Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 26B A4B IT vs gpt-oss-120b
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against gpt-oss-120b's 70.2 (BB), and leads in 3 of 5 scored categories. gpt-oss-120b 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 26B A4B ITGoogle DeepMind · Open-weight model
BB72.2/100#4 of 100 open-weight models
gpt-oss-120bOpenAI · Open-weight model
BB70.2/100#6 of 100 open-weight models
Which one, for what
Best for Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context
Ahead on
- Capability, 59 against 54
- Reach, 90 against 75
- Documentation and upkeep, 65 against 45
Watch for
No safety evaluation figures published
gpt-oss-120b BB
Best for Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support
Ahead on
- Risk, 95 against 70
- Agent interface, 90 against 85
Watch for
Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision
Score by category
| Category | Weight this run | Gemma 4 26B A4B IT | gpt-oss-120b | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 59 | 54 | Gemma 4 26B A4B IT +5 |
| Reach | 25%25 | 90 | 75 | Gemma 4 26B A4B IT +15 |
| Risk | 20%20 | 70 | 95 | gpt-oss-120b +25 |
| Agent interface | 10%10 | 85 | 90 | gpt-oss-120b +5 |
| Documentation and upkeep | 10%10 | 65 | 45 | Gemma 4 26B A4B IT +20 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 72.2/100 | BB 70.2/100 |
Facts side by side
| Fact | Gemma 4 26B A4B IT | gpt-oss-120b |
|---|---|---|
| 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 | no | 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 26B A4B IT
A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.
gpt-oss-120b
A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.
Before you call either
Gemma 4 26B A4B IT
- On the reference machine run
llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images - With Ollama run
ollama run gemma4:26band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM 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 - 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
- Validate tool names and arguments before running them, and check any JSON yourself
gpt-oss-120b
- Start it with
ollama run gpt-oss:120bon the reference machine, the build that fits - On the Strix Halo machine raise the GPU memory limit first with
amd-ttm --set 100, since the default of about 62.8 GiB is below the 78 GB it needs - With vLLM use
vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choiceper vLLM's recipe, or parse raw output with openai-harmony - Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
- Set
Reasoning: lowin the system message for short tasks, medium is the default - Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
- Expect one tool call per turn and loop, don't wait for parallel calls
Questions
Which is better for AI agents, Gemma 4 26B A4B IT or gpt-oss-120b?
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against gpt-oss-120b's 70.2 (BB), and leads in 3 of 5 scored categories. gpt-oss-120b leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-26b-a4b-it.json·/api/v1/tools/gpt-oss-120b.json - From a terminal
anchor compare gemma-4-26b-a4b-it gpt-oss-120b(the CLI) - Over MCP
compare_tools {"a": "gemma-4-26b-a4b-it", "b": "gpt-oss-120b"}at/mcp, no key