Head to head · Open-weight models you run yourself · October 2026 research run
Apertus v1.5 8B vs Gemma 4 E2B IT
Apertus v1.5 8B 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. 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 Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.
Ahead on
- Risk, 75 against 70
- Documentation and upkeep, 84 against 75
Watch for
No released runtime supports it, only Swiss AI's forks of vLLM and Transformers
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 40
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5
Watch for
No safety evaluation figures published
Score by category
| Category | Weight this run | Apertus v1.5 8B | Gemma 4 E2B IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 95 | Gemma 4 E2B IT +55 |
| Risk | 20%20 | 75 | 70 | Apertus v1.5 8B +5 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 84 | 75 | Apertus v1.5 8B +9 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 60.4/100 |
Facts side by side
| Fact | Apertus v1.5 8B | Gemma 4 E2B IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Google DeepMind |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-14 | 2026-07-20 |
| Terms last updated | 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 | 2.8/5 (4) | 3.5/5 (4) |
Verdicts
Apertus v1.5 8B
A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.
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
Apertus v1.5 8B
- Accept the terms on the card and set HF_TOKEN before downloading
- Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
- Serve with
--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertusfor tools, and leave thinking off when you need tools - For thinking, set
--reasoning-parser apertusand--default-chat-template-kwargs.enable_thinking true, and budget max tokens generously - Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
- Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
- If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent
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, Apertus v1.5 8B or Gemma 4 E2B IT?
Apertus v1.5 8B 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.
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Machine-readable
- This page as Markdown
/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/apertus-v1-5-8b.json·/api/v1/tools/gemma-4-e2b-it.json - From a terminal
anchor compare apertus-v1-5-8b gemma-4-e2b-it(the CLI) - Over MCP
compare_tools {"a": "apertus-v1-5-8b", "b": "gemma-4-e2b-it"}at/mcp, no key