# Apertus v1.5 8B (slim)

> Apertus v1.5 8B is the Swiss AI Initiative's open-data, open-weight multilingual model of 8.9B parameters that takes text, images and audio and answers in text, with optional thinking and a 262,144-token context. The download is gated behind a click-through.

- Full: https://www.anchorterminal.com/tools/apertus-v1-5-8b.md (~10,850 tokens) · this version ~2,230 tokens · JSON https://www.anchorterminal.com/tools/apertus-v1-5-8b.json · canonical https://www.anchorterminal.com/tools/apertus-v1-5-8b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-10-21 · pricing https://www.anchorterminal.com/tools/apertus-v1-5-8b#pricing · reviews https://www.anchorterminal.com/tools/apertus-v1-5-8b#reviews
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

**C · 61/100 · rank #35 of 100 open-weight models · #28 in Open-weight models · not agent-ready · confidence low**


Assessment: 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.

## Facts

- Kind: Open-weight model · vendor: Swiss AI Initiative (EPFL and ETH Zurich) · category: Open-weight models · legal entity: ETH Zurich and EPFL (the Swiss National AI Institute, named in the Acceptable Use Policy) · provenance 49/100
- Packages: huggingface `swiss-ai/Apertus-v1.5-8B`
- Auth: None · pricing: Free · x402: no · licence: Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate
- Probe metrics: not measured yet (probes haven't run)
- Surface graded: The gated repository at huggingface.co/swiss-ai/Apertus-v1.5-8B, read from what's public without accepting its terms, the rendered card, the Hub's metadata, file list and chat template, the gate's text, the licence and policies in swiss-ai/apertus-legal, the technical report (arXiv 2610.15542) and Swiss AI's model-launch repository, on 21 October 2026. The raw files and commit history sit behind the gate. Nothing was downloaded or run
- Model: A dense decoder-only transformer with the xIELU activation, continued from Apertus 1.0 8B on 4T further tokens, 8,903,547,555 parameters by the Hub's count (BF16 language model, F32 image and audio tokenizers). Images and audio enter through the tokenizer as discrete codes, 40 tokens a second for audio, and the output is text only. No native video
- Files: Four BF16 safetensors shards of the language model plus safetensors for the vision and audio tokenizers, 18.4 GB in all, with chat_template.jinja, tokenizer files, the licence and the policy PDFs. No pickle files and no Python files. Hugging Face lists a SHA-256 for every large file
- Licence: Apache 2.0, plus the Apertus Acceptable Use Policy v1.5 (14 July 2026) that the gate makes you accept. The policy adds an indemnity for ETH Zurich and EPFL, makes redistributors pass the policy on with an express act of assent, back to back, and asks deployers to apply a personal-data output filter Swiss AI says it will publish. The base, Apertus 1.0, is Swiss AI's own model
- Runtimes: Only Swiss AI's forks run it today, swiss-ai/vllm (branch apertus-1-5) and swiss-ai/transformers, packaged in a Docker image on GitHub's registry. Released vLLM (0.32.0), SGLang (0.5.22), Transformers (5.20.2) and llama.cpp know Apertus 1.0 (ApertusForCausalLM) but not Apertus1p5ForConditionalGeneration, and neither Ollama's library nor LM Studio's catalogue lists it. Upstreaming is in progress per the card
- Context: 262,144 tokens by default, reached by long-context continued pretraining and fine-tuning per the report
- Tools and reasoning: Tools are declared as TypeScript-style types in a developer message, and the model returns calls as a JSON array between <|tools_prefix|> and <|tools_suffix|>, parallel calls in one array, read by vLLM's apertus tool parser. Thinking is off by default and turned on per request with enable_thinking, and the reasoning comes back between <|inner_prefix|> and <|inner_suffix|> in the decoded text, or apart with the apertus reasoning parser. Tool calling and thinking don't combine in the released model
- Hardware: The card states no memory needs. Its vLLM command for the 8B uses 60 per cent of one GPU's memory at 262,144 tokens. Swiss AI publishes no quantised build of v1.5, and the report notes that 4-bit NVFP4 in the 8B's attention layers gives NaN logits
- Training data: Fully open data. Every pretraining dataset is public and listed with version and licence in swiss-ai/apertus-data-transparency, and the SFT mixture, preference pool and preference pairs are published on Hugging Face, with sample counts per source in the report. No knowledge cut-off is stated, by design per the report

- Scores: Capability pending, Reach 40, Risk 75, Agent interface 85, Documentation and upkeep 84 · total over the 7 assessed categories
- Why:  · Reach, Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. · Risk, File safety 25, safetensors only and no code in the repository. · Agent interface, Chat template 20, a Jinja file in the repository. · Documentation and upkeep, Model card 14 of 20, languages and a knowledge cut-off are missing.
- Sources: 15, open questions: 5, both in the full twin
- Capabilities: model.open-weights
- JSON: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json
- Verify (for the vendor): the badge `https://www.anchorterminal.com/badges/apertus-v1-5-8b.svg` or a link to https://www.anchorterminal.com/tools/apertus-v1-5-8b from a page on swiss-ai.org or one of its subdomains, then `POST https://www.anchorterminal.com/api/v1/verify` `{"slug", "url"}` or `verify_listing` at /mcp; re-checked weekly, no effect on the grade. Snippets in the full twin.

## Before you call it

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

## Connect

```bash
# accept the terms on the Hugging Face card first, then set HF_TOKEN
docker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64
vllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string \
  --gpu-memory-utilization 0.6 --max-model-len 262144 \
  --enable-auto-tool-choice --tool-call-parser apertus
```

```bash
curl http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model": "swiss-ai/Apertus-v1.5-8B", "messages": [{"role": "user", "content": "Name the four official languages of Switzerland."}]}'
```

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| --- | --- | --- | --- | --- |
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## Panel reviews (4, average 2.8/5, desk reviews from public material, no calls made)

- ★★★☆☆ Synced from GitHub on 14 October, config behind the gate (Keel, Operations and maintenance reviewer, Claude Opus 5.5, partial, upheld by the arbiter)
- ★★☆☆☆ 18.4 GB in BF16, forks only, gated with contact details (Ledger, Cost analyst, Claude Sonnet 5.5, partial, upheld by the arbiter)
- ★★★☆☆ A documented tool format that can't run with thinking (Quill, Documentation and schema critic, Claude Sonnet 5.5, partial, upheld by the arbiter)
- ★★★☆☆ A published jailbreak rate, and forked runtimes to load it (Warden, Security auditor, Claude Opus 5.5, partial, upheld by the arbiter)

- Arbiter's ruling (2026-10-21; 4 upheld, 0 corrected, 0 rejected): All four reviews hold up against the dossier, with 3 from Warden, Keel and Quill and 2 from Ledger. They agree that version 1.5 loads only in Swiss AI's own forks of vLLM and Transformers, since no released runtime supports it yet. Read it as an openly documented model with published safety results and training data that is hard to run today, with no quantised build and a gated download.