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Kimi K2.6 vs Qwen-Plus

Qwen-Plus costs 5.1x less per output token than Kimi K2.6 ($0.78 vs $4.00 per million). Updated October 21, 2026; 27 benchmarks have a result for both.

Kimi K2.6Moonshot AI
Coding
52.1
Overall
60.5
Output $/M
$4.00
27
Qwen-PlusQwen
Coding
No data
Overall
No data
Output $/M
$0.78
0
vs boards won of 27 shared

Kimi K2.6 or Qwen-Plus: which to pick

Only Kimi K2.6 has a coding score, 52.1 on the Vibe Coding Index; Qwen-Plus has none, so the two cannot be ranked on coding.

Pick Qwen-Plus for output tokens 5.1x cheaper ($0.78 vs $4.00 per million) and a larger context window (1M against 262K tokens).

Shared results
27
Kimi K2.6 only
82
Qwen-Plus only
1
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.

At a glance

Coding index
Kimi K2.6 52.1 from 3 benchmarks, Qwen-Plus not benchmarked.
Widest category
Math, 42.5 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: Kimi K2.6 96.1% against Qwen-Plus 17.8%, 78.3 points apart on a 0 to 100 scale.
Output price
Kimi K2.6 $4.00 against Qwen-Plus $0.78 per million output tokens, 5.1x apart.
Context window
Kimi K2.6 takes 262K tokens, Qwen-Plus 1M.

