# BigBang-v1 vs DeepSeek-V3.2

> DeepSeek-V3.2 scores 58.6 (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-deepseek-v3-2
- Markdown: https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.md (~5,850 tokens)
- Slim: https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.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

DeepSeek-V3.2 scores 58.6 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 1 of 4 scored categories. BigBang-v1 leads on risk and 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
- DeepSeek-V3.2: grade C, 58.6/100, rank #65 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.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:
- Risk, 55 against 50
- Documentation and upkeep, 46 against 34

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

### DeepSeek-V3.2 (C)

Good for: Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider

Ahead on:
- Reach, 60 against 55

Watch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB


## Score by category

| Category | Weight | BigBang-v1 | DeepSeek-V3.2 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 60 | DeepSeek-V3.2 +5 |
| Risk | 20% (20 this run) | 55 | 50 | BigBang-v1 +5 |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 46 | 34 | BigBang-v1 +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.4 · C** | **58.6 · C** | |

## Facts side by side

| Fact | BigBang-v1 | DeepSeek-V3.2 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | DeepSeek |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-06 | 2025-12-01 |
| 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) | 2.3/5 (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.

**DeepSeek-V3.2.** DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.

## 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

### DeepSeek-V3.2

1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens

## Questions

### Which is better for AI agents, BigBang-v1 or DeepSeek-V3.2?

DeepSeek-V3.2 scores 58.6 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 1 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "bigbang-v1", "b": "deepseek-v3-2"}`. From a terminal: `anchor compare bigbang-v1 deepseek-v3-2`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json and https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json

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- [DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-thinkingcap-qwen3-8-27b.md)

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