# Qwen3-VL-32B-Instruct vs ZAYA1-8B (slim)

> ZAYA1-8B scores 61 (C) to Qwen3-VL-32B-Instruct's 56.4 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.md (~8,900 tokens) · this version ~580 tokens · JSON https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.json · canonical https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 1 of 4 scored categories. Qwen3-VL-32B-Instruct leads on reach. Both do open-weight models you run yourself.

- Qwen3-VL-32B-Instruct: C 56.4, rank #115 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.min.md
- ZAYA1-8B: C 61, rank #85 of 160 open-weight models · https://www.anchorterminal.com/tools/zaya1-8b.min.md
- Qwen3-VL-32B-Instruct, good for An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video Ahead on Reach, 85 against 80.
- ZAYA1-8B, good for People who want a small, fast reasoning model for maths and code and can live without tool calling Ahead on Documentation and upkeep, 50 against 31.

| Category | Qwen3-VL-32B-Instruct | ZAYA1-8B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 80 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 31 | 50 |

| Fact (where they differ) | Qwen3-VL-32B-Instruct | ZAYA1-8B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Zyphra |
| Last release | 2025-11-01 | 2026-07-02 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Documentation and upkeep, ZAYA1-8B +19: Qwen3-VL-32B-Instruct 31 (The card misses limitations, languages and a cut-off (11).); ZAYA1-8B 50 (Model card 5, Intended use and the parameter counts are on the card.)
- Reach, Qwen3-VL-32B-Instruct +5: Qwen3-VL-32B-Instruct 85 (Alibaba's own Q4_K_M GGUF is 19.76 GB and 20.53 GB with the vision file it needs for images, so with a fifth added it needs about 25 GB and the 32 GB band (20).); ZAYA1-8B 80 (Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB.)