# Apertus v1.5 8B vs DiffusionGemma 26B A4B IT

> Apertus v1.5 8B scores 61 (C) to DiffusionGemma 26B A4B IT's 50 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it
- Markdown: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Apertus v1.5 8B scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 3 of 4 scored categories. DiffusionGemma 26B A4B IT leads on reach. Both do open-weight models you run yourself.

- Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json
- DiffusionGemma 26B A4B IT: grade D, 50/100, rank #94 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Apertus v1.5 8B (C)

Good 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
- Agent interface, 85 against 70
- Documentation and upkeep, 84 against 60

Watch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers

### DiffusionGemma 26B A4B IT (D)

Good for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4

Ahead on:
- Reach, 50 against 40

Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band


## Score by category

| Category | Weight | Apertus v1.5 8B | DiffusionGemma 26B A4B IT | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 40 | 50 | DiffusionGemma 26B A4B IT +10 |
| Risk | 20% (20 this run) | 75 | 70 | Apertus v1.5 8B +5 |
| Agent interface | 10% (10 this run) | 85 | 70 | Apertus v1.5 8B +15 |
| Documentation and upkeep | 10% (10 this run) | 84 | 60 | Apertus v1.5 8B +24 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **50 · D** | |

## Facts side by side

| Fact | Apertus v1.5 8B | DiffusionGemma 26B A4B 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 | 2026-07-15 |
| 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) | 2.5/5 (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.

**DiffusionGemma 26B A4B IT.** A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.

## Before you call either

### Apertus v1.5 8B

1. Accept the terms on the card and set HF_TOKEN before downloading
2. 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
3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools
4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously
5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent

### DiffusionGemma 26B A4B IT

1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions
2. In Transformers, install `transformers>=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype="auto"`; generation_config.json already holds the recommended sampler
3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `<|think|>` at the start of the system prompt, read the reasoning between `<|channel>thought` and `<channel|>`, and drop it from history before the next turn
4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself
5. Streaming arrives one 256-token canvas at a time, not token by token
6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs

## Questions

### Which is better for AI agents, Apertus v1.5 8B or DiffusionGemma 26B A4B IT?

Apertus v1.5 8B scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 3 of 4 scored categories. DiffusionGemma 26B A4B IT leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "apertus-v1-5-8b", "b": "diffusiongemma-26b-a4b-it"}`. From a terminal: `anchor compare apertus-v1-5-8b diffusiongemma-26b-a4b-it`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json

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