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 K2.6BarsQwen-PlusTies
Coding20
Reasoning40
Math30
Knowledge40
General140
All shared boards270
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
LMArena WebDev1509Not measuredOnly Kimi K2.6 measured
AA Coding Index61.8Not measuredOnly Kimi K2.6 measured
LiveCodeBench (Vals AI)86.8%Not measuredOnly Kimi K2.6 measured
SWE-bench Verified (Vals AI)76.2%Not measuredOnly Kimi K2.6 measured
Vibe Code Bench (Vals AI)37.9%Not measuredOnly Kimi K2.6 measured
Code migration (Vals AI)27.8%Not measuredOnly Kimi K2.6 measured
ProgramBench (Vals AI)0.0%Not measuredOnly Kimi K2.6 measured
Terminal-Bench 2.1 (Vals AI)53.6%Not measuredOnly Kimi K2.6 measured
SciCode51.5%Not measuredOnly Kimi K2.6 measured
Terminal-Bench Hard (Artificial Analysis run)43.9%Not measuredOnly Kimi K2.6 measured
WeirdML55.9%Not measuredOnly Kimi K2.6 measured
ALE-Bench1092.7Not measuredOnly Kimi K2.6 measured
LMArena Text: coding14891328Kimi K2.6
LMArena Text: software and IT14841343Kimi K2.6
LMArena WebDev: React1505Not measuredOnly Kimi K2.6 measured
LMArena WebDev: HTML1485Not measuredOnly Kimi K2.6 measured
LMArena WebDev: from an image1521Not measuredOnly Kimi K2.6 measured
Neither model has a result on the other 22 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
AA Intelligence Index27.0Not measuredOnly Kimi K2.6 measured
Epoch Capabilities Index150.9Not measuredOnly Kimi K2.6 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
Agentic work benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
AA Agentic Index20.5Not measuredOnly Kimi K2.6 measured
Design Arena: agents building web apps1268Not measuredOnly Kimi K2.6 measured
Design Arena: agents building full-stack apps1155Not measuredOnly Kimi K2.6 measured
Design Arena: agents building mobile apps1157Not measuredOnly Kimi K2.6 measured
Design Arena: agents building Android apps1160Not measuredOnly Kimi K2.6 measured
Design Arena: agents building Godot games1120Not measuredOnly Kimi K2.6 measured
Design Arena: agents building games1141Not measuredOnly Kimi K2.6 measured
Design Arena: HTML slides1184Not measuredOnly Kimi K2.6 measured
Design Arena: PowerPoint slides1128Not measuredOnly Kimi K2.6 measured
Design Arena: spreadsheets1276Not measuredOnly Kimi K2.6 measured
Finance Agent (Vals AI)44.9%Not measuredOnly Kimi K2.6 measured
Tax Agent Bench (Vals AI)12.4%Not measuredOnly Kimi K2.6 measured
Legal Research Bench (Vals AI)15.9%Not measuredOnly Kimi K2.6 measured
Excel Modeling Benchmark (Vals AI)57.9%Not measuredOnly Kimi K2.6 measured
τ²-Bench Telecom95.9%Not measuredOnly Kimi K2.6 measured
OSWorld 2.04.6%Not measuredOnly Kimi K2.6 measured
Vending-Bench 2$6,205Not measuredOnly Kimi K2.6 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
GPQA Diamond89.1%Not measuredOnly Kimi K2.6 measured
MMMU Pro (Vals AI)86.3%Not measuredOnly Kimi K2.6 measured
GPQA Diamond (Artificial Analysis run)91.1%Not measuredOnly Kimi K2.6 measured
Humanity's Last Exam (Artificial Analysis run)37.5%Not measuredOnly Kimi K2.6 measured
GPQA Diamond (Epoch AI run)90.8%48.1%Kimi K2.6
Chess puzzles20.0%Not measuredOnly Kimi K2.6 measured
Mystery game puzzles18.0%Not measuredOnly Kimi K2.6 measured
EBR-bench2.4%Not measuredOnly Kimi K2.6 measured
LMArena Text: hard prompts14701317Kimi K2.6
LMArena Text: expert14911328Kimi K2.6
LMArena Text: hard prompts in English14711333Kimi K2.6
LMArena Vision: diagrams1298Not measuredOnly Kimi K2.6 measured
LMArena Vision: homework1310Not measuredOnly Kimi K2.6 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
OTIS Mock AIME96.1%17.8%Kimi K2.6
FrontierMath, tiers 1 to 357.2%Not measuredOnly Kimi K2.6 measured
FrontierMath, tier 425.6%Not measuredOnly Kimi K2.6 measured
ProofBench16.0%Not measuredOnly Kimi K2.6 measured
MATH level 5Not measured65.3%Only Qwen-Plus measured
LMArena Text: math14741326Kimi K2.6
LMArena Text: mathematical work14801332Kimi K2.6
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
MMLU Pro87.6%Not measuredOnly Kimi K2.6 measured
LegalBench (Vals AI)84.7%Not measuredOnly Kimi K2.6 measured
CaseLaw (Vals AI)61.2%Not measuredOnly Kimi K2.6 measured
CorpFin (Vals AI)66.7%Not measuredOnly Kimi K2.6 measured
TaxEval (Vals AI)74.7%Not measuredOnly Kimi K2.6 measured
MortgageTax (Vals AI)65.8%Not measuredOnly Kimi K2.6 measured
MedCode (Vals AI)40.1%Not measuredOnly Kimi K2.6 measured
SimpleQA Verified34.9%Not measuredOnly Kimi K2.6 measured
LMArena Text: science14681352Kimi K2.6
LMArena Text: medicine and healthcare14571350Kimi K2.6
LMArena Text: legal and government14621349Kimi K2.6
LMArena Text: business and finance14451303Kimi K2.6
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
Design Arena: websites1269Not measuredOnly Kimi K2.6 measured
Design Arena: UI components1266Not measuredOnly Kimi K2.6 measured
Design Arena: game dev1251Not measuredOnly Kimi K2.6 measured
Design Arena: data viz1249Not measuredOnly Kimi K2.6 measured
Design Arena: 3D1268Not measuredOnly Kimi K2.6 measured
Design Arena: all code categories1272Not measuredOnly Kimi K2.6 measured
Design Arena: SVG1181Not measuredOnly Kimi K2.6 measured
Design Arena: ASCII art1165Not measuredOnly Kimi K2.6 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
General benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
Public Benefits Bench (Vals AI)56.6%Not measuredOnly Kimi K2.6 measured
SAGE (Vals AI)50.2%Not measuredOnly Kimi K2.6 measured
MedScribe (Vals AI)78.2%Not measuredOnly Kimi K2.6 measured
Vals Multimodal Index56.4%Not measuredOnly Kimi K2.6 measured
IFBench76.0%Not measuredOnly Kimi K2.6 measured
Long context reasoning (AA-LCR)81.0%Not measuredOnly Kimi K2.6 measured
DTBench90.9%81.1%Kimi K2.6
LMCA37.3%24.0%Kimi K2.6
Furniture assembly21.7%Not measuredOnly Kimi K2.6 measured
ExploitBench18.4%Not measuredOnly Kimi K2.6 measured
LMArena Text14551326Kimi K2.6
LMArena Text: instruction following14511303Kimi K2.6
LMArena Text: creative writing14341292Kimi K2.6
LMArena Vision1282Not measuredOnly Kimi K2.6 measured
LMArena Text: English14601339Kimi K2.6
LMArena Text: non-English14461310Kimi K2.6
LMArena Text: Chinese15201347Kimi K2.6
LMArena Text: German1451Not measuredOnly Kimi K2.6 measured
LMArena Text: Japanese14431251Kimi K2.6
LMArena Text: Korean1427Not measuredOnly Kimi K2.6 measured
LMArena Text: French1471Not measuredOnly Kimi K2.6 measured
LMArena Text: Spanish1464Not measuredOnly Kimi K2.6 measured
LMArena Text: Russian14461323Kimi K2.6
LMArena Text: Polish1438Not measuredOnly Kimi K2.6 measured
LMArena Text: multi-turn14531336Kimi K2.6
LMArena Text: longer queries14681324Kimi K2.6
LMArena Text: writing and language14391296Kimi K2.6
LMArena Text: entertainment and media14281270Kimi K2.6
LMArena Vision: English1287Not measuredOnly Kimi K2.6 measured
LMArena Vision: Chinese1346Not measuredOnly Kimi K2.6 measured
LMArena Vision: reading text1291Not measuredOnly Kimi K2.6 measured
LMArena Vision: humor1280Not measuredOnly Kimi K2.6 measured
LMArena Vision: creative writing1273Not measuredOnly Kimi K2.6 measured
LMArena Document1451Not measuredOnly Kimi K2.6 measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Kimi K2.6 and Qwen-Plus
BenchmarkKimi K2.6Qwen-PlusLeader
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 K2.6$0.0029Fits in one request
Qwen-Plus$0.0006Fits in one request

