ZMIME
Comparison · 3 models · Updated Nov 2, 2026

DeepSeek R1 0528 vs Kimi K2.5 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. DeepSeek

    DeepSeek R1 0528

    Released May 28, 2025

    53/100
    • ECI141.2
    • Price$0.512 / $2.19
    • Context164K
  2. Moonshot AI

    Kimi K2.5

    Released Jan 27, 2026

    62/100
    • ECI148.1
    • Price$0.60 / $3.00
    • Context262K
  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 PlusDeepSeek R1 0528: Text · Kimi K2.5: Text, Images, Video · Qwen3.6 Plus: Text, Images, Video
  • Self-hostingDeepSeek R1 0528 and Kimi K2.5Publishes downloadable weights (MIT)
How the score is built
MeasureWeightDeepSeek R1 0528Kimi K2.5Qwen3.6 Plus
CapabilityCapabilities Index (ECI)50%667474
Price25%494445
Inputs & features15%357080
Context window10%283760
Overall100%53/10062/10066/100
02 — Side by side

Every spec in one table

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

DeepSeek R1 0528 vs Kimi K2.5 vs Qwen3.6 Plus specifications side by side
SpecificationDeepSeek R1 0528DeepSeekKimi K2.5Moonshot AIQwen3.6 PlusAlibaba (Qwen)
Capability
Capabilities Index (ECI)141.2148.1 (best)147.8
ECI rank#108 of 165#68 of 165 (best)#69 of 165
GPQA DiamondGraduate-level science questions76.3%87.6%88.4% (best)
FrontierMath Tiers 1–3Research-level mathematics——38.3%
OTIS Mock AIME 2024–2025Competition mathematics66.4%92.2%93.3% (best)
SWE-bench VerifiedFixing real GitHub issues—73.8% (best)57.9%
SimpleQA VerifiedShort factual questions—34.3%44.1% (best)
Price per million tokens
Input$0.512$0.60$0.50 (best)
Output$2.19 (best)$3.00$3.00
Cached input——$0.05
Blended (3:1)$0.932 (best)$1.20$1.13
Long-context rateSame rateSame rateOver 256K: $2.00 / $6.00
Price sourceMedian of 16 providersMedian of 33 providersOfficial Alibaba API
Limits
Context window163,840 tokens262,144 tokens1,000,000 tokens (best)
Max output32,768 tokens262,144 tokens (best)65,536 tokens
Inputs and features
TextYesYesYes
ImagesNoYesYes
PDFsNoNoNo
AudioNoNoNo
VideoNoYesYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoYes
Availability
WeightsOpenMITOpenProprietary
API model ID——qwen3.6-plus
API providers1633 (best)22
ReleasedMay 28, 2025Jan 27, 2026Apr 2, 2026
Knowledge cutoff—Jan 2025Apr 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 R1 0528$9.50
  • Kimi K2.5$12.00
  • Qwen3.6 Plus$11.00
04 — Questions

Which should you choose?

Which is better: DeepSeek R1 0528, Kimi K2.5 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, DeepSeek R1 0528, Kimi K2.5 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: DeepSeek R1 0528 up to 32,768, Kimi K2.5 up to 262,144, Qwen3.6 Plus up to 65,536 tokens.

Which can read images, PDFs, audio or video?

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

Are any of these open source?

DeepSeek R1 0528 and Kimi K2.5 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.