ZMIME
Comparison · 3 models · Updated Nov 2, 2026

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

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. 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

  2. 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

  3. OpenAI

    GPT OSS 20B

    Released Aug 5, 2025

    61/100
    • ECI137.8
    • Price$0.07 / $0.25
    • Context131K
    Full specs Remove GPT OSS 20B
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 inputsQwen3 30B-A3B Instruct 2507: Text · GLM-4.7: Text · GPT OSS 20B: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightQwen3 30B-A3B Instruct 2507GLM-4.7GPT OSS 20B
CapabilityCapabilities Index (ECI)50%616961
Price25%854890
Inputs & features15%253535
Context window10%373225
Overall100%59/10055/10061/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Qwen3 30B-A3B Instruct 2507 vs GLM-4.7 vs GPT OSS 20B specifications side by side
SpecificationQwen3 30B-A3B Instruct 2507Alibaba (Qwen)GLM-4.7Z.ai (Zhipu)GPT OSS 20BOpenAI
Capability
Capabilities Index (ECI)137.3143.7 (best)137.8
ECI rank#124 of 165#94 of 165 (best)#122 of 165
GPQA DiamondGraduate-level science questions55.6%83.3% (best)60.8%
OTIS Mock AIME 2024–2025Competition mathematics62.2%83.3% (best)65.3%
SimpleQA VerifiedShort factual questions—32.2%—
Price per million tokens
Input$0.10$0.60$0.07 (best)
Output$0.30$2.20$0.25 (best)
Cached input—$0.11—
Blended (3:1)$0.15$1.00$0.115 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 12 providersOfficial Z.AI APIMedian of 37 providers
Limits
Context window262,144 tokens (best)204,800 tokens131,072 tokens
Max output16,384 tokens131,072 tokens (best)32,768 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenApache 2.0OpenOpen
API model ID—glm-4.7—
API providers143141 (best)
ReleasedJul 28, 2025Dec 22, 2025Aug 5, 2025
Knowledge cutoff—Apr 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.

  • Qwen3 30B-A3B Instruct 2507$1.60
  • GLM-4.7$10.40
  • GPT OSS 20B$1.20
04 — Questions

Which should you choose?

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

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

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 Qwen3 30B-A3B Instruct 2507, GLM-4.7 and GPT OSS 20B 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: Qwen3 30B-A3B Instruct 2507 up to 16,384, GLM-4.7 up to 131,072, GPT OSS 20B up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Qwen3 30B-A3B Instruct 2507 accepts text; GLM-4.7 accepts text; GPT OSS 20B 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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