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

Gemma 4 12B IT vs gpt-oss-20b

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against gpt-oss-20b's 69.9 (B), and leads in 3 of 5 scored categories. gpt-oss-20b leads on risk and agent interface. 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

Gemma 4 12B ITGoogle DeepMind · Open-weight model BB71.3/100#5 of 100 open-weight models gpt-oss-20bOpenAI · Open-weight model B69.9/100#8 of 100 open-weight models

Which one, for what

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 90
Documentation and upkeep, 75 against 42

Also in its favour

Agent-ready, a grade of BB or better

Watch for

No safety evaluation figures published

gpt-oss-20b B

Best for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time

Ahead on

Risk, 85 against 70
Agent interface, 90 against 85

Watch for

No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered

Score by category

Category	Weight this run	Gemma 4 12B IT	gpt-oss-20b	Edge
Capability	35%35	50	49	Gemma 4 12B IT +1
Reach	25%25	95	90	Gemma 4 12B IT +5
Risk	20%20	70	85	gpt-oss-20b +15
Agent interface	10%10	85	90	gpt-oss-20b +5
Documentation and upkeep	10%10	75	42	Gemma 4 12B IT +33
Negative events	≤15	0	0
Total		BB 71.3/100	B 69.9/100

Facts side by side

Fact	Gemma 4 12B IT	gpt-oss-20b
Kind	Open-weight model	Open-weight model
Vendor	Google DeepMind	OpenAI
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	yes	no
Last release	2026-07-20	2025-08-26
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	3.8/5 (4)	3.5/5 (4)

Verdicts

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.

gpt-oss-20b

gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.

Before you call either

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 --mmproj with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio
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
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

gpt-oss-20b

Start it with ollama run gpt-oss:20b (the build that fits the reference machine), or vllm serve openai/gpt-oss-20b with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel
Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in <|call|>, reasoning on the analysis channel
Set effort with Reasoning: high (or low, medium) in the system message, or reasoning_effort in the template; medium is the default
Ask for JSON by putting a JSON Schema under # Response Formats in the developer message
Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
Expect one tool call per turn; run several in sequence rather than in parallel

Questions

Which is better for AI agents, Gemma 4 12B IT or gpt-oss-20b?

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against gpt-oss-20b's 69.9 (B), and leads in 3 of 5 scored categories. gpt-oss-20b leads on risk and agent interface.

Other comparisons with Gemma 4 12B IT or gpt-oss-20b

Machine-readable

This page as Markdown /compare/gemma-4-12b-it-vs-gpt-oss-20b.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/gemma-4-12b-it.json · /api/v1/tools/gpt-oss-20b.json
From a terminal anchor compare gemma-4-12b-it gpt-oss-20b (the CLI)
Over MCP compare_tools {"a": "gemma-4-12b-it", "b": "gpt-oss-20b"} at /mcp, no key

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