ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GLM-4.7 vs GLM-4.6V vs Kimi K2 Thinking

GLM-4.6V comes out ahead, 59 to 42 and 42 on our weighted score, and it is the cheaper option too.

  1. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    42/100
    • ECI143.5
    • Price$0.60 / $2.20
    • Context205K
  2. Our pick

    Z.ai (Zhipu)

    GLM-4.6V

    Released Dec 8, 2025

    59/100
    • ECI—
    • Price$0.30 / $0.90
    • Context128K
  3. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    42/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
01 — Verdict

GLM-4.6V is our pick

GLM-4.6V is the better all-round choice, scoring 59/100 against Kimi K2 Thinking (42) and GLM-4.7 (42). It leads on price and inputs & features. Kimi K2 Thinking wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

  • CapabilityNot enough dataNo independent benchmark covers every model here yet
  • Lowest priceGLM-4.6VGLM-4.6V $0.45 · GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · GLM-4.7 204,800 · GLM-4.6V 128,000 tokens
  • Widest inputsGLM-4.6VGLM-4.7: Text · GLM-4.6V: Text, Images, Video · Kimi K2 Thinking: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.7GLM-4.6VKimi K2 Thinking
Price50%506648
Inputs & features30%357035
Context window20%322437
Overall100%42/10059/10042/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

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

GLM-4.7 vs GLM-4.6V vs Kimi K2 Thinking specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)GLM-4.6VZ.ai (Zhipu)Kimi K2 ThinkingMoonshot AI
Capability
Capabilities Index (ECI)143.5—146.0 (best)
ECI rank#84 of 148—#72 of 148 (best)
GPQA DiamondGraduate-level science questions83.3%—84.2% (best)
OTIS Mock AIME 2024–2025Competition mathematics83.3% (best)—83.1%
SimpleQA VerifiedShort factual questions32.2%——
Price per million tokens
Input$0.60$0.30 (best)$0.60
Output$2.20$0.90 (best)$2.50
Cached input$0.11——
Blended (3:1)$1.00$0.45 (best)$1.07
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIOfficial Z.AI APIMedian of 10 providers
Limits
Context window204,800 tokens128,000 tokens262,144 tokens (best)
Max output131,072 tokens32,768 tokens262,144 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoYesNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpen
API model IDglm-4.7glm-4.6v—
API providers20 (best)1010
ReleasedDec 22, 2025Dec 8, 2025Nov 6, 2025
Knowledge cutoffApr 2025Apr 2025Aug 2024
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.

  • GLM-4.7$10.40
  • GLM-4.6V$4.80
  • Kimi K2 Thinking$11.00
04 — Questions

Which should you choose?

Which is better: GLM-4.7, GLM-4.6V or Kimi K2 Thinking?

GLM-4.6V is the better all-round choice, scoring 59/100 against Kimi K2 Thinking (42) and GLM-4.7 (42). It leads on price and inputs & features. Kimi K2 Thinking wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, GLM-4.7, GLM-4.6V or Kimi K2 Thinking?

GLM-4.6V is cheaper at $0.30 input / $0.90 output per million tokens (official Z.AI 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 $0.45 per million tokens for GLM-4.6V versus $1.00 for GLM-4.7 (2.2× as much) and $1.07 for Kimi K2 Thinking (2.4× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.5, GLM-4.6V has not been scored yet and Kimi K2 Thinking has an ECI of 146.0.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, GLM-4.6V and Kimi K2 Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Which has the bigger context window?

Kimi K2 Thinking has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 128,000 for GLM-4.6V. Maximum output per response: GLM-4.7 up to 131,072, GLM-4.6V up to 32,768, Kimi K2 Thinking up to 262,144 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; GLM-4.6V accepts text, images and video; Kimi K2 Thinking accepts text. GLM-4.6V handles the widest range of inputs.

Are any of these open source?

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

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. GLM-4.6V came out Dec 8, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, GLM-4.6V Apr 2025, Kimi K2 Thinking Aug 2024.

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.