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Comparison · 2 models · Updated Oct 4, 2026

Qwen3.5 397B-A17B vs Kimi K2.7 Code

Too close to call on our weighted score (Qwen3.5 397B-A17B 65, Kimi K2.7 Code 64). The right pick depends on what you value most.

  1. Alibaba (Qwen)

    Qwen3.5 397B-A17B

    Released Feb 15, 2026

    65/100
    • ECI146.7
    • Price$0.60 / $3.60
    • Context262K
    Full specs Remove Qwen3.5 397B-A17B

  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 a point (Qwen3.5 397B-A17B 65/100, Kimi K2.7 Code 64/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability and Qwen3.5 397B-A17B on price. 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 · Qwen3.5 397B-A17B 146.7
  • Lowest priceQwen3.5 397B-A17BQwen3.5 397B-A17B $1.35 · Kimi K2.7 Code $1.71 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameQwen3.5 397B-A17B 262,144 · Kimi K2.7 Code 262,144 tokens
  • Widest inputsQwen3.5 397B-A17BQwen3.5 397B-A17B: Text, Images, Audio, Video · Kimi K2.7 Code: Text, Images, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightQwen3.5 397B-A17BKimi K2.7 Code
CapabilityCapabilities Index (ECI)50%7478
Price25%4439
Inputs & features15%9080
Context window10%3737
Overall100%65/10064/100
02 — Side by side

Every spec in one table

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

Qwen3.5 397B-A17B vs Kimi K2.7 Code specifications side by side
SpecificationQwen3.5 397B-A17BAlibaba (Qwen)Kimi K2.7 CodeMoonshot AI
Capability
Capabilities Index (ECI)146.7150.0 (best)
ECI rank#67 of 148#49 of 148 (best)
GPQA DiamondGraduate-level science questions86.4%87.9% (best)
FrontierMath Tiers 1–3Research-level mathematics31.2%54.0% (best)
OTIS Mock AIME 2024–2025Competition mathematics88.9%95.6% (best)
SimpleQA VerifiedShort factual questions—36.5%
Price per million tokens
Input$0.60 (best)$0.95
Output$3.60 (best)$4.00
Cached input—$0.19
Blended (3:1)$1.35 (best)$1.71
Long-context rateSame rateSame rate
Price sourceOfficial Alibaba APIOfficial Moonshot AI API
Limits
Context window262,144 tokens262,144 tokens
Max output65,536 tokens262,144 tokens (best)
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoNo
AudioYesNo
VideoYesYes
ReasoningYesYes
Tool callingYesYes
Structured outputYesYes
Availability
WeightsOpenOpen
API model IDqwen3.5-397b-a17bkimi-k2.7-code
API providers2351 (best)
ReleasedFeb 15, 2026Jun 12, 2026
Knowledge cutoff—Jan 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.

  • Qwen3.5 397B-A17B$13.20
  • Kimi K2.7 Code$17.50
04 — Questions

Which should you choose?

Which is better: Qwen3.5 397B-A17B or Kimi K2.7 Code?

It is close. Our weighted score puts them within a point (Qwen3.5 397B-A17B 65/100, Kimi K2.7 Code 64/100), so choose by what matters most for your work: Kimi K2.7 Code for raw capability and Qwen3.5 397B-A17B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen3.5 397B-A17B or Kimi K2.7 Code?

Qwen3.5 397B-A17B is cheaper at $0.60 input / $3.60 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 $1.35 per million tokens for Qwen3.5 397B-A17B versus $1.71 for Kimi K2.7 Code (1.3× 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 Qwen3.5 397B-A17B 146.7 (#67 of 148). The confidence ranges of the top two overlap (148.1–151.8 vs 144.8–148.2), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2.7 Code 87.9%, Qwen3.5 397B-A17B 86.4%; FrontierMath Tiers 1–3 — Kimi K2.7 Code 54.0%, Qwen3.5 397B-A17B 31.2%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, Qwen3.5 397B-A17B 88.9%.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3.5 397B-A17B and 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?

Qwen3.5 397B-A17B and Kimi K2.7 Code share the same 262,144-token context window. Maximum output per response: Qwen3.5 397B-A17B up to 65,536, Kimi K2.7 Code up to 262,144 tokens.

Which can read images, PDFs, audio or video?

Qwen3.5 397B-A17B accepts text, images, audio and video; Kimi K2.7 Code accepts text, images and video. Qwen3.5 397B-A17B handles the widest range of inputs.

Are any of these open source?

Yes, both publish their weights, so you can self-host them.

Which is newer?

Kimi K2.7 Code is the newest, released Jun 12, 2026. Qwen3.5 397B-A17B came out Feb 15, 2026. Knowledge cutoff: 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

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