ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GLM-4.7 vs Kimi K2 Thinking vs Qwen3 Coder Next

Qwen3 Coder Next comes out ahead, 51 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
    Full specs Remove GLM-4.7

  2. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    42/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
    Full specs Remove Kimi K2 Thinking
  3. Our pick

    Alibaba (Qwen)

    Qwen3 Coder Next

    Released Feb 3, 2026

    51/100
    • ECI—
    • Price$0.20 / $1.20
    • Context262K
    Full specs Remove Qwen3 Coder Next
01 — Verdict

Qwen3 Coder Next is our pick

Qwen3 Coder Next is the better all-round choice, scoring 51/100 against Kimi K2 Thinking (42) and GLM-4.7 (42). It leads on price. 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 priceQwen3 Coder NextQwen3 Coder Next $0.45 · GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextKimi K2 Thinking and Qwen3 Coder NextKimi K2 Thinking 262,144 · Qwen3 Coder Next 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsSame inputsGLM-4.7: Text · Kimi K2 Thinking: Text · Qwen3 Coder Next: 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 ThinkingQwen3 Coder Next
Price50%504866
Inputs & features30%353535
Context window20%323737
Overall100%42/10042/10051/100

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 Kimi K2 Thinking vs Qwen3 Coder Next specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)Kimi K2 ThinkingMoonshot AIQwen3 Coder NextAlibaba (Qwen)
Capability
Capabilities Index (ECI)143.5146.0 (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$0.60$0.20 (best)
Output$2.20$2.50$1.20 (best)
Cached input$0.11——
Blended (3:1)$1.00$1.07$0.45 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 10 providersMedian of 11 providers
Limits
Context window204,800 tokens262,144 tokens (best)262,144 tokens (best)
Max output131,072 tokens262,144 tokens (best)65,536 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesNo
Tool callingYesYesYes
Structured outputNoNoYes
Availability
WeightsOpenOpenOpen
API model IDglm-4.7——
API providers20 (best)1011
ReleasedDec 22, 2025Nov 6, 2025Feb 3, 2026
Knowledge cutoffApr 2025Aug 2024Sep 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 Thinking$11.00
  • Qwen3 Coder Next$4.40
04 — Questions

Which should you choose?

Which is better: GLM-4.7, Kimi K2 Thinking or Qwen3 Coder Next?

Qwen3 Coder Next is the better all-round choice, scoring 51/100 against Kimi K2 Thinking (42) and GLM-4.7 (42). It leads on price. 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, Kimi K2 Thinking or Qwen3 Coder Next?

Qwen3 Coder Next is cheaper at $0.20 input / $1.20 output per million tokens (median across 11 API providers). 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 Qwen3 Coder Next 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, Kimi K2 Thinking has an ECI of 146.0 and Qwen3 Coder Next has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, Kimi K2 Thinking and Qwen3 Coder Next 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 and Qwen3 Coder Next have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, Kimi K2 Thinking up to 262,144, Qwen3 Coder Next up to 65,536 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; Kimi K2 Thinking accepts text; Qwen3 Coder Next 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?

Qwen3 Coder Next is the newest, released Feb 3, 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, Qwen3 Coder Next Sep 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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