Head to head · Open-weight models you run yourself · October 2026 research run
BigBang-v1 vs Gemma 4 12B IT
Gemma 4 12B IT scores 71.3 (BB) on agent readiness against BigBang-v1's 54.4 (C), and leads in every scored category. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
Which one, for what
Best 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.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
BF16 is the only build from the publisher, needing about 86 GB
Best for Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context
Ahead on
- Reach, 95 against 55
- Risk, 70 against 55
- Agent interface, 85 against 60
- Documentation and upkeep, 75 against 46
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5
Watch for
No safety evaluation figures published
Score by category
| Category | Weight this run | BigBang-v1 | Gemma 4 12B IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 95 | Gemma 4 12B IT +40 |
| Risk | 20%20 | 55 | 70 | Gemma 4 12B IT +15 |
| Agent interface | 10%10 | 60 | 85 | Gemma 4 12B IT +25 |
| Documentation and upkeep | 10%10 | 46 | 75 | Gemma 4 12B IT +29 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.4/100 | BB 71.3/100 |
Facts side by side
| Fact | BigBang-v1 | Gemma 4 12B IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | Google DeepMind |
| 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 | yes |
| Last release | 2026-08-06 | 2026-07-20 |
| 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) | 3.8/5 (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.
Gemma 4 12B IT
A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.
Before you call either
BigBang-v1
- 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 - The card says to keep at least 128K of context so its thinking isn't cut short
- Thinking control isn't documented, try the base template's
enable_thinkingfalse in chat_template_kwargs and check the output - For a smaller build you'll need someone else's quantisation, the publisher has none
- Validate JSON output yourself or use the runtime's guided decoding
Gemma 4 12B IT
- On the reference machine run
llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072, and add--mmprojwith mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio - With Ollama run
ollama run gemma4:12band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text; Google's w4a16 build is the 4-bit route there - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them
- Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama
Questions
Which is better for AI agents, BigBang-v1 or Gemma 4 12B IT?
Gemma 4 12B IT scores 71.3 (BB) on agent readiness against BigBang-v1's 54.4 (C), and leads in every scored category.
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Machine-readable
- This page as Markdown
/compare/bigbang-v1-vs-gemma-4-12b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/bigbang-v1.json·/api/v1/tools/gemma-4-12b-it.json - From a terminal
anchor compare bigbang-v1 gemma-4-12b-it(the CLI) - Over MCP
compare_tools {"a": "bigbang-v1", "b": "gemma-4-12b-it"}at/mcp, no key