ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Kimi K2 Thinking vs GLM-4.7

Too close to call on our weighted score (Kimi K2 Thinking 58, GLM-4.7 56). The right pick depends on what you value most.

  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. Add a model

    Make it a three-way comparison.

01 — Verdict

Too close to call

It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, GLM-4.7 56/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and GLM-4.7 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · GLM-4.7 143.5
  • Lowest priceGLM-4.7GLM-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 tokens
  • Widest inputsSame inputsKimi K2 Thinking: Text · GLM-4.7: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightKimi K2 ThinkingGLM-4.7
CapabilityCapabilities Index (ECI)50%7370
Price25%4850
Inputs & features15%3535
Context window10%3732
Overall100%58/10056/100
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 specifications side by side
SpecificationKimi K2 ThinkingMoonshot AIGLM-4.7Z.ai (Zhipu)
Capability
Capabilities Index (ECI)146.0 (best)143.5
ECI rank#72 of 148 (best)#84 of 148
GPQA DiamondGraduate-level science questions84.2% (best)83.3%
OTIS Mock AIME 2024–2025Competition mathematics83.1%83.3% (best)
SimpleQA VerifiedShort factual questions—32.2%
Price per million tokens
Input$0.60$0.60
Output$2.50$2.20 (best)
Cached input—$0.11
Blended (3:1)$1.07$1.00 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 10 providersOfficial Z.AI API
Limits
Context window262,144 tokens (best)204,800 tokens
Max output262,144 tokens (best)131,072 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesYes
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model ID—glm-4.7
API providers1020 (best)
ReleasedNov 6, 2025Dec 22, 2025
Knowledge cutoffAug 2024Apr 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
04 — Questions

Which should you choose?

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

It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, GLM-4.7 56/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and GLM-4.7 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

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

GLM-4.7 is cheaper at $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 $1.00 per million tokens for GLM-4.7 versus $1.07 for Kimi K2 Thinking (1.1× as much).

Which scores higher on benchmarks?

Kimi K2 Thinking scores higher on the Capabilities Index (ECI): 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 (143.4–147.6 vs 141.3–145.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, GLM-4.7 83.3%; OTIS Mock AIME 2024–2025 — 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 and GLM-4.7 yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Thinking 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?

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

Which can read images, PDFs, audio or video?

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

Are any of these open source?

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

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, 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 Oct 4, 2026.

05 — Keep comparing

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