ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GLM-4.7 vs GPT-5.4 nano vs Kimi K2 Thinking

GPT-5.4 nano comes out ahead, 68 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
  2. Our pick

    OpenAI

    GPT-5.4 nano

    Released Mar 17, 2026

    68/100
    • ECI145.8
    • Price$0.20 / $1.25
    • Context400K
  3. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    58/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
01 — Verdict

GPT-5.4 nano is our pick

GPT-5.4 nano is the better all-round choice, scoring 68/100 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%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · GPT-5.4 nano 145.8 · GLM-4.7 143.5
  • Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Kimi K2 Thinking 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsGPT-5.4 nanoGLM-4.7: Text · GPT-5.4 nano: Text, Images · Kimi K2 Thinking: Text
  • Self-hostingGLM-4.7 and Kimi K2 ThinkingPublishes downloadable weights
How the score is built
MeasureWeightGLM-4.7GPT-5.4 nanoKimi K2 Thinking
CapabilityCapabilities Index (ECI)50%707373
Price25%506648
Inputs & features15%357035
Context window10%324437
Overall100%56/10068/10058/100
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 GPT-5.4 nano vs Kimi K2 Thinking specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)GPT-5.4 nanoOpenAIKimi K2 ThinkingMoonshot AI
Capability
Capabilities Index (ECI)143.5145.8146.0 (best)
ECI rank#84 of 148#75 of 148#72 of 148 (best)
GPQA DiamondGraduate-level science questions83.3%78.5%84.2% (best)
FrontierMath Tiers 1–3Research-level mathematics—44.9%—
OTIS Mock AIME 2024–2025Competition mathematics83.3%87.8% (best)83.1%
SimpleQA VerifiedShort factual questions32.2% (best)11.7%—
Price per million tokens
Input$0.60$0.20 (best)$0.60
Output$2.20$1.25 (best)$2.50
Cached input$0.11$0.02 (best)—
Blended (3:1)$1.00$0.463 (best)$1.07
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIOfficial OpenAI APIMedian of 10 providers
Limits
Context window204,800 tokens400,000 tokens (best)262,144 tokens
Max output131,072 tokens128,000 tokens262,144 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYeslow · medium · high · xhighYes
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenProprietaryOpen
API model IDglm-4.7gpt-5.4-nano—
API providers2026 (best)10
ReleasedDec 22, 2025Mar 17, 2026Nov 6, 2025
Knowledge cutoffApr 2025Aug 31, 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
  • GPT-5.4 nano$4.50
  • Kimi K2 Thinking$11.00
04 — Questions

Which should you choose?

Which is better: GLM-4.7, GPT-5.4 nano or Kimi K2 Thinking?

GPT-5.4 nano is the better all-round choice, scoring 68/100 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, GLM-4.7, GPT-5.4 nano or Kimi K2 Thinking?

GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI 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 $0.463 per million tokens for GPT-5.4 nano versus $1.00 for GLM-4.7 (2.2× as much) and $1.07 for Kimi K2 Thinking (2.3× 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), GPT-5.4 nano 145.8 (#75 of 148) and GLM-4.7 143.5 (#84 of 148). The confidence ranges of the top two overlap (143.4–147.6 vs 143.2–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, GLM-4.7 83.3%, GPT-5.4 nano 78.5%; OTIS Mock AIME 2024–2025 — GPT-5.4 nano 87.8%, GLM-4.7 83.3%, Kimi K2 Thinking 83.1%.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, GPT-5.4 nano and Kimi K2 Thinking 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?

GPT-5.4 nano has the largest context window at 400,000 tokens, against 262,144 for Kimi K2 Thinking and 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, GPT-5.4 nano up to 128,000, Kimi K2 Thinking up to 262,144 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; GPT-5.4 nano accepts text and images; Kimi K2 Thinking accepts text. GPT-5.4 nano handles the widest range of inputs.

Are any of these open source?

GLM-4.7 and Kimi K2 Thinking publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

Which is newer?

GPT-5.4 nano is the newest, released Mar 17, 2026. GLM-4.7 came out Dec 22, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, GPT-5.4 nano Aug 31, 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.