ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GLM-4.7 vs Kimi K2 Thinking vs MiniMax-M2.7

MiniMax-M2.7 comes out ahead, 61 to 58 and 56 on our weighted score, and it is the cheaper option too.

  1. 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

  2. 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
  3. Our pick

    MiniMax

    MiniMax-M2.7

    Released Mar 18, 2026

    61/100
    • ECI145.9
    • Price$0.30 / $1.20
    • Context205K
    Full specs Remove MiniMax-M2.7
01 — Verdict

MiniMax-M2.7 is our pick

MiniMax-M2.7 is the better all-round choice, scoring 61/100 against Kimi K2 Thinking (58) and GLM-4.7 (56). It leads on price. Kimi K2 Thinking wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · MiniMax-M2.7 145.9 · GLM-4.7 143.5
  • Lowest priceMiniMax-M2.7MiniMax-M2.7 $0.525 · 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 · MiniMax-M2.7 204,800 tokens
  • Widest inputsSame inputsGLM-4.7: Text · Kimi K2 Thinking: Text · MiniMax-M2.7: 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 ThinkingMiniMax-M2.7
CapabilityCapabilities Index (ECI)50%707373
Price25%504863
Inputs & features15%353535
Context window10%323732
Overall100%56/10058/10061/100
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 Thinking vs MiniMax-M2.7 specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)Kimi K2 ThinkingMoonshot AIMiniMax-M2.7MiniMax
Capability
Capabilities Index (ECI)143.5146.0 (best)145.9
ECI rank#84 of 148#72 of 148 (best)#73 of 148
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$0.60$0.30 (best)
Output$2.20$2.50$1.20 (best)
Cached input$0.11—$0.06 (best)
Blended (3:1)$1.00$1.07$0.525 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 10 providersOfficial MiniMax (minimax.io) API
Limits
Context window204,800 tokens262,144 tokens (best)204,800 tokens
Max output131,072 tokens262,144 tokens (best)131,072 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpen
API model IDglm-4.7—MiniMax-M2.7
API providers201029 (best)
ReleasedDec 22, 2025Nov 6, 2025Mar 18, 2026
Knowledge cutoffApr 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
  • Kimi K2 Thinking$11.00
  • MiniMax-M2.7$5.40
04 — Questions

Which should you choose?

Which is better: GLM-4.7, Kimi K2 Thinking or MiniMax-M2.7?

MiniMax-M2.7 is the better all-round choice, scoring 61/100 against Kimi K2 Thinking (58) and GLM-4.7 (56). It leads on price. Kimi K2 Thinking wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GLM-4.7, Kimi K2 Thinking or MiniMax-M2.7?

MiniMax-M2.7 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) 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 $0.525 per million tokens for MiniMax-M2.7 versus $1.00 for GLM-4.7 (1.9× as much) and $1.07 for Kimi K2 Thinking (2× 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), MiniMax-M2.7 145.9 (#73 of 148) and GLM-4.7 143.5 (#84 of 148). The confidence ranges of the top two overlap (143.4–147.6 vs 138.2–148.0), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, Kimi K2 Thinking and MiniMax-M2.7 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. 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 204,800 for MiniMax-M2.7. Maximum output per response: GLM-4.7 up to 131,072, Kimi K2 Thinking up to 262,144, MiniMax-M2.7 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; Kimi K2 Thinking accepts text; MiniMax-M2.7 accepts text. They handle the same number of input types.

Are any of these open source?

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

Which is newer?

MiniMax-M2.7 is the newest, released Mar 18, 2026. GLM-4.7 came out Dec 22, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.7 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.

05 — Keep comparing

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