ZMIME
Comparison · 2 models · Updated Nov 2, 2026

GLM-4.7 vs Kimi K2 Instruct

GLM-4.7 comes out ahead, 55 to 50 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    55/100
    • ECI143.7
    • Price$0.60 / $2.20
    • Context205K
    Full specs Remove GLM-4.7

  2. Moonshot AI

    Kimi K2 Instruct

    Released Jul 11, 2025

    50/100
    • ECI139.9
    • Price$0.57 / $2.30
    • Context131K
    Full specs Remove Kimi K2 Instruct

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

GLM-4.7 is our pick

GLM-4.7 is the better all-round choice, scoring 55/100 against Kimi K2 Instruct (50). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGLM-4.7Capabilities Index (ECI): GLM-4.7 143.7 · Kimi K2 Instruct 139.9
  • Lowest priceGLM-4.7GLM-4.7 $1.00 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
  • Longest contextGLM-4.7GLM-4.7 204,800 · Kimi K2 Instruct 131,072 tokens
  • Widest inputsSame inputsGLM-4.7: Text · Kimi K2 Instruct: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.7Kimi K2 Instruct
CapabilityCapabilities Index (ECI)50%6964
Price25%4848
Inputs & features15%3525
Context window10%3225
Overall100%55/10050/100
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 Kimi K2 Instruct specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)Kimi K2 InstructMoonshot AI
Capability
Capabilities Index (ECI)143.7 (best)139.9
ECI rank#94 of 165 (best)#112 of 165
GPQA DiamondGraduate-level science questions83.3%—
OTIS Mock AIME 2024–2025Competition mathematics83.3%—
SimpleQA VerifiedShort factual questions32.2%—
Price per million tokens
Input$0.60$0.57 (best)
Output$2.20 (best)$2.30
Cached input$0.11—
Blended (3:1)$1.00 (best)$1.00
Long-context rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 11 providers
Limits
Context window204,800 tokens (best)131,072 tokens
Max output131,072 tokens (best)32,768 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpenModified MIT
API model IDglm-4.7—
API providers31 (best)11
ReleasedDec 22, 2025Jul 11, 2025
Knowledge cutoffApr 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.

  • GLM-4.7$10.40
  • Kimi K2 Instruct$10.30
04 — Questions

Which should you choose?

Which is better: GLM-4.7 or Kimi K2 Instruct?

GLM-4.7 is the better all-round choice, scoring 55/100 against Kimi K2 Instruct (50). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GLM-4.7 or Kimi K2 Instruct?

GLM-4.7 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). Kimi K2 Instruct costs $0.57 input / $2.30 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.7 versus $1.00 for Kimi K2 Instruct (1× as much).

Which scores higher on benchmarks?

GLM-4.7 scores higher on the Capabilities Index (ECI): GLM-4.7 143.7 (#94 of 165) and Kimi K2 Instruct 139.9 (#112 of 165). The confidence ranges of the top two overlap (141.4–145.8 vs 136.3–141.6), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7 and Kimi K2 Instruct yet, so there is no like-for-like coding score. On overall capability, GLM-4.7 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?

GLM-4.7 has the largest context window at 204,800 tokens, against 131,072 for Kimi K2 Instruct. Maximum output per response: GLM-4.7 up to 131,072, Kimi K2 Instruct up to 32,768 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; Kimi K2 Instruct accepts text. They handle the same number of input types.

Are any of these open source?

Yes, both publish their weights (Modified MIT), so you can self-host them.

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. Kimi K2 Instruct came out Jul 11, 2025. Knowledge cutoff: GLM-4.7 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.

05 — Keep comparing

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