Head to head · Open-weight models you run yourself · October 2026 research run
BigBang-v1 vs Gemma 4 E2B IT
Gemma 4 E2B IT scores 60.4 (C) 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
BigBang-v1The Endless Frontier (endless-frontier) · Open-weight model
C54.4/100#83 of 100 open-weight models
Gemma 4 E2B ITGoogle DeepMind · Open-weight model
C60.4/100#54 of 100 open-weight models
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 that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning
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
- Rated higher by the reviewer agents, 3.5 against 2.3 out of 5
Watch for
No safety evaluation figures published
Score by category
| Category | Weight this run | BigBang-v1 | Gemma 4 E2B IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 95 | Gemma 4 E2B IT +40 |
| Risk | 20%20 | 55 | 70 | Gemma 4 E2B IT +15 |
| Agent interface | 10%10 | 60 | 85 | Gemma 4 E2B IT +25 |
| Documentation and upkeep | 10%10 | 46 | 75 | Gemma 4 E2B IT +29 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.4/100 | C 60.4/100 |
Facts side by side
| Fact | BigBang-v1 | Gemma 4 E2B 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) | 3.5/5 (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.
Gemma 4 E2B IT
A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.
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 E2B IT
- On the reference machine run
llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072, and add--mmprojwith gemma-4-E2B-it-mmproj.gguf for images and audio - With Ollama run
ollama run gemma4:e2band 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
Questions
Which is better for AI agents, BigBang-v1 or Gemma 4 E2B IT?
Gemma 4 E2B IT scores 60.4 (C) on agent readiness against BigBang-v1's 54.4 (C), and leads in every scored category.
Other comparisons with BigBang-v1 or Gemma 4 E2B IT
- Agents-A1-4B vs BigBang-v1
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- BigBang-v1 vs Nemotron 3 Super 120B A12B
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- Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-Flash-Next
- Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/bigbang-v1-vs-gemma-4-e2b-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-e2b-it.json - From a terminal
anchor compare bigbang-v1 gemma-4-e2b-it(the CLI) - Over MCP
compare_tools {"a": "bigbang-v1", "b": "gemma-4-e2b-it"}at/mcp, no key