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Comparison · 3 models · Updated Oct 4, 2026

DeepSeek-V3.1 vs Kimi K2 Thinking vs Qwen3 14B

Too close to call on our weighted score (Kimi K2 Thinking 58, DeepSeek-V3.1 55, Qwen3 14B 54). The right pick depends on what you value most.

  1. DeepSeek

    DeepSeek-V3.1

    Released Aug 21, 2025

    55/100
    • ECI139.9
    • Price$0.385 / $1.25
    • Context131K
    Full specs Remove DeepSeek-V3.1

  2. 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

  3. Alibaba (Qwen)

    Qwen3 14B

    Released Apr 29, 2025

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

Too close to call

It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, DeepSeek-V3.1 55/100, Qwen3 14B 54/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and DeepSeek-V3.1 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 · DeepSeek-V3.1 139.9 · Qwen3 14B 138.2
  • Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · DeepSeek-V3.1 131,072 · Qwen3 14B 131,072 tokens
  • Widest inputsSame inputsDeepSeek-V3.1: Text · Kimi 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
MeasureWeightDeepSeek-V3.1Kimi K2 ThinkingQwen3 14B
CapabilityCapabilities Index (ECI)50%657363
Price25%604860
Inputs & features15%353535
Context window10%243724
Overall100%55/10058/10054/100
02 — Side by side

Every spec in one table

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

DeepSeek-V3.1 vs Kimi K2 Thinking vs Qwen3 14B specifications side by side
SpecificationDeepSeek-V3.1DeepSeekKimi K2 ThinkingMoonshot AIQwen3 14BAlibaba (Qwen)
Capability
Capabilities Index (ECI)139.9146.0 (best)138.2
ECI rank#100 of 148#72 of 148 (best)#107 of 148
GPQA DiamondGraduate-level science questions—84.2% (best)63.8%
OTIS Mock AIME 2024–2025Competition mathematics—83.1% (best)66.4%
Price per million tokens
Input$0.385$0.60$0.35 (best)
Output$1.25 (best)$2.50$1.40
Cached input———
Blended (3:1)$0.601 (best)$1.07$0.613
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 8 providersMedian of 10 providersOfficial Alibaba API
Limits
Context window131,072 tokens262,144 tokens (best)131,072 tokens
Max output8,192 tokens262,144 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenMIT LicenseOpenOpen
API model ID——qwen3-14b
API providers810 (best)1
ReleasedAug 21, 2025Nov 6, 2025Apr 29, 2025
Knowledge cutoff—Aug 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.

  • DeepSeek-V3.1$6.35
  • Kimi K2 Thinking$11.00
  • Qwen3 14B$6.30
04 — Questions

Which should you choose?

Which is better: DeepSeek-V3.1, Kimi K2 Thinking or Qwen3 14B?

It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, DeepSeek-V3.1 55/100, Qwen3 14B 54/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and DeepSeek-V3.1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, DeepSeek-V3.1, Kimi K2 Thinking or Qwen3 14B?

DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Qwen3 14B costs $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 $0.601 per million tokens for DeepSeek-V3.1 versus $0.613 for Qwen3 14B (1× as much) and $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), DeepSeek-V3.1 139.9 (#100 of 148) and Qwen3 14B 138.2 (#107 of 148). Their confidence ranges do not overlap (143.4–147.6 vs 136.1–143.3), so the gap is a real one.

Which is better for coding?

There are no published SWE-bench Verified results for DeepSeek-V3.1, 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. All three 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 DeepSeek-V3.1 and 131,072 for Qwen3 14B. Maximum output per response: DeepSeek-V3.1 up to 8,192, Kimi K2 Thinking up to 262,144, Qwen3 14B up to 8,192 tokens.

Which can read images, PDFs, audio or video?

DeepSeek-V3.1 accepts text; Kimi K2 Thinking accepts text; Qwen3 14B accepts text. They handle the same number of input types.

Are any of these open source?

Yes, all three publish their weights (MIT License), so you can self-host them.

Which is newer?

Kimi K2 Thinking is the newest, released Nov 6, 2025. DeepSeek-V3.1 came out Aug 21, 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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