ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Qwen3 235B-A22B Thinking 2507 vs GPT-5 Chat vs GLM-4.7

Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 42, GLM-4.7 41, GPT-5 Chat 31). The right pick depends on what you value most.

  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. OpenAI

    GPT-5 Chat

    Released Aug 7, 2025

    31/100
    • ECI—
    • Price$1.25 / $10.00
    • Context400K
    Full specs Remove GPT-5 Chat

  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

Too close to call

It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, GPT-5 Chat 31/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and GPT-5 Chat for long inputs. 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 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 · GPT-5 Chat $3.44 per 1M tokens (3:1 blend)
  • Longest contextGPT-5 ChatGPT-5 Chat 400,000 · Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsGPT-5 ChatQwen3 235B-A22B Thinking 2507: Text · GPT-5 Chat: Text, Images · GLM-4.7: Text
  • Self-hostingQwen3 235B-A22B Thinking 2507 and GLM-4.7Publishes downloadable weights (Apache 2.0)
How the score is built
MeasureWeightQwen3 235B-A22B Thinking 2507GPT-5 ChatGLM-4.7
Price50%492348
Inputs & features30%353535
Context window20%374432
Overall100%42/10031/10041/100

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 GPT-5 Chat vs GLM-4.7 specifications side by side
SpecificationQwen3 235B-A22B Thinking 2507Alibaba (Qwen)GPT-5 ChatOpenAIGLM-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 (best)$1.25$0.60
Output$2.90$10.00$2.20 (best)
Cached input——$0.11
Blended (3:1)$0.95 (best)$3.44$1.00
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 5 providersOfficial Z.AI API
Limits
Context window262,144 tokens400,000 tokens (best)204,800 tokens
Max output81,920 tokens128,000 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesNoYes
Structured outputNoNoNo
Availability
WeightsOpenApache 2.0ProprietaryOpen
API model ID——glm-4.7
API providers12531 (best)
ReleasedJul 25, 2025Aug 7, 2025Dec 22, 2025
Knowledge cutoff—Sep 30, 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
  • GPT-5 Chat$32.50
  • GLM-4.7$10.40
04 — Questions

Which should you choose?

Which is better: Qwen3 235B-A22B Thinking 2507, GPT-5 Chat or GLM-4.7?

It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, GPT-5 Chat 31/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and GPT-5 Chat for long inputs. 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, GPT-5 Chat 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); GPT-5 Chat costs $1.25 input / $10.00 output per million tokens (median across 5 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 $3.44 for GPT-5 Chat (3.6× 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, GPT-5 Chat 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, GPT-5 Chat 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 GPT-5 Chat does not support tool calling, which most coding agents need.

Which has the bigger context window?

GPT-5 Chat has the largest context window at 400,000 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507 and 204,800 for GLM-4.7. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, GPT-5 Chat up to 128,000, GLM-4.7 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Qwen3 235B-A22B Thinking 2507 accepts text; GPT-5 Chat accepts text and images; GLM-4.7 accepts text. GPT-5 Chat handles the widest range of inputs.

Are any of these open source?

Qwen3 235B-A22B Thinking 2507 and GLM-4.7 publishes its weights (Apache 2.0) and can be self-hosted; GPT-5 Chat is proprietary.

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. GPT-5 Chat came out Aug 7, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025. Knowledge cutoff: GPT-5 Chat Sep 30, 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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