# Agents-A1-4B vs BigBang-v1

> Agents-A1-4B scores 61 (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/agents-a1-4b-vs-bigbang-v1
- Markdown: https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md (~5,800 tokens)
- Slim: https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.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

Agents-A1-4B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

- Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json
- 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
- 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

### Agents-A1-4B (C)

Good for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence

Ahead on:
- Reach, 90 against 55
- Agent interface, 70 against 60

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5

Watch for: The base isn't declared, and the technical report doesn't cover the 4B

### 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.

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


## Score by category

| Category | Weight | Agents-A1-4B | BigBang-v1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 55 | Agents-A1-4B +35 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 60 | Agents-A1-4B +10 |
| Documentation and upkeep | 10% (10 this run) | 43 | 46 | BigBang-v1 +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **54.4 · C** | |

## Facts side by side

| Fact | Agents-A1-4B | BigBang-v1 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | InternScience (Shanghai AI Laboratory) | The Endless Frontier (endless-frontier) |
| 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 | 2026-07-14 | 2026-08-06 |
| 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) | 2.3/5 (4) |

## Verdicts

**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.

**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.

## Before you call either

### Agents-A1-4B

1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector
2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work
3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14
4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them
5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention
6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding
7. Validate JSON output yourself, since structured output isn't documented

### 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

## Questions

### Which is better for AI agents, Agents-A1-4B or BigBang-v1?

Agents-A1-4B scores 61 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in 2 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "agents-a1-4b", "b": "bigbang-v1"}`. From a terminal: `anchor compare agents-a1-4b bigbang-v1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json

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- [BigBang-v1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next.md)
- [BigBang-v1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b.md)

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