ZMIME
Comparison · 3 models · Updated Nov 2, 2026

GLM-4.7 vs GPT-5 Nano vs Qwen3 30B-A3B Instruct 2507

GPT-5 Nano comes out ahead, 68 to 59 and 55 on our weighted score, and it is the cheaper option too.

  1. 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
  2. Our pick

    OpenAI

    GPT-5 Nano

    Released Aug 7, 2025

    68/100
    • ECI139.3
    • Price$0.05 / $0.40
    • Context400K
    Full specs Remove GPT-5 Nano

  3. Alibaba (Qwen)

    Qwen3 30B-A3B Instruct 2507

    Released Jul 28, 2025

    59/100
    • ECI137.3
    • Price$0.10 / $0.30
    • Context262K
    Full specs Remove Qwen3 30B-A3B Instruct 2507
01 — Verdict

GPT-5 Nano is our pick

GPT-5 Nano is the better all-round choice, scoring 68/100 against Qwen3 30B-A3B Instruct 2507 (59) and GLM-4.7 (55). It leads on price, inputs & features and context window. GLM-4.7 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGLM-4.7Capabilities Index (ECI): GLM-4.7 143.7 · GPT-5 Nano 139.3 · Qwen3 30B-A3B Instruct 2507 137.3
  • Lowest priceGPT-5 NanoGPT-5 Nano $0.138 · Qwen3 30B-A3B Instruct 2507 $0.15 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3 30B-A3B Instruct 2507 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsGPT-5 NanoGLM-4.7: Text · GPT-5 Nano: Text, Images · Qwen3 30B-A3B Instruct 2507: Text
  • Self-hostingGLM-4.7 and Qwen3 30B-A3B Instruct 2507Publishes downloadable weights (Apache 2.0)
How the score is built
MeasureWeightGLM-4.7GPT-5 NanoQwen3 30B-A3B Instruct 2507
CapabilityCapabilities Index (ECI)50%696361
Price25%488785
Inputs & features15%357025
Context window10%324437
Overall100%55/10068/10059/100
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 GPT-5 Nano vs Qwen3 30B-A3B Instruct 2507 specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)GPT-5 NanoOpenAIQwen3 30B-A3B Instruct 2507Alibaba (Qwen)
Capability
Capabilities Index (ECI)143.7 (best)139.3137.3
ECI rank#94 of 165 (best)#116 of 165#124 of 165
GPQA DiamondGraduate-level science questions83.3% (best)69.4%55.6%
FrontierMath Tiers 1–3Research-level mathematics—20.0%—
OTIS Mock AIME 2024–2025Competition mathematics83.3% (best)81.1%62.2%
SimpleQA VerifiedShort factual questions32.2% (best)11.7%—
Price per million tokens
Input$0.60$0.05 (best)$0.10
Output$2.20$0.40$0.30 (best)
Cached input$0.11$0.005 (best)—
Blended (3:1)$1.00$0.138 (best)$0.15
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIOfficial OpenAI APIMedian of 12 providers
Limits
Context window204,800 tokens400,000 tokens (best)262,144 tokens
Max output131,072 tokens (best)128,000 tokens16,384 tokens
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesminimal · low · medium · highNo
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenProprietaryOpenApache 2.0
API model IDglm-4.7gpt-5-nano—
API providers31 (best)3014
ReleasedDec 22, 2025Aug 7, 2025Jul 28, 2025
Knowledge cutoffApr 2025May 30, 2024—
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
  • GPT-5 Nano$1.30
  • Qwen3 30B-A3B Instruct 2507$1.60
04 — Questions

Which should you choose?

Which is better: GLM-4.7, GPT-5 Nano or Qwen3 30B-A3B Instruct 2507?

GPT-5 Nano is the better all-round choice, scoring 68/100 against Qwen3 30B-A3B Instruct 2507 (59) and GLM-4.7 (55). It leads on price, inputs & features and context window. GLM-4.7 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GLM-4.7, GPT-5 Nano or Qwen3 30B-A3B Instruct 2507?

GPT-5 Nano is cheaper at $0.05 input / $0.40 output per million tokens (official OpenAI API price). Qwen3 30B-A3B Instruct 2507 costs $0.10 input / $0.30 output per million tokens (median across 12 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 $0.138 per million tokens for GPT-5 Nano versus $0.15 for Qwen3 30B-A3B Instruct 2507 (1.1× as much) and $1.00 for GLM-4.7 (7.3× as much).

Which scores higher on benchmarks?

GLM-4.7 scores higher on the Capabilities Index (ECI): GLM-4.7 143.7 (#94 of 165), GPT-5 Nano 139.3 (#116 of 165) and Qwen3 30B-A3B Instruct 2507 137.3 (#124 of 165). The confidence ranges of the top two overlap (141.4–145.8 vs 134.3–141.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-4.7 83.3%, GPT-5 Nano 69.4%, Qwen3 30B-A3B Instruct 2507 55.6%; OTIS Mock AIME 2024–2025 — GLM-4.7 83.3%, GPT-5 Nano 81.1%, Qwen3 30B-A3B Instruct 2507 62.2%.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, GPT-5 Nano and Qwen3 30B-A3B Instruct 2507 yet, so there is no like-for-like coding score. On overall capability, GLM-4.7 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?

GPT-5 Nano has the largest context window at 400,000 tokens, against 262,144 for Qwen3 30B-A3B Instruct 2507 and 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, GPT-5 Nano up to 128,000, Qwen3 30B-A3B Instruct 2507 up to 16,384 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; GPT-5 Nano accepts text and images; Qwen3 30B-A3B Instruct 2507 accepts text. GPT-5 Nano handles the widest range of inputs.

Are any of these open source?

GLM-4.7 and Qwen3 30B-A3B Instruct 2507 publishes its weights (Apache 2.0) and can be self-hosted; GPT-5 Nano is proprietary.

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. GPT-5 Nano came out Aug 7, 2025; Qwen3 30B-A3B Instruct 2507 came out Jul 28, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, GPT-5 Nano May 30, 2024.

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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