# Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next

> Qwen3-VL-30B-A3B-Instruct scores 58 (C) to Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next
- Markdown: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.md (~7,200 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.min.md (~580 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.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-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

- 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
- Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #87 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.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-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 60
- Risk, 55 against 45

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 1.8 out of 5

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

### Swift 1.5 Qwen3.8-Flash-Next (D)

Good for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

Watch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses


## Score by category

| Category | Weight | Qwen3-VL-30B-A3B-Instruct | Swift 1.5 Qwen3.8-Flash-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 95 | 60 | Qwen3-VL-30B-A3B-Instruct +35 |
| Risk | 20% (20 this run) | 55 | 45 | Qwen3-VL-30B-A3B-Instruct +10 |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 36 | 40 | Swift 1.5 Qwen3.8-Flash-Next +4 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **58 · C** | **52.3 · D** | |

## Facts side by side

| Fact | Qwen3-VL-30B-A3B-Instruct | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-11-01 | 2026-09-24 |
| 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 | 3/5 (4) | 1.8/5 (4) |

## Verdicts

**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.

**Swift 1.5 Qwen3.8-Flash-Next.** A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

## Before you call either

### 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

### Swift 1.5 Qwen3.8-Flash-Next

1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use
2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

## Questions

### Which is better for AI agents, Qwen3-VL-30B-A3B-Instruct or Swift 1.5 Qwen3.8-Flash-Next?

Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 2 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-vl-30b-a3b-instruct", "b": "swift-1-5-qwen3-8-flash-next"}`. From a terminal: `anchor compare qwen3-vl-30b-a3b-instruct swift-1-5-qwen3-8-flash-next`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-instruct.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json

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