Apertus 1.5 70B
A solid pick for self-hosting and full data control.
Overview
Apertus 1.5 70B was released on 24 July 2026. It continues the training of Apertus 1.0 with 2 trillion additional tokens and adds image and audio understanding, an optional reasoning mode and a 262,144-token context, four times that of 1.0. It is one of the few models where everything is published: weights, training data, recipes and intermediate checkpoints, under the Apache 2.0 licence with an acceptable use policy to accept at download. The initiative says it was developed with Swiss data protection and copyright law and the AI Act transparency obligations in mind. For production, the publisher provides a dedicated vLLM image; the model has no built-in output filter, plan for one on your side.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- No documented EU residency
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- you handle sensitive data with no EU option
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
13/19 criteria documented · 68 % · 7 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/apertus-1-5-70b)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Not documented |
| Data processing agreement (DPA) | Not documented |
| Transfer outside the EU | Not documented |
| Training on your data | Not documented |
| Configurable retention | Not documented |
| Zero Data Retention | Not documented |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
4/8 documented · 2/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 39.9 GB | 46 GB | 64GB Mac / dual 24GB GPUs |
| Q8 · balanced | 74.9 GB | 84 GB | 96-128GB Mac Studio / 4× GPUs |
| FP16 · max quality | 140 GB | 156 GB | Server GPU infra (H100/A100…) |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve swiss-ai/Apertus-v1.5-70B \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |