ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Kimi K2.5 vs DeepSeek R1 0528 vs Qwen3.6 Plus

Qwen3.6 Plus comes out ahead, 66 to 62 and 53 on our weighted score, though DeepSeek R1 0528 is 17% cheaper per token.

  1. Moonshot AI

    Kimi K2.5

    Released Jan 27, 2026

    62/100
    • ECI148.1
    • Price$0.60 / $3.00
    • Context262K
  2. DeepSeek

    DeepSeek R1 0528

    Released May 28, 2025

    53/100
    • ECI141.2
    • Price$0.512 / $2.19
    • Context164K
  3. Our pick

    Alibaba (Qwen)

    Qwen3.6 Plus

    Released Apr 2, 2026

    66/100
    • ECI147.8
    • Price$0.50 / $3.00
    • Context1M
01 — Verdict

Qwen3.6 Plus is our pick

Qwen3.6 Plus is the better all-round choice, scoring 66/100 against Kimi K2.5 (62) and DeepSeek R1 0528 (53). It leads on inputs & features and context window. DeepSeek R1 0528 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2.5Capabilities Index (ECI): Kimi K2.5 148.1 · Qwen3.6 Plus 147.8 · DeepSeek R1 0528 141.2
  • Lowest priceDeepSeek R1 0528DeepSeek R1 0528 $0.932 · Qwen3.6 Plus $1.13 · Kimi K2.5 $1.20 per 1M tokens (3:1 blend)
  • Longest contextQwen3.6 PlusQwen3.6 Plus 1,000,000 · Kimi K2.5 262,144 · DeepSeek R1 0528 163,840 tokens
  • Widest inputsKimi K2.5 and Qwen3.6 PlusKimi K2.5: Text, Images, Video · DeepSeek R1 0528: Text · Qwen3.6 Plus: Text, Images, Video
  • Self-hostingKimi K2.5 and DeepSeek R1 0528Publishes downloadable weights (MIT)
How the score is built
MeasureWeightKimi K2.5DeepSeek R1 0528Qwen3.6 Plus
CapabilityCapabilities Index (ECI)50%746674
Price25%444945
Inputs & features15%703580
Context window10%372860
Overall100%62/10053/10066/100
02 — Side by side

Every spec in one table

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

Kimi K2.5 vs DeepSeek R1 0528 vs Qwen3.6 Plus specifications side by side
SpecificationKimi K2.5Moonshot AIDeepSeek R1 0528DeepSeekQwen3.6 PlusAlibaba (Qwen)
Capability
Capabilities Index (ECI)148.1 (best)141.2147.8
ECI rank#68 of 165 (best)#108 of 165#69 of 165
GPQA DiamondGraduate-level science questions87.6%76.3%88.4% (best)
FrontierMath Tiers 1–3Research-level mathematics——38.3%
OTIS Mock AIME 2024–2025Competition mathematics92.2%66.4%93.3% (best)
SWE-bench VerifiedFixing real GitHub issues73.8% (best)—57.9%
SimpleQA VerifiedShort factual questions34.3%—44.1% (best)
Price per million tokens
Input$0.60$0.512$0.50 (best)
Output$3.00$2.19 (best)$3.00
Cached input——$0.05
Blended (3:1)$1.20$0.932 (best)$1.13
Long-context rateSame rateSame rateOver 256K: $2.00 / $6.00
Price sourceMedian of 33 providersMedian of 16 providersOfficial Alibaba API
Limits
Context window262,144 tokens163,840 tokens1,000,000 tokens (best)
Max output262,144 tokens (best)32,768 tokens65,536 tokens
Inputs and features
TextYesYesYes
ImagesYesNoYes
PDFsNoNoNo
AudioNoNoNo
VideoYesNoYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoYes
Availability
WeightsOpenOpenMITProprietary
API model ID——qwen3.6-plus
API providers33 (best)1622
ReleasedJan 27, 2026May 28, 2025Apr 2, 2026
Knowledge cutoffJan 2025—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 K2.5$12.00
  • DeepSeek R1 0528$9.50
  • Qwen3.6 Plus$11.00
04 — Questions

Which should you choose?

Which is better: Kimi K2.5, DeepSeek R1 0528 or Qwen3.6 Plus?

Qwen3.6 Plus is the better all-round choice, scoring 66/100 against Kimi K2.5 (62) and DeepSeek R1 0528 (53). It leads on inputs & features and context window. DeepSeek R1 0528 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2.5, DeepSeek R1 0528 or Qwen3.6 Plus?

DeepSeek R1 0528 is cheaper at $0.512 input / $2.19 output per million tokens (median across 16 API providers). Qwen3.6 Plus costs $0.50 input / $3.00 output per million tokens (official Alibaba API price); Kimi K2.5 costs $0.60 input / $3.00 output per million tokens (median across 33 API providers). At a typical mix of three input tokens to one output token, that is $0.932 per million tokens for DeepSeek R1 0528 versus $1.13 for Qwen3.6 Plus (1.2× as much) and $1.20 for Kimi K2.5 (1.3× as much).

Which scores higher on benchmarks?

Kimi K2.5 scores higher on the Capabilities Index (ECI): Kimi K2.5 148.1 (#68 of 165), Qwen3.6 Plus 147.8 (#69 of 165) and DeepSeek R1 0528 141.2 (#108 of 165). The confidence ranges of the top two overlap (146.3–149.5 vs 145.8–149.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.6 Plus 88.4%, Kimi K2.5 87.6%, DeepSeek R1 0528 76.3%; OTIS Mock AIME 2024–2025 — Qwen3.6 Plus 93.3%, Kimi K2.5 92.2%, DeepSeek R1 0528 66.4%.

Which is better for coding?

There are no published SWE-bench Verified results for DeepSeek R1 0528 yet, so there is no like-for-like coding score. On overall capability, Kimi K2.5 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?

Qwen3.6 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.5 and 163,840 for DeepSeek R1 0528. Maximum output per response: Kimi K2.5 up to 262,144, DeepSeek R1 0528 up to 32,768, Qwen3.6 Plus up to 65,536 tokens.

Which can read images, PDFs, audio or video?

Kimi K2.5 accepts text, images and video; DeepSeek R1 0528 accepts text; Qwen3.6 Plus accepts text, images and video. Kimi K2.5 handles the widest range of inputs.

Are any of these open source?

Kimi K2.5 and DeepSeek R1 0528 publishes its weights (MIT) and can be self-hosted; Qwen3.6 Plus is proprietary.

Which is newer?

Qwen3.6 Plus is the newest, released Apr 2, 2026. Kimi K2.5 came out Jan 27, 2026; DeepSeek R1 0528 came out May 28, 2025. Knowledge cutoff: Kimi K2.5 Jan 2025, Qwen3.6 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 Nov 2, 2026.