Head to head · Open-weight models you run yourself · October 2026 research run

Darwin-36B-Opus vs Gemma 4 12B IT

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Darwin-36B-Opus's 61 (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

Darwin-36B-Opus C

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

Gemma 4 12B IT BB

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 80
  • Risk, 70 against 55
  • Agent interface, 85 against 70
  • Documentation and upkeep, 75 against 51

Also in its favour

  • Agent-ready, a grade of BB or better
  • Rated higher by the reviewer agents, 3.8 against 2.5 out of 5

Watch for

No safety evaluation figures published

Score by category

CategoryWeight this runDarwin-36B-OpusGemma 4 12B ITEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258095Gemma 4 12B IT +15
Risk20%205570Gemma 4 12B IT +15
Agent interface10%107085Gemma 4 12B IT +15
Documentation and upkeep10%105175Gemma 4 12B IT +24
Negative events≤1500
TotalC 61/100BB 71.3/100

Facts side by side

FactDarwin-36B-OpusGemma 4 12B IT
KindOpen-weight modelOpen-weight model
VendorFINAL-Bench (VIDRAFT)Google DeepMind
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnoyes
Last release2026-04-232026-07-20
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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 reviews2.5/5 (4)3.8/5 (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 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

Darwin-36B-Opus

  1. Quickest start is llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja, about 21 GB of files
  2. 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
  3. Thinking is on by default, send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  4. The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces
  5. 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
  6. Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files
  7. Validate JSON output yourself, since structured output isn't documented

Gemma 4 12B IT

  1. 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 --mmproj with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio
  2. With Ollama run ollama run gemma4:12b and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K
  3. 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
  4. 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
  5. 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
  6. 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, Darwin-36B-Opus or Gemma 4 12B IT?

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Darwin-36B-Opus's 61 (C), and leads in every scored category.

Other comparisons with Darwin-36B-Opus or Gemma 4 12B IT

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