# Apertus v1.5 8B vs occamy-1.0 > Apertus v1.5 8B and occamy-1.0 score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0 - Markdown: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.md (~5,950 tokens) - Slim: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.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 and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 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 - occamy-1.0: grade C, 61/100, rank #51 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.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 55 - Agent interface, 85 against 70 - Documentation and upkeep, 84 against 74 Watch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers ### occamy-1.0 (C) Good for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac. Ahead on: - Reach, 85 against 40 Watch for: No safety results, prompt-injection guidance or security contact ## Score by category | Category | Weight | Apertus v1.5 8B | occamy-1.0 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 85 | occamy-1.0 +45 | | Risk | 20% (20 this run) | 75 | 55 | Apertus v1.5 8B +20 | | Agent interface | 10% (10 this run) | 85 | 70 | Apertus v1.5 8B +15 | | Documentation and upkeep | 10% (10 this run) | 84 | 74 | Apertus v1.5 8B +10 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Apertus v1.5 8B | occamy-1.0 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Accio (Accio-Lab) | | 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 | no | | Last release | 2026-10-14 | 2026-10-02 | | 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 (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. **occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own. ## 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 ### occamy-1.0 1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later 2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism 3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says 4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5 5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata 6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head ## Questions ### Which is better for AI agents, Apertus v1.5 8B or occamy-1.0? Apertus v1.5 8B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "apertus-v1-5-8b", "b": "occamy-1-0"}`. From a terminal: `anchor compare apertus-v1-5-8b occamy-1-0` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json ## Other comparisons with Apertus v1.5 8B or occamy-1.0 - [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md) - [Agents-A1-4B vs occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.md) - [Apertus v1.5 8B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.md) - [Apertus v1.5 8B vs BigBang-v1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.md) - [Apertus v1.5 8B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.md) - [Apertus v1.5 8B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.md) - [Apertus v1.5 8B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.md) - 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[occamy-1.0 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.md) - [occamy-1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.md) - [occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.md) - [occamy-1.0 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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