ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Qwen3 235B-A22B Thinking 2507 vs Kimi K2 Thinking vs GLM-4.7

Too close to call on our weighted score (Kimi K2 Thinking 56, Qwen3 235B-A22B Thinking 2507 55, GLM-4.7 55). The right pick depends on what you value most.

  1. Alibaba (Qwen)

    Qwen3 235B-A22B Thinking 2507

    Released Jul 25, 2025

    55/100
    • ECI143.8
    • Price$0.30 / $2.90
    • Context262K
    Full specs Remove Qwen3 235B-A22B Thinking 2507

  2. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    56/100
    • ECI145.9
    • Price$0.60 / $2.50
    • Context262K
    Full specs Remove Kimi K2 Thinking

  3. 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
01 — Verdict

Too close to call

It is close. Our weighted score puts them within a point (Kimi K2 Thinking 56/100, Qwen3 235B-A22B Thinking 2507 55/100, GLM-4.7 55/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and Qwen3 235B-A22B Thinking 2507 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 145.9 · Qwen3 235B-A22B Thinking 2507 143.8 · GLM-4.7 143.7
  • Lowest priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextQwen3 235B-A22B Thinking 2507 and Kimi K2 ThinkingQwen3 235B-A22B Thinking 2507 262,144 · Kimi K2 Thinking 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsSame inputsQwen3 235B-A22B Thinking 2507: Text · Kimi 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
MeasureWeightQwen3 235B-A22B Thinking 2507Kimi K2 ThinkingGLM-4.7
CapabilityCapabilities Index (ECI)50%697169
Price25%494648
Inputs & features15%353535
Context window10%373732
Overall100%55/10056/10055/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Qwen3 235B-A22B Thinking 2507 vs Kimi K2 Thinking vs GLM-4.7 specifications side by side
SpecificationQwen3 235B-A22B Thinking 2507Alibaba (Qwen)Kimi K2 ThinkingMoonshot AIGLM-4.7Z.ai (Zhipu)
Capability
Capabilities Index (ECI)143.8145.9 (best)143.7
ECI rank#93 of 165#84 of 165 (best)#94 of 165
GPQA DiamondGraduate-level science questions80.1%84.2% (best)83.3%
OTIS Mock AIME 2024–2025Competition mathematics86.7% (best)83.1%83.3%
SimpleQA VerifiedShort factual questions40.4% (best)—32.2%
Price per million tokens
Input$0.30 (best)$0.60$0.60
Output$2.90$2.50$2.20 (best)
Cached input——$0.11
Blended (3:1)$0.95 (best)$1.07$1.00
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 15 providersOfficial Z.AI API
Limits
Context window262,144 tokens (best)262,144 tokens (best)204,800 tokens
Max output81,920 tokens262,144 tokens (best)131,072 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenApache 2.0OpenOpen
API model ID——glm-4.7
API providers121631 (best)
ReleasedJul 25, 2025Nov 6, 2025Dec 22, 2025
Knowledge cutoff—Aug 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.

  • Qwen3 235B-A22B Thinking 2507$8.80
  • Kimi K2 Thinking$11.00
  • GLM-4.7$10.40
04 — Questions

Which should you choose?

Which is better: Qwen3 235B-A22B Thinking 2507, Kimi K2 Thinking or GLM-4.7?

It is close. Our weighted score puts them within a point (Kimi K2 Thinking 56/100, Qwen3 235B-A22B Thinking 2507 55/100, GLM-4.7 55/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and Qwen3 235B-A22B Thinking 2507 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen3 235B-A22B Thinking 2507, Kimi K2 Thinking or GLM-4.7?

Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 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 15 API providers). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $1.00 for GLM-4.7 (1.1× as much) and $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 145.9 (#84 of 165), Qwen3 235B-A22B Thinking 2507 143.8 (#93 of 165) and GLM-4.7 143.7 (#94 of 165). The confidence ranges of the top two overlap (143.3–147.4 vs 141.9–145.2), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, GLM-4.7 83.3%, Qwen3 235B-A22B Thinking 2507 80.1%; OTIS Mock AIME 2024–2025 — Qwen3 235B-A22B Thinking 2507 86.7%, GLM-4.7 83.3%, Kimi K2 Thinking 83.1%.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507, 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. All three support tool calling for agent workflows.

Which has the bigger context window?

Qwen3 235B-A22B Thinking 2507 and Kimi K2 Thinking have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-4.7. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, Kimi K2 Thinking up to 262,144, GLM-4.7 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Qwen3 235B-A22B Thinking 2507 accepts text; 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, all three publish their weights (Apache 2.0), 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; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 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 Nov 2, 2026.

05 — Keep comparing

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