |
| MATH 500 (Vals AI) | 94.2% | Not measured | Only Kimi K2 0711 measured |
| MGSM (Vals AI) | 91.0% | Not measured | Only Kimi K2 0711 measured |
| AA Math Index | 57.0 | 73.0 | Qwen3 32B |
| AIME 2025 (Artificial Analysis run) | 57.0% | 73.0% | Qwen3 32B |
| OTIS Mock AIME | Not measured | 66.9% | Only Qwen3 32B measured |
| LMArena Text: math | Not measured | 1399 | Only Qwen3 32B measured |
| LMArena Text: mathematical work | Not measured | 1420 | Only Qwen3 32B measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| MMLU Pro | 79.4% | Not measured | Only Kimi K2 0711 measured |
| LegalBench (Vals AI) | 81.5% | Not measured | Only Kimi K2 0711 measured |
| CorpFin (Vals AI) | 50.4% | Not measured | Only Kimi K2 0711 measured |
| TaxEval (Vals AI) | 70.2% | Not measured | Only Kimi K2 0711 measured |
| MedQA (Vals AI) | 84.0% | Not measured | Only Kimi K2 0711 measured |
| MMLU Pro (Artificial Analysis run) | 82.4% | 79.8% | Kimi K2 0711 |
| LMArena Text: science | Not measured | 1372 | Only Qwen3 32B measured |
| LMArena Text: medicine and healthcare | Not measured | 1368 | Only Qwen3 32B measured |
| LMArena Text: legal and government | Not measured | 1324 | Only Qwen3 32B measured |
| LMArena Text: business and finance | Not measured | 1318 | Only Qwen3 32B measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| Design Arena: websites | 1050 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: UI components | 1030 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: game dev | 974 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: data viz | 1018 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: all code categories | 1034 | Not measured | Only Kimi K2 0711 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Kimi K2 0711 | Qwen3 32B | Leader |
|---|---|---|---|
| IFBench | 41.5% | 36.3% | Kimi K2 0711 |
| Long context reasoning (AA-LCR) | 53.0% | 0.0% | Kimi K2 0711 |
| DTBench | Not measured | 67.5% | Only Qwen3 32B measured |
| LMCA | Not measured | 17.3% | Only Qwen3 32B measured |
| Fiction.LiveBench | 61.1% | 74.2% | Qwen3 32B |
| Creative writing (Lech Mazur) | 85.6% | Not measured | Only Kimi K2 0711 measured |
| CL-bench | 11.9% | Not measured | Only Kimi K2 0711 measured |
| LMArena Text | Not measured | 1340 | Only Qwen3 32B measured |
| LMArena Text: instruction following | Not measured | 1305 | Only Qwen3 32B measured |
| LMArena Text: creative writing | Not measured | 1297 | Only Qwen3 32B measured |
| LMArena Text: English | Not measured | 1359 | Only Qwen3 32B measured |
| LMArena Text: non-English | Not measured | 1317 | Only Qwen3 32B measured |
| LMArena Text: Chinese | Not measured | 1357 | Only Qwen3 32B measured |
| LMArena Text: German | Not measured | 1341 | Only Qwen3 32B measured |
| LMArena Text: Russian | Not measured | 1311 | Only Qwen3 32B measured |
| LMArena Text: multi-turn | Not measured | 1331 | Only Qwen3 32B measured |
| LMArena Text: longer queries | Not measured | 1327 | Only Qwen3 32B measured |
| LMArena Text: writing and language | Not measured | 1298 | Only Qwen3 32B measured |
| LMArena Text: entertainment and media | Not measured | 1279 | Only Qwen3 32B measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Kimi K2 0711 | Qwen3 32B | 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 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B 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 0711 | Qwen3 32B |
|---|---|---|
| Maker | Moonshot AI | Qwen |
| Weights | Open weights | Open weights |
| Released | July 11, 2025 | April 28, 2025 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.57 | $0.08 |
| Output $/Mtok | $2.30 | $0.28 |
| Coding index | 64.7 | 40.0 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Kimi K2 0711 better than Qwen3 32B for coding?
Pick Kimi K2 0711 for coding: it scores 64.7 on the Vibe Coding Index against 40.0 for Qwen3 32B. Pick Qwen3 32B when output tokens cost 8.2x less, or when what matters is AA Math Index or AIME 2025 (Artificial Analysis run), where it leads.
Which is cheaper, Kimi K2 0711 or Qwen3 32B?
Kimi K2 0711 costs $0.57 per million input tokens and $2.30 per million output tokens; Qwen3 32B costs $0.08 and $0.28. Cost per solved task is known for neither of them.
Which has the bigger context window, Kimi K2 0711 or Qwen3 32B?
Neither: both take 131K tokens.
How many benchmarks is the Kimi K2 0711 vs Qwen3 32B comparison based on?
14 benchmarks have a result for both models as of October 21, 2026. Kimi K2 0711's coding index rests on 2 independent benchmarks and Qwen3 32B's on 1. Where only one model has a result, the page says so instead of naming a winner.