ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Kimi K2 Thinking vs GLM-4.7 vs Qwen3.5 Plus

Qwen3.5 Plus comes out ahead, 66 to 58 and 56 on our weighted score, and it is the cheaper option too.

  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. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    56/100
    • ECI143.5
    • Price$0.60 / $2.20
    • Context205K
    Full specs Remove GLM-4.7
  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 GLM-4.7 (56). It leads on price, inputs & features and context window. 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 · GLM-4.7 143.5
  • Lowest priceQwen3.5 PlusQwen3.5 Plus $0.90 · GLM-4.7 $1.00 · 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 · GLM-4.7 204,800 tokens
  • Widest inputsQwen3.5 PlusKimi K2 Thinking: Text · GLM-4.7: Text · Qwen3.5 Plus: Text, Images, Video
  • Self-hostingKimi K2 Thinking and GLM-4.7Publishes downloadable weights
How the score is built
MeasureWeightKimi K2 ThinkingGLM-4.7Qwen3.5 Plus
CapabilityCapabilities Index (ECI)50%737074
Price25%485052
Inputs & features15%353570
Context window10%373260
Overall100%58/10056/10066/100
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 GLM-4.7 vs Qwen3.5 Plus specifications side by side
SpecificationKimi K2 ThinkingMoonshot AIGLM-4.7Z.ai (Zhipu)Qwen3.5 PlusAlibaba (Qwen)
Capability
Capabilities Index (ECI)146.0143.5146.8 (best)
ECI rank#72 of 148#84 of 148#65 of 148 (best)
GPQA DiamondGraduate-level science questions84.2%83.3%84.9% (best)
OTIS Mock AIME 2024–2025Competition mathematics83.1%83.3%86.7% (best)
SimpleQA VerifiedShort factual questions—32.2% (best)25.4%
Price per million tokens
Input$0.60$0.60$0.40 (best)
Output$2.50$2.20 (best)$2.40
Cached input—$0.11—
Blended (3:1)$1.07$1.00$0.90 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 10 providersOfficial Z.AI APIOfficial Alibaba API
Limits
Context window262,144 tokens204,800 tokens1,000,000 tokens (best)
Max output262,144 tokens (best)131,072 tokens65,536 tokens
Inputs and features
TextYesYesYes
ImagesNoNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenProprietary
API model ID—glm-4.7qwen3.5-plus
API providers1020 (best)10
ReleasedNov 6, 2025Dec 22, 2025Feb 16, 2026
Knowledge cutoffAug 2024Apr 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.

  • Kimi K2 Thinking$11.00
  • GLM-4.7$10.40
  • Qwen3.5 Plus$8.80
04 — Questions

Which should you choose?

Which is better: Kimi K2 Thinking, GLM-4.7 or Qwen3.5 Plus?

Qwen3.5 Plus is the better all-round choice, scoring 66/100 against Kimi K2 Thinking (58) and GLM-4.7 (56). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2 Thinking, GLM-4.7 or Qwen3.5 Plus?

Qwen3.5 Plus is cheaper at $0.40 input / $2.40 output per million tokens (official Alibaba API price). GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI 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 $0.90 per million tokens for Qwen3.5 Plus versus $1.00 for GLM-4.7 (1.1× as much) and $1.07 for Kimi K2 Thinking (1.2× 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 GLM-4.7 143.5 (#84 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. On individual benchmarks: GPQA Diamond — Qwen3.5 Plus 84.9%, Kimi K2 Thinking 84.2%, GLM-4.7 83.3%; OTIS Mock AIME 2024–2025 — Qwen3.5 Plus 86.7%, GLM-4.7 83.3%, Kimi K2 Thinking 83.1%.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2 Thinking, GLM-4.7 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 204,800 for GLM-4.7. Maximum output per response: Kimi K2 Thinking up to 262,144, GLM-4.7 up to 131,072, Qwen3.5 Plus up to 65,536 tokens.

Which can read images, PDFs, audio or video?

Kimi K2 Thinking accepts text; GLM-4.7 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 GLM-4.7 publishes its weights and can be self-hosted; Qwen3.5 Plus is proprietary.

Which is newer?

Qwen3.5 Plus is the newest, released Feb 16, 2026. GLM-4.7 came out Dec 22, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, GLM-4.7 Apr 2025, 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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