Qwen-Plus costs less

Repository review

50K input + 3K output tokens

Kimi K2.6$0.059Fits in one request
Qwen-Plus$0.015Fits in one request

Qwen-Plus costs less

Agent loop

200K input + 10K output tokens

Kimi K2.6$0.230Fits in one request
Qwen-Plus$0.060Fits in one request

Qwen-Plus 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 K2.6 and Qwen-Plus
SpecificationMoonshotAI Kimi K2.6Qwen-Plus
MakerMoonshot AIQwen
WeightsOpen weightsNo public weights listed
ReleasedApril 20, 2026February 1, 2025
Context window262K1M
Input $/Mtok$0.95$0.26
Output $/Mtok$4.00$0.78
Coding index52.1Not benchmarked
Independent measurements behind it30
Cost per solved task$0.35Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Kimi K2.6 better than Qwen-Plus for coding?

Only Kimi K2.6 has a coding score, 52.1 on the Vibe Coding Index; Qwen-Plus has none, so the two cannot be ranked on coding. Pick Qwen-Plus for output tokens 5.1x cheaper ($0.78 vs $4.00 per million) and a larger context window (1M against 262K tokens).

Which is cheaper, Kimi K2.6 or Qwen-Plus?

Kimi K2.6 costs $0.95 per million input tokens and $4.00 per million output tokens; Qwen-Plus costs $0.26 and $0.78. Cost per solved task is known for Kimi K2.6 only.

Which has the bigger context window, Kimi K2.6 or Qwen-Plus?

Qwen-Plus, with 1M tokens against 262K.

How many benchmarks is the Kimi K2.6 vs Qwen-Plus comparison based on?

27 benchmarks have a result for both models as of October 21, 2026. Kimi K2.6's coding index rests on 3 independent benchmarks and Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.

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