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Kimi K2 0905 vs Qwen3 30B A3B

Kimi K2 0905 leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 15.3 to 7.6, and Qwen3 30B A3B costs 5.0x less per output token ($0.50 vs $2.50 per million). Updated October 22, 2026; 15 benchmarks have a result for both.

Kimi K2 0905Moonshot AI
Coding
No data
Overall
No data
Output $/M
$2.50
13
Qwen3 30B A3BQwen
Coding
No data
Overall
38.2
Output $/M
$0.50
2
vs boards won of 15 shared

Kimi K2 0905 or Qwen3 30B A3B: 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
15
Kimi K2 0905 only
2
Qwen3 30B A3B only
37
Like-for-like categories
8 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.

At a glance

Coding index
Kimi K2 0905 not benchmarked, Qwen3 30B A3B not benchmarked.
Widest category
Agentic work, 47.4 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): Kimi K2 0905 53.0% against Qwen3 30B A3B 0.0%, 53.0 points apart on a 0 to 100 scale.
Output price
Kimi K2 0905 $2.50 against Qwen3 30B A3B $0.50 per million output tokens, 5.0x apart.
Context window
Kimi K2 0905 takes 262K tokens, Qwen3 30B A3B 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.

Boards won, where both have a result
CategoryKimi K2 0905BarsQwen3 30B A3BTies
Coding30
Intelligence10
Agentic work10
Reasoning20
Math02
Knowledge10
Web design20
General30
All shared boards132
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
LiveCodeBench (Artificial Analysis run)61.0%50.6%Kimi K2 0905
Terminal-Bench Hard (Artificial Analysis run)23.5%6.8%Kimi K2 0905
WeirdML36.7%29.8%Kimi K2 0905
ALE-Bench267.1Not measuredOnly Kimi K2 0905 measured
LMArena Text: codingNot measured1337Only Qwen3 30B A3B measured
LMArena Text: software and ITNot measured1340Only Qwen3 30B A3B measured
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
AA Intelligence Index15.37.6Kimi K2 0905
Epoch Capabilities IndexNot measured137.3Only Qwen3 30B A3B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
τ²-Bench Telecom73.4%26.0%Kimi K2 0905
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
GPQA Diamond (Artificial Analysis run)76.7%61.6%Kimi K2 0905
Humanity's Last Exam (Artificial Analysis run)6.4%6.2%Kimi K2 0905
GPQA Diamond (Epoch AI run)Not measured61.7%Only Qwen3 30B A3B measured
LMArena Text: hard promptsNot measured1314Only Qwen3 30B A3B measured
LMArena Text: expertNot measured1314Only Qwen3 30B A3B measured
LMArena Text: hard prompts in EnglishNot measured1329Only Qwen3 30B A3B measured
Kagi LLM Benchmark52.2%Not measuredOnly Kimi K2 0905 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
AA Math Index57.372.3Qwen3 30B A3B
AIME 2025 (Artificial Analysis run)57.3%72.3%Qwen3 30B A3B
OTIS Mock AIMENot measured62.8%Only Qwen3 30B A3B measured
LMArena Text: mathNot measured1355Only Qwen3 30B A3B measured
LMArena Text: mathematical workNot measured1363Only Qwen3 30B A3B measured
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
MMLU Pro (Artificial Analysis run)81.9%77.7%Kimi K2 0905
LMArena Text: scienceNot measured1321Only Qwen3 30B A3B measured
LMArena Text: medicine and healthcareNot measured1318Only Qwen3 30B A3B measured
LMArena Text: legal and governmentNot measured1314Only Qwen3 30B A3B measured
LMArena Text: business and financeNot measured1309Only Qwen3 30B A3B measured
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index20
Web design benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
Design Arena: websites1088954Kimi K2 0905
Design Arena: UI componentsNot measured943Only Qwen3 30B A3B measured
Design Arena: game devNot measured898Only Qwen3 30B A3B measured
Design Arena: data vizNot measured967Only Qwen3 30B A3B measured
Design Arena: all code categories1098941Kimi K2 0905
Neither model has a result on the other 4 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
General benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
IFBench41.7%41.5%Kimi K2 0905
Long context reasoning (AA-LCR)53.0%0.0%Kimi K2 0905
DTBenchNot measured60.3%Only Qwen3 30B A3B measured
LMCANot measured15.8%Only Qwen3 30B A3B measured
Fiction.LiveBench66.7%40.6%Kimi K2 0905
Creative writing (Lech Mazur)Not measured75.3%Only Qwen3 30B A3B measured
LMArena TextNot measured1317Only Qwen3 30B A3B measured
LMArena Text: instruction followingNot measured1283Only Qwen3 30B A3B measured
LMArena Text: creative writingNot measured1271Only Qwen3 30B A3B measured
LMArena Text: EnglishNot measured1335Only Qwen3 30B A3B measured
LMArena Text: non-EnglishNot measured1295Only Qwen3 30B A3B measured
LMArena Text: ChineseNot measured1347Only Qwen3 30B A3B measured
LMArena Text: GermanNot measured1307Only Qwen3 30B A3B measured
LMArena Text: JapaneseNot measured1254Only Qwen3 30B A3B measured
LMArena Text: KoreanNot measured1261Only Qwen3 30B A3B measured
LMArena Text: FrenchNot measured1352Only Qwen3 30B A3B measured
LMArena Text: SpanishNot measured1317Only Qwen3 30B A3B measured
LMArena Text: RussianNot measured1291Only Qwen3 30B A3B measured
LMArena Text: PolishNot measured1299Only Qwen3 30B A3B measured
LMArena Text: multi-turnNot measured1307Only Qwen3 30B A3B measured
LMArena Text: longer queriesNot measured1312Only Qwen3 30B A3B measured
LMArena Text: writing and languageNot measured1290Only Qwen3 30B A3B measured
LMArena Text: entertainment and mediaNot measured1258Only Qwen3 30B A3B measured
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Kimi K2 0905 and Qwen3 30B A3B
BenchmarkKimi K2 0905Qwen3 30B A3BLeader
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

Kimi K2 0905$0.0019Fits in one request
Qwen3 30B A3B$0.0004Fits in one request

Qwen3 30B A3B costs less

Repository review

50K input + 3K output tokens

Kimi K2 0905$0.037Fits in one request
Qwen3 30B A3B$0.0075Exceeds the context window

Qwen3 30B A3B costs less

Agent loop

200K input + 10K output tokens

Kimi K2 0905$0.145Fits in one request
Qwen3 30B A3B$0.029Exceeds the context window

Qwen3 30B A3B 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.

Specifications of Kimi K2 0905 and Qwen3 30B A3B
SpecificationMoonshotAI Kimi K2 0905Qwen3 30B A3B
MakerMoonshot AIQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 4, 2025April 28, 2025
Context window262K41K
Input $/Mtok$0.60$0.12
Output $/Mtok$2.50$0.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Kimi K2 0905 better than Qwen3 30B A3B 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 30B A3B?

Kimi K2 0905 costs $0.60 per million input tokens and $2.50 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for neither of them.

Which has the bigger context window, Kimi K2 0905 or Qwen3 30B A3B?

Kimi K2 0905, with 262K tokens against 41K.

How many benchmarks is the Kimi K2 0905 vs Qwen3 30B A3B comparison based on?

15 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 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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