# BigBang-v1 vs Qwen3-VL-32B-Instruct

> Qwen3-VL-32B-Instruct scores 56.4 (C) to BigBang-v1's 54.4 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct
- Markdown: https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/bigbang-v1-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

Qwen3-VL-32B-Instruct scores 56.4 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 1 of 4 scored categories. BigBang-v1 leads on documentation and upkeep. Both do open-weight models you run yourself.

- BigBang-v1: grade C, 54.4/100, rank #83 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.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

### BigBang-v1 (C)

Good for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.

Ahead on:
- Documentation and upkeep, 46 against 31

Watch for: BF16 is the only build from the publisher, needing about 86 GB

### 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 55

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


## Score by category

| Category | Weight | BigBang-v1 | Qwen3-VL-32B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 85 | Qwen3-VL-32B-Instruct +30 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 46 | 31 | BigBang-v1 +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.4 · C** | **56.4 · C** | |

## Facts side by side

| Fact | BigBang-v1 | Qwen3-VL-32B-Instruct |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | 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 | 2026-08-06 | 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 | 2.3/5 (4) | 2.5/5 (4) |

## Verdicts

**BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.

**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

### BigBang-v1

1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
2. The card says to keep at least 128K of context so its thinking isn't cut short
3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output
4. For a smaller build you'll need someone else's quantisation, the publisher has none
5. Validate JSON output yourself or use the runtime's guided decoding

### 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, BigBang-v1 or Qwen3-VL-32B-Instruct?

Qwen3-VL-32B-Instruct scores 56.4 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 1 of 4 scored categories. BigBang-v1 leads on documentation and upkeep.


## For agents

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

## Other comparisons with BigBang-v1 or Qwen3-VL-32B-Instruct

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- [Ornith-1.5-9B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-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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