ZMIME
Comparison · 3 models · Updated Nov 2, 2026

GLM-4.7 vs GLM-4.5-Air vs Qwen3 235B-A22B Thinking 2507

GLM-4.5-Air comes out ahead, 48 to 42 and 41 on our weighted score, and it is the cheaper option too.

  1. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    41/100
    • ECI143.7
    • Price$0.60 / $2.20
    • Context205K
    Full specs Remove GLM-4.7
  2. Our pick

    Z.ai (Zhipu)

    GLM-4.5-Air

    Released Jul 28, 2025

    48/100
    • ECI—
    • Price$0.20 / $1.10
    • Context131K
    Full specs Remove GLM-4.5-Air

  3. Alibaba (Qwen)

    Qwen3 235B-A22B Thinking 2507

    Released Jul 25, 2025

    42/100
    • ECI143.8
    • Price$0.30 / $2.90
    • Context262K
    Full specs Remove Qwen3 235B-A22B Thinking 2507
01 — Verdict

GLM-4.5-Air is our pick

GLM-4.5-Air is the better all-round choice, scoring 48/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price. Qwen3 235B-A22B Thinking 2507 wins on context window. 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 priceGLM-4.5-AirGLM-4.5-Air $0.425 · Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · GLM-4.5-Air 131,072 tokens
  • Widest inputsSame inputsGLM-4.7: Text · GLM-4.5-Air: Text · Qwen3 235B-A22B Thinking 2507: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.7GLM-4.5-AirQwen3 235B-A22B Thinking 2507
Price50%486549
Inputs & features30%353535
Context window20%322537
Overall100%41/10048/10042/100

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 GLM-4.5-Air vs Qwen3 235B-A22B Thinking 2507 specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)GLM-4.5-AirZ.ai (Zhipu)Qwen3 235B-A22B Thinking 2507Alibaba (Qwen)
Capability
Capabilities Index (ECI)143.7—143.8 (best)
ECI rank#94 of 165—#93 of 165 (best)
GPQA DiamondGraduate-level science questions83.3% (best)—80.1%
OTIS Mock AIME 2024–2025Competition mathematics83.3%—86.7% (best)
SimpleQA VerifiedShort factual questions32.2%—40.4% (best)
Price per million tokens
Input$0.60$0.20 (best)$0.30
Output$2.20$1.10 (best)$2.90
Cached input$0.11$0.03 (best)—
Blended (3:1)$1.00$0.425 (best)$0.95
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIOfficial Z.AI APIMedian of 11 providers
Limits
Context window204,800 tokens131,072 tokens262,144 tokens (best)
Max output131,072 tokens (best)98,304 tokens81,920 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpenApache 2.0
API model IDglm-4.7glm-4.5-air—
API providers31 (best)1912
ReleasedDec 22, 2025Jul 28, 2025Jul 25, 2025
Knowledge cutoffApr 2025Apr 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
  • GLM-4.5-Air$4.20
  • Qwen3 235B-A22B Thinking 2507$8.80
04 — Questions

Which should you choose?

Which is better: GLM-4.7, GLM-4.5-Air or Qwen3 235B-A22B Thinking 2507?

GLM-4.5-Air is the better all-round choice, scoring 48/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price. Qwen3 235B-A22B Thinking 2507 wins on context window. 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, GLM-4.5-Air or Qwen3 235B-A22B Thinking 2507?

GLM-4.5-Air is cheaper at $0.20 input / $1.10 output per million tokens (official Z.AI API price). Qwen3 235B-A22B Thinking 2507 costs $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). At a typical mix of three input tokens to one output token, that is $0.425 per million tokens for GLM-4.5-Air versus $0.95 for Qwen3 235B-A22B Thinking 2507 (2.2× as much) and $1.00 for GLM-4.7 (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 143.7, GLM-4.5-Air has not been scored yet and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, GLM-4.5-Air and Qwen3 235B-A22B Thinking 2507 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?

Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 131,072 for GLM-4.5-Air. Maximum output per response: GLM-4.7 up to 131,072, GLM-4.5-Air up to 98,304, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; GLM-4.5-Air accepts text; Qwen3 235B-A22B Thinking 2507 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. GLM-4.5-Air came out Jul 28, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, GLM-4.5-Air 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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