ZMIME
Comparison · 3 models · Updated Nov 2, 2026

GLM-4.7 vs GPT OSS 20B vs Qwen3 30B-A3B Instruct 2507

Too close to call on our weighted score (GPT OSS 20B 61, Qwen3 30B-A3B Instruct 2507 59, GLM-4.7 55). The right pick depends on what you value most.

  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. OpenAI

    GPT OSS 20B

    Released Aug 5, 2025

    61/100
    • ECI137.8
    • Price$0.07 / $0.25
    • Context131K
    Full specs Remove GPT OSS 20B

  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

Too close to call

It is close. Our weighted score puts them within 2 points (GPT OSS 20B 61/100, Qwen3 30B-A3B Instruct 2507 59/100, GLM-4.7 55/100), so choose by what matters most for your work: GLM-4.7 for raw capability, GPT OSS 20B on price and Qwen3 30B-A3B Instruct 2507 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGLM-4.7Capabilities Index (ECI): GLM-4.7 143.7 · GPT OSS 20B 137.8 · Qwen3 30B-A3B Instruct 2507 137.3
  • Lowest priceGPT OSS 20BGPT OSS 20B $0.115 · Qwen3 30B-A3B Instruct 2507 $0.15 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextQwen3 30B-A3B Instruct 2507Qwen3 30B-A3B Instruct 2507 262,144 · GLM-4.7 204,800 · GPT OSS 20B 131,072 tokens
  • Widest inputsSame inputsGLM-4.7: Text · GPT OSS 20B: Text · Qwen3 30B-A3B Instruct 2507: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.7GPT OSS 20BQwen3 30B-A3B Instruct 2507
CapabilityCapabilities Index (ECI)50%696161
Price25%489085
Inputs & features15%353525
Context window10%322537
Overall100%55/10061/10059/100
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 OSS 20B vs Qwen3 30B-A3B Instruct 2507 specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)GPT OSS 20BOpenAIQwen3 30B-A3B Instruct 2507Alibaba (Qwen)
Capability
Capabilities Index (ECI)143.7 (best)137.8137.3
ECI rank#94 of 165 (best)#122 of 165#124 of 165
GPQA DiamondGraduate-level science questions83.3% (best)60.8%55.6%
OTIS Mock AIME 2024–2025Competition mathematics83.3% (best)65.3%62.2%
SimpleQA VerifiedShort factual questions32.2%——
Price per million tokens
Input$0.60$0.07 (best)$0.10
Output$2.20$0.25 (best)$0.30
Cached input$0.11——
Blended (3:1)$1.00$0.115 (best)$0.15
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 37 providersMedian of 12 providers
Limits
Context window204,800 tokens131,072 tokens262,144 tokens (best)
Max output131,072 tokens (best)32,768 tokens16,384 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpenApache 2.0
API model IDglm-4.7——
API providers3141 (best)14
ReleasedDec 22, 2025Aug 5, 2025Jul 28, 2025
Knowledge cutoffApr 2025Jun 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 OSS 20B$1.20
  • Qwen3 30B-A3B Instruct 2507$1.60
04 — Questions

Which should you choose?

Which is better: GLM-4.7, GPT OSS 20B or Qwen3 30B-A3B Instruct 2507?

It is close. Our weighted score puts them within 2 points (GPT OSS 20B 61/100, Qwen3 30B-A3B Instruct 2507 59/100, GLM-4.7 55/100), so choose by what matters most for your work: GLM-4.7 for raw capability, GPT OSS 20B on price and Qwen3 30B-A3B Instruct 2507 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GLM-4.7, GPT OSS 20B or Qwen3 30B-A3B Instruct 2507?

GPT OSS 20B is cheaper at $0.07 input / $0.25 output per million tokens (median across 37 API providers). 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 $0.115 per million tokens for GPT OSS 20B versus $0.15 for Qwen3 30B-A3B Instruct 2507 (1.3× as much) and $1.00 for GLM-4.7 (8.7× 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 OSS 20B 137.8 (#122 of 165) and Qwen3 30B-A3B Instruct 2507 137.3 (#124 of 165). Their confidence ranges do not overlap (141.4–145.8 vs 133.1–139.7), so the gap is a real one. On individual benchmarks: GPQA Diamond — GLM-4.7 83.3%, GPT OSS 20B 60.8%, Qwen3 30B-A3B Instruct 2507 55.6%; OTIS Mock AIME 2024–2025 — GLM-4.7 83.3%, GPT OSS 20B 65.3%, 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 OSS 20B 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?

Qwen3 30B-A3B Instruct 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 131,072 for GPT OSS 20B. Maximum output per response: GLM-4.7 up to 131,072, GPT OSS 20B up to 32,768, Qwen3 30B-A3B Instruct 2507 up to 16,384 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; GPT OSS 20B accepts text; Qwen3 30B-A3B Instruct 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. GPT OSS 20B came out Aug 5, 2025; Qwen3 30B-A3B Instruct 2507 came out Jul 28, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, GPT OSS 20B Jun 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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