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Kimi K2 Thinking vs Qwen3 VL 8B Instruct

Kimi K2 Thinking leads Qwen3 VL 8B Instruct on the Artificial Analysis Intelligence Index, 22.0 to 7.3, and Qwen3 VL 8B Instruct costs 3.3x less per output token ($0.75 vs $2.50 per million). Updated October 21, 2026; 11 benchmarks have a result for both.

Kimi K2 ThinkingMoonshot AI
Coding
No data
Overall
No data
Output $/M
$2.50
11
Qwen3 VL 8B InstructQwen
Coding
No data
Overall
No data
Output $/M
$0.75
0
vs boards won of 11 shared

Kimi K2 Thinking or Qwen3 VL 8B Instruct: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Shared results
11
Kimi K2 Thinking only
19
Qwen3 VL 8B Instruct only
0
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.

At a glance

Coding index
Kimi K2 Thinking not benchmarked, Qwen3 VL 8B Instruct not benchmarked.
Widest category
Math, 64.0 points apart on average over 2 shared benchmarks.
Widest single gap
AA Math Index: Kimi K2 Thinking 94.7 against Qwen3 VL 8B Instruct 30.7, 64.0 points apart on a 0 to 100 scale.
Output price
Kimi K2 Thinking $2.50 against Qwen3 VL 8B Instruct $0.75 per million output tokens, 3.3x 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.

Boards won, where both have a result
CategoryKimi K2 ThinkingBarsQwen3 VL 8B InstructTies
Coding20
Intelligence10
Agentic work10
Reasoning20
Math20
Knowledge10
General20
All shared boards110
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
SWE-bench Verified63.4%Not measuredOnly Kimi K2 Thinking measured
LiveCodeBench (Vals AI)63.1%Not measuredOnly Kimi K2 Thinking measured
SWE-bench Verified (Vals AI)60.2%Not measuredOnly Kimi K2 Thinking measured
LiveCodeBench (Artificial Analysis run)85.3%35.3%Kimi K2 Thinking
Terminal-Bench Hard (Artificial Analysis run)31.1%3.8%Kimi K2 Thinking
Terminal-Bench35.7%Not measuredOnly Kimi K2 Thinking measured
WeirdML42.8%Not measuredOnly Kimi K2 Thinking measured
ALE-Bench597.5Not measuredOnly Kimi K2 Thinking measured
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
AA Intelligence Index22.07.3Kimi K2 Thinking
Epoch Capabilities Index145.8Not measuredOnly Kimi K2 Thinking measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
τ²-Bench Telecom93.0%29.2%Kimi K2 Thinking
METR time horizons59.2%Not measuredOnly Kimi K2 Thinking measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
GPQA Diamond78.5%Not measuredOnly Kimi K2 Thinking measured
GPQA Diamond (Artificial Analysis run)83.8%57.9%Kimi K2 Thinking
Humanity's Last Exam (Artificial Analysis run)23.8%3.8%Kimi K2 Thinking
Neither model has a result on the other 19 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
AIME85.4%Not measuredOnly Kimi K2 Thinking measured
MGSM (Vals AI)90.2%Not measuredOnly Kimi K2 Thinking measured
AA Math Index94.730.7Kimi K2 Thinking
AIME 2025 (Artificial Analysis run)94.7%30.7%Kimi K2 Thinking
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
MMLU Pro81.1%Not measuredOnly Kimi K2 Thinking measured
LegalBench (Vals AI)80.2%Not measuredOnly Kimi K2 Thinking measured
CaseLaw (Vals AI)65.7%Not measuredOnly Kimi K2 Thinking measured
CorpFin (Vals AI)60.6%Not measuredOnly Kimi K2 Thinking measured
TaxEval (Vals AI)71.7%Not measuredOnly Kimi K2 Thinking measured
MedQA (Vals AI)92.6%Not measuredOnly Kimi K2 Thinking measured
MMLU Pro (Artificial Analysis run)84.8%74.9%Kimi K2 Thinking
Neither model has a result on the other 11 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
Design Arena: websites1113Not measuredOnly Kimi K2 Thinking measured
Neither model has a result on the other 8 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
IFBench68.1%39.9%Kimi K2 Thinking
Long context reasoning (AA-LCR)72.0%33.3%Kimi K2 Thinking
CL-bench17.6%Not measuredOnly Kimi K2 Thinking measured
Neither model has a result on the other 43 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Kimi K2 Thinking and Qwen3 VL 8B Instruct
BenchmarkKimi K2 ThinkingQwen3 VL 8B InstructLeader
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 Thinking$0.0019Fits in one request
Qwen3 VL 8B Instruct$0.0006Fits in one request

Qwen3 VL 8B Instruct costs less

Repository review

50K input + 3K output tokens

Kimi K2 Thinking$0.037Fits in one request
Qwen3 VL 8B Instruct$0.015Fits in one request

Qwen3 VL 8B Instruct costs less

Agent loop

200K input + 10K output tokens

Kimi K2 Thinking$0.145Fits in one request
Qwen3 VL 8B Instruct$0.058Fits in one request

Qwen3 VL 8B Instruct 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 Thinking and Qwen3 VL 8B Instruct
SpecificationMoonshotAI Kimi K2 ThinkingQwen3 VL 8B Instruct
MakerMoonshot AIQwen
WeightsOpen weightsOpen weights
ReleasedNovember 6, 2025October 14, 2025
Context window262K262K
Input $/Mtok$0.60$0.25
Output $/Mtok$2.50$0.75
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved task$0.61Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Kimi K2 Thinking better than Qwen3 VL 8B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Kimi K2 Thinking or Qwen3 VL 8B Instruct?

Kimi K2 Thinking costs $0.60 per million input tokens and $2.50 per million output tokens; Qwen3 VL 8B Instruct costs $0.25 and $0.75. Cost per solved task is known for Kimi K2 Thinking only.

Which has the bigger context window, Kimi K2 Thinking or Qwen3 VL 8B Instruct?

Neither: both take 262K tokens.

How many benchmarks is the Kimi K2 Thinking vs Qwen3 VL 8B Instruct comparison based on?

11 benchmarks have a result for both models as of October 21, 2026. Kimi K2 Thinking's coding index rests on 1 independent benchmark and Qwen3 VL 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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