Head to head · Open-weight models you run yourself · October 2026 research run
Darwin-36B-Opus vs Gemma 4 E2B IT
Darwin-36B-Opus and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep. 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 Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
The merge method is proprietary and the current weights include an undescribed targeted refinement
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 80
- Risk, 70 against 55
- Agent interface, 85 against 70
- Documentation and upkeep, 75 against 51
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5
Watch for
No safety evaluation figures published
Score by category
| Category | Weight this run | Darwin-36B-Opus | Gemma 4 E2B IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 95 | Gemma 4 E2B IT +15 |
| Risk | 20%20 | 55 | 70 | Gemma 4 E2B IT +15 |
| Agent interface | 10%10 | 70 | 85 | Gemma 4 E2B IT +15 |
| Documentation and upkeep | 10%10 | 51 | 75 | Gemma 4 E2B IT +24 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 60.4/100 |
Facts side by side
| Fact | Darwin-36B-Opus | Gemma 4 E2B IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | FINAL-Bench (VIDRAFT) | 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 | 2026-04-23 | 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 | 2.5/5 (4) | 3.5/5 (4) |
Verdicts
Darwin-36B-Opus
A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.
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
Darwin-36B-Opus
- Quickest start is
llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja, about 21 GB of files - On the reference machine BF16 fits,
vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parsers for Qwen3.6's format - Thinking is on by default, send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces
- generation_config.json stops only on the end-of-text token, so add
<|im_end|>as a stop token if a runtime keeps generating after the turn ends - Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files
- Validate JSON output yourself, since structured output isn't documented
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, Darwin-36B-Opus or Gemma 4 E2B IT?
Darwin-36B-Opus and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.
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Machine-readable
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/compare/darwin-36b-opus-vs-gemma-4-e2b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/darwin-36b-opus.json·/api/v1/tools/gemma-4-e2b-it.json - From a terminal
anchor compare darwin-36b-opus gemma-4-e2b-it(the CLI) - Over MCP
compare_tools {"a": "darwin-36b-opus", "b": "gemma-4-e2b-it"}at/mcp, no key