ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Qwen3 235B-A22B Thinking 2507 vs Baichuan M2 32B vs GLM-4.7

Baichuan M2 32B comes out ahead, 58 to 42 and 41 on our weighted score, and it is the cheaper option too.

  1. 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
  2. Our pick

    Baichuan

    Baichuan M2 32B

    Released Aug 13, 2025

    58/100
    • ECI—
    • Price$0.07 / $0.07
    • Context131K
    Full specs Remove Baichuan M2 32B

  3. 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
01 — Verdict

Baichuan M2 32B is our pick

Baichuan M2 32B is the better all-round choice, scoring 58/100 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 priceBaichuan M2 32BBaichuan M2 32B $0.07 · 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 · Baichuan M2 32B 131,072 tokens
  • Widest inputsSame inputsQwen3 235B-A22B Thinking 2507: Text · Baichuan M2 32B: 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 2507Baichuan M2 32BGLM-4.7
Price50%4910048
Inputs & features30%351035
Context window20%372532
Overall100%42/10058/10041/100

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.

Qwen3 235B-A22B Thinking 2507 vs Baichuan M2 32B vs GLM-4.7 specifications side by side
SpecificationQwen3 235B-A22B Thinking 2507Alibaba (Qwen)Baichuan M2 32BBaichuanGLM-4.7Z.ai (Zhipu)
Capability
Capabilities Index (ECI)143.8 (best)—143.7
ECI rank#93 of 165 (best)—#94 of 165
GPQA DiamondGraduate-level science questions80.1%—83.3% (best)
OTIS Mock AIME 2024–2025Competition mathematics86.7% (best)—83.3%
SimpleQA VerifiedShort factual questions40.4% (best)—32.2%
Price per million tokens
Input$0.30$0.07 (best)$0.60
Output$2.90$0.07 (best)$2.20
Cached input——$0.11
Blended (3:1)$0.95$0.07 (best)$1.00
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 1 providersOfficial Z.AI API
Limits
Context window262,144 tokens (best)131,072 tokens204,800 tokens
Max output81,920 tokens131,072 tokens (best)131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesNoYes
Structured outputNoNoNo
Availability
WeightsOpenApache 2.0OpenApache-2.0Open
API model ID——glm-4.7
API providers12131 (best)
ReleasedJul 25, 2025Aug 13, 2025Dec 22, 2025
Knowledge cutoff——Apr 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
  • Baichuan M2 32B$0.84
  • GLM-4.7$10.40
04 — Questions

Which should you choose?

Which is better: Qwen3 235B-A22B Thinking 2507, Baichuan M2 32B or GLM-4.7?

Baichuan M2 32B is the better all-round choice, scoring 58/100 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, Qwen3 235B-A22B Thinking 2507, Baichuan M2 32B or GLM-4.7?

Baichuan M2 32B is cheaper at $0.07 input / $0.07 output per million tokens (median across 1 API provider). 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 $0.07 per million tokens for Baichuan M2 32B versus $0.95 for Qwen3 235B-A22B Thinking 2507 (14× as much) and $1.00 for GLM-4.7 (14× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8, Baichuan M2 32B has not been scored yet and GLM-4.7 has an ECI of 143.7.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507, Baichuan M2 32B and GLM-4.7 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Baichuan M2 32B does not support tool calling, which most coding agents need.

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 Baichuan M2 32B. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, Baichuan M2 32B up to 131,072, GLM-4.7 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Qwen3 235B-A22B Thinking 2507 accepts text; Baichuan M2 32B 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 and Apache-2.0), so you can self-host them.

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. Baichuan M2 32B came out Aug 13, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025. Knowledge cutoff: 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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