# Apertus v1.5 8B vs Qwen3-VL-32B-Instruct

> Apertus v1.5 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.

- Canonical: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct
- Markdown: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct.md (~6,450 tokens)
- Slim: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Apertus v1.5 8B scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 3 of 4 scored categories. Qwen3-VL-32B-Instruct leads on reach. Both do open-weight models you run yourself.

- Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json
- Qwen3-VL-32B-Instruct: grade C, 56.4/100, rank #75 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-instruct.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Apertus v1.5 8B (C)

Good for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.

Ahead on:
- Risk, 75 against 55
- Agent interface, 85 against 60
- Documentation and upkeep, 84 against 31

Watch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers

### Qwen3-VL-32B-Instruct (C)

Good for: An agent with a 32 GB GPU or more that wants the strongest dense Qwen3-VL model for images, documents and video

Ahead on:
- Reach, 85 against 40

Watch for: No safety evaluation, agent-use guidance or security contact


## Score by category

| Category | Weight | Apertus v1.5 8B | Qwen3-VL-32B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 40 | 85 | Qwen3-VL-32B-Instruct +45 |
| Risk | 20% (20 this run) | 75 | 55 | Apertus v1.5 8B +20 |
| Agent interface | 10% (10 this run) | 85 | 60 | Apertus v1.5 8B +25 |
| Documentation and upkeep | 10% (10 this run) | 84 | 31 | Apertus v1.5 8B +53 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **56.4 · C** | |

## Facts side by side

| Fact | Apertus v1.5 8B | Qwen3-VL-32B-Instruct |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-14 | 2025-11-01 |
| Terms last updated |  | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.8/5 (4) | 2.5/5 (4) |

## Verdicts

**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.

**Qwen3-VL-32B-Instruct.** The largest dense Qwen3-VL cousin, for harder work on screenshots, documents, charts and video, needing about 25 GB from Alibaba's Q4_K_M GGUF with its vision file, in every major runtime under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it, so plan to run it yourself.

## Before you call either

### Apertus v1.5 8B

1. Accept the terms on the card and set HF_TOKEN before downloading
2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools
4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously
5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent

### Qwen3-VL-32B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:32b-instruct`
2. In Ollama the plain `qwen3-vl:32b` tag is the Thinking edition, so name `32b-instruct`
3. With the GGUF, load the mmproj vision file too, or images won't be read
4. Parse tool calls as JSON inside `<tool_call>` tags, or use SGLang's `--tool-call-parser qwen`
5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
8. If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's

## Questions

### Which is better for AI agents, Apertus v1.5 8B or Qwen3-VL-32B-Instruct?

Apertus v1.5 8B scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 3 of 4 scored categories. Qwen3-VL-32B-Instruct leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "apertus-v1-5-8b", "b": "qwen3-vl-32b-instruct"}`. From a terminal: `anchor compare apertus-v1-5-8b qwen3-vl-32b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-instruct.json

## Other comparisons with Apertus v1.5 8B or Qwen3-VL-32B-Instruct

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- [MiniCPM5-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-32b-instruct.md)
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- [MiniMax-M2.7 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct.md)
- [Muse Glimmer 30B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct.md)
- [Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-32b-instruct.md)
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- [Olmo 3 7B Think vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct.md)
- [Ornith-1.5-35B-A3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct.md)
- [Ornith-1.5-397B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.md)
- [Ornith-1.5-9B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-32b-instruct.md)
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- [Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b.md)
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