ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Qwen3.6 Plus vs Kimi K2.7 Code

Too close to call on our weighted score (Qwen3.6 Plus 66, Kimi K2.7 Code 64). The right pick depends on what you value most.

  1. Alibaba (Qwen)

    Qwen3.6 Plus

    Released Apr 2, 2026

    66/100
    • ECI147.6
    • Price$0.50 / $3.00
    • Context1M
    Full specs Remove Qwen3.6 Plus

  2. Moonshot AI

    Kimi K2.7 Code

    Released Jun 12, 2026

    64/100
    • ECI150.0
    • Price$0.95 / $4.00
    • Context262K
    Full specs Remove Kimi K2.7 Code

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Too close to call

It is close. Our weighted score puts them within 1 points (Qwen3.6 Plus 66/100, Kimi K2.7 Code 64/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability, Qwen3.6 Plus on price and Qwen3.6 Plus for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2.7 CodeCapabilities Index (ECI): Kimi K2.7 Code 150.0 · Qwen3.6 Plus 147.6
  • Lowest priceQwen3.6 PlusQwen3.6 Plus $1.13 · Kimi K2.7 Code $1.71 per 1M tokens (3:1 blend)
  • Longest contextQwen3.6 PlusQwen3.6 Plus 1,000,000 · Kimi K2.7 Code 262,144 tokens
  • Widest inputsSame inputsQwen3.6 Plus: Text, Images, Video · Kimi K2.7 Code: Text, Images, Video
  • Self-hostingKimi K2.7 CodePublishes downloadable weights
How the score is built
MeasureWeightQwen3.6 PlusKimi K2.7 Code
CapabilityCapabilities Index (ECI)50%7578
Price25%4739
Inputs & features15%7080
Context window10%6037
Overall100%66/10064/100
02 — Side by side

Every spec in one table

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

Qwen3.6 Plus vs Kimi K2.7 Code specifications side by side
SpecificationQwen3.6 PlusAlibaba (Qwen)Kimi K2.7 CodeMoonshot AI
Capability
Capabilities Index (ECI)147.6150.0 (best)
ECI rank#59 of 148#49 of 148 (best)
GPQA DiamondGraduate-level science questions88.4% (best)87.9%
FrontierMath Tiers 1–3Research-level mathematics38.3%54.0% (best)
OTIS Mock AIME 2024–2025Competition mathematics93.3%95.6% (best)
SWE-bench VerifiedFixing real GitHub issues57.9%—
SimpleQA VerifiedShort factual questions44.1% (best)36.5%
Price per million tokens
Input$0.50 (best)$0.95
Output$3.00 (best)$4.00
Cached input$0.05 (best)$0.19
Blended (3:1)$1.13 (best)$1.71
Long-context rateOver 256K: $2.00 / $6.00Same rate
Price sourceOfficial Alibaba APIOfficial Moonshot AI API
Limits
Context window1,000,000 tokens (best)262,144 tokens
Max output65,536 tokens262,144 tokens (best)
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoNo
AudioNoNo
VideoYesYes
ReasoningYesYes
Tool callingYesYes
Structured outputNoYes
Availability
WeightsProprietaryOpen
API model IDqwen3.6-pluskimi-k2.7-code
API providers1851 (best)
ReleasedApr 2, 2026Jun 12, 2026
Knowledge cutoffApr 2025Jan 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.

  • Qwen3.6 Plus$11.00
  • Kimi K2.7 Code$17.50
04 — Questions

Which should you choose?

Which is better: Qwen3.6 Plus or Kimi K2.7 Code?

It is close. Our weighted score puts them within 1 points (Qwen3.6 Plus 66/100, Kimi K2.7 Code 64/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability, Qwen3.6 Plus on price and Qwen3.6 Plus for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen3.6 Plus or Kimi K2.7 Code?

Qwen3.6 Plus is cheaper at $0.50 input / $3.00 output per million tokens (official Alibaba API price). Kimi K2.7 Code costs $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $1.13 per million tokens for Qwen3.6 Plus versus $1.71 for Kimi K2.7 Code (1.5× as much).

Which scores higher on benchmarks?

Kimi K2.7 Code scores higher on the Capabilities Index (ECI): Kimi K2.7 Code 150.0 (#49 of 148) and Qwen3.6 Plus 147.6 (#59 of 148). The confidence ranges of the top two overlap (148.1–151.8 vs 145.4–149.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.6 Plus 88.4%, Kimi K2.7 Code 87.9%; FrontierMath Tiers 1–3 — Kimi K2.7 Code 54.0%, Qwen3.6 Plus 38.3%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, Qwen3.6 Plus 93.3%; SimpleQA Verified — Qwen3.6 Plus 44.1%, Kimi K2.7 Code 36.5%.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2.7 Code yet, so there is no like-for-like coding score. On overall capability, Kimi K2.7 Code 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?

Qwen3.6 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.7 Code. Maximum output per response: Qwen3.6 Plus up to 65,536, Kimi K2.7 Code up to 262,144 tokens.

Which can read images, PDFs, audio or video?

Qwen3.6 Plus accepts text, images and video; Kimi K2.7 Code accepts text, images and video. They handle the same number of input types.

Are any of these open source?

Kimi K2.7 Code publishes its weights and can be self-hosted; Qwen3.6 Plus is proprietary.

Which is newer?

Kimi K2.7 Code is the newest, released Jun 12, 2026. Qwen3.6 Plus came out Apr 2, 2026. Knowledge cutoff: Qwen3.6 Plus Apr 2025, Kimi K2.7 Code Jan 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

Related comparisons