ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Kimi K2 Thinking vs Qwen3 14B

Kimi K2 Thinking comes out ahead, 58 to 54 on our weighted score, though Qwen3 14B is 43% cheaper per token.

  1. Our pick

    Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    58/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
    Full specs Remove Kimi K2 Thinking

  2. Alibaba (Qwen)

    Qwen3 14B

    Released Apr 29, 2025

    54/100
    • ECI138.2
    • Price$0.35 / $1.40
    • Context131K
    Full specs Remove Qwen3 14B

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Kimi K2 Thinking is our pick

Kimi K2 Thinking is the better all-round choice, scoring 58/100 against Qwen3 14B (54). It leads on capability and context window. Qwen3 14B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · Qwen3 14B 138.2
  • Lowest priceQwen3 14BQwen3 14B $0.613 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · Qwen3 14B 131,072 tokens
  • Widest inputsSame inputsKimi K2 Thinking: Text · Qwen3 14B: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightKimi K2 ThinkingQwen3 14B
CapabilityCapabilities Index (ECI)50%7363
Price25%4860
Inputs & features15%3535
Context window10%3724
Overall100%58/10054/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Kimi K2 Thinking vs Qwen3 14B specifications side by side
SpecificationKimi K2 ThinkingMoonshot AIQwen3 14BAlibaba (Qwen)
Capability
Capabilities Index (ECI)146.0 (best)138.2
ECI rank#72 of 148 (best)#107 of 148
GPQA DiamondGraduate-level science questions84.2% (best)63.8%
OTIS Mock AIME 2024–2025Competition mathematics83.1% (best)66.4%
Price per million tokens
Input$0.60$0.35 (best)
Output$2.50$1.40 (best)
Cached input——
Blended (3:1)$1.07$0.613 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 10 providersOfficial Alibaba API
Limits
Context window262,144 tokens (best)131,072 tokens
Max output262,144 tokens (best)8,192 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesYes
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model ID—qwen3-14b
API providers10 (best)1
ReleasedNov 6, 2025Apr 29, 2025
Knowledge cutoffAug 2024Apr 2025
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Kimi K2 Thinking$11.00
  • Qwen3 14B$6.30
04 — Questions

Which should you choose?

Which is better: Kimi K2 Thinking or Qwen3 14B?

Kimi K2 Thinking is the better all-round choice, scoring 58/100 against Qwen3 14B (54). It leads on capability and context window. Qwen3 14B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2 Thinking or Qwen3 14B?

Qwen3 14B is cheaper at $0.35 input / $1.40 output per million tokens (official Alibaba API price). Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.613 per million tokens for Qwen3 14B versus $1.07 for Kimi K2 Thinking (1.8× as much).

Which scores higher on benchmarks?

Kimi K2 Thinking scores higher on the Capabilities Index (ECI): Kimi K2 Thinking 146.0 (#72 of 148) and Qwen3 14B 138.2 (#107 of 148). Their confidence ranges do not overlap (143.4–147.6 vs 133.5–140.1), so the gap is a real one. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, Qwen3 14B 63.8%; OTIS Mock AIME 2024–2025 — Kimi K2 Thinking 83.1%, Qwen3 14B 66.4%.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2 Thinking and Qwen3 14B yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Thinking leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Which has the bigger context window?

Kimi K2 Thinking has the largest context window at 262,144 tokens, against 131,072 for Qwen3 14B. Maximum output per response: Kimi K2 Thinking up to 262,144, Qwen3 14B up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Kimi K2 Thinking accepts text; Qwen3 14B accepts text. They handle the same number of input types.

Are any of these open source?

Yes, both publish their weights, so you can self-host them.

Which is newer?

Kimi K2 Thinking is the newest, released Nov 6, 2025. Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, Qwen3 14B Apr 2025.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.

05 — Keep comparing

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