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Comparison · 3 models · Updated Oct 4, 2026

Kimi K2 Thinking vs DeepSeek-V3.1 vs Qwen3.5 Plus

Qwen3.5 Plus comes out ahead, 66 to 58 and 55 on our weighted score, though DeepSeek-V3.1 is 33% cheaper per token.

  1. 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. DeepSeek

    DeepSeek-V3.1

    Released Aug 21, 2025

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

    Alibaba (Qwen)

    Qwen3.5 Plus

    Released Feb 16, 2026

    66/100
    • ECI146.8
    • Price$0.40 / $2.40
    • Context1M
    Full specs Remove Qwen3.5 Plus
01 — Verdict

Qwen3.5 Plus is our pick

Qwen3.5 Plus is the better all-round choice, scoring 66/100 against Kimi K2 Thinking (58) and DeepSeek-V3.1 (55). It leads on inputs & features and context window. DeepSeek-V3.1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen3.5 PlusCapabilities Index (ECI): Qwen3.5 Plus 146.8 · Kimi K2 Thinking 146.0 · DeepSeek-V3.1 139.9
  • Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Qwen3.5 Plus $0.90 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextQwen3.5 PlusQwen3.5 Plus 1,000,000 · Kimi K2 Thinking 262,144 · DeepSeek-V3.1 131,072 tokens
  • Widest inputsQwen3.5 PlusKimi K2 Thinking: Text · DeepSeek-V3.1: Text · Qwen3.5 Plus: Text, Images, Video
  • Self-hostingKimi K2 Thinking and DeepSeek-V3.1Publishes downloadable weights (MIT License)
How the score is built
MeasureWeightKimi K2 ThinkingDeepSeek-V3.1Qwen3.5 Plus
CapabilityCapabilities Index (ECI)50%736574
Price25%486052
Inputs & features15%353570
Context window10%372460
Overall100%58/10055/10066/100
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 DeepSeek-V3.1 vs Qwen3.5 Plus specifications side by side
SpecificationKimi K2 ThinkingMoonshot AIDeepSeek-V3.1DeepSeekQwen3.5 PlusAlibaba (Qwen)
Capability
Capabilities Index (ECI)146.0139.9146.8 (best)
ECI rank#72 of 148#100 of 148#65 of 148 (best)
GPQA DiamondGraduate-level science questions84.2%—84.9% (best)
OTIS Mock AIME 2024–2025Competition mathematics83.1%—86.7% (best)
SimpleQA VerifiedShort factual questions——25.4%
Price per million tokens
Input$0.60$0.385 (best)$0.40
Output$2.50$1.25 (best)$2.40
Cached input———
Blended (3:1)$1.07$0.601 (best)$0.90
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 10 providersMedian of 8 providersOfficial Alibaba API
Limits
Context window262,144 tokens131,072 tokens1,000,000 tokens (best)
Max output262,144 tokens (best)8,192 tokens65,536 tokens
Inputs and features
TextYesYesYes
ImagesNoNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenMIT LicenseProprietary
API model ID——qwen3.5-plus
API providers10 (best)810 (best)
ReleasedNov 6, 2025Aug 21, 2025Feb 16, 2026
Knowledge cutoffAug 2024—Apr 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
  • DeepSeek-V3.1$6.35
  • Qwen3.5 Plus$8.80
04 — Questions

Which should you choose?

Which is better: Kimi K2 Thinking, DeepSeek-V3.1 or Qwen3.5 Plus?

Qwen3.5 Plus is the better all-round choice, scoring 66/100 against Kimi K2 Thinking (58) and DeepSeek-V3.1 (55). It leads on inputs & features and context window. DeepSeek-V3.1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2 Thinking, DeepSeek-V3.1 or Qwen3.5 Plus?

DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Qwen3.5 Plus costs $0.40 input / $2.40 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 $0.90 for Qwen3.5 Plus (1.5× as much) and $1.07 for Kimi K2 Thinking (1.8× as much).

Which scores higher on benchmarks?

Qwen3.5 Plus scores higher on the Capabilities Index (ECI): Qwen3.5 Plus 146.8 (#65 of 148), Kimi K2 Thinking 146.0 (#72 of 148) and DeepSeek-V3.1 139.9 (#100 of 148). The confidence ranges of the top two overlap (144.6–148.1 vs 143.4–147.6), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2 Thinking, DeepSeek-V3.1 and Qwen3.5 Plus yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 Plus 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?

Qwen3.5 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2 Thinking and 131,072 for DeepSeek-V3.1. Maximum output per response: Kimi K2 Thinking up to 262,144, DeepSeek-V3.1 up to 8,192, Qwen3.5 Plus up to 65,536 tokens.

Which can read images, PDFs, audio or video?

Kimi K2 Thinking accepts text; DeepSeek-V3.1 accepts text; Qwen3.5 Plus accepts text, images and video. Qwen3.5 Plus handles the widest range of inputs.

Are any of these open source?

Kimi K2 Thinking and DeepSeek-V3.1 publishes its weights (MIT License) and can be self-hosted; Qwen3.5 Plus is proprietary.

Which is newer?

Qwen3.5 Plus is the newest, released Feb 16, 2026. Kimi K2 Thinking came out Nov 6, 2025; DeepSeek-V3.1 came out Aug 21, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, Qwen3.5 Plus 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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