# Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct

> Qwen3.8-27B scores 68.6 (B) to Qwen3-VL-30B-A3B-Instruct's 58 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.md (~6,450 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-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

Qwen3.8-27B scores 68.6 (B) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 2 of 5 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach. Both do open-weight models you run yourself.

- Qwen3.8-27B: grade B, 68.6/100, rank #10 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json
- Qwen3-VL-30B-A3B-Instruct: grade C, 58/100, rank #68 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-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

### Qwen3.8-27B (B)

Good for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.

Ahead on:
- Capability, 70 against 39
- Agent interface, 70 against 60

Watch for: No safety results, prompt-injection guidance or security contact

### Qwen3-VL-30B-A3B-Instruct (C)

Good for: An agent with 24 GB or more to spare that wants a faster, stronger local reader of images, documents and video than the 8B model

Ahead on:
- Reach, 95 against 90

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


## Score by category

| Category | Weight | Qwen3.8-27B | Qwen3-VL-30B-A3B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 70 | 39 | Qwen3.8-27B +31 |
| Reach | 25% (25 this run) | 90 | 95 | Qwen3-VL-30B-A3B-Instruct +5 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 60 | Qwen3.8-27B +10 |
| Documentation and upkeep | 10% (10 this run) | 36 | 36 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **68.6 · B** | **58 · C** | |

## Facts side by side

| Fact | Qwen3.8-27B | Qwen3-VL-30B-A3B-Instruct |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | 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 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-14 | 2025-11-01 |
| Terms last updated | no document linked | 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 | 3/5 (4) | 3/5 (4) |

## Verdicts

**Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.

**Qwen3-VL-30B-A3B-Instruct.** A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails before it drives a GUI.

## Before you call either

### Qwen3.8-27B

1. Quickest start is `ollama run qwen3.8`, about 18 GB of files
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented
3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`
5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions
8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts
9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

### Qwen3-VL-30B-A3B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:30b-a3b-instruct`
2. In Ollama the plain `qwen3-vl:30b` tag is the Thinking edition, so name `30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later

## Questions

### Which is better for AI agents, Qwen3.8-27B or Qwen3-VL-30B-A3B-Instruct?

Qwen3.8-27B scores 68.6 (B) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 2 of 5 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-8-27b", "b": "qwen3-vl-30b-a3b-instruct"}`. From a terminal: `anchor compare qwen3-8-27b qwen3-vl-30b-a3b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-instruct.json

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- [Qwen3.8-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next.md)
- [Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct.md)
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