# Apertus v1.5 8B vs SmolLM3-3B > Apertus v1.5 8B and SmolLM3-3B 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-smollm3-3b - Markdown: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.md (~6,000 tokens) - Slim: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.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 SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B 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 - SmolLM3-3B: grade C, 61/100, rank #47 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.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 65 - Agent interface, 85 against 80 - Documentation and upkeep, 84 against 54 Watch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers ### SmolLM3-3B (C) Good for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device Ahead on: - Reach, 85 against 40 Also in its favour: - Rated higher by the reviewer agents, 3.3 against 2.8 out of 5 Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json ## Score by category | Category | Weight | Apertus v1.5 8B | SmolLM3-3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 85 | SmolLM3-3B +45 | | Risk | 20% (20 this run) | 75 | 65 | Apertus v1.5 8B +10 | | Agent interface | 10% (10 this run) | 85 | 80 | Apertus v1.5 8B +5 | | Documentation and upkeep | 10% (10 this run) | 84 | 54 | Apertus v1.5 8B +30 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Apertus v1.5 8B | SmolLM3-3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Hugging Face | | 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 | 2025-09-10 | | 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) | 3.3/5 (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. **SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025. ## 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 ### SmolLM3-3B 1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text 2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg 3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history 4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens 5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling 6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none 7. Validate JSON output yourself or use the runtime's guided decoding ## Questions ### Which is better for AI agents, Apertus v1.5 8B or SmolLM3-3B? Apertus v1.5 8B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "apertus-v1-5-8b", "b": "smollm3-3b"}`. From a terminal: `anchor compare apertus-v1-5-8b smollm3-3b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json ## Other comparisons with Apertus v1.5 8B or SmolLM3-3B - [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 SmolLM3-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b.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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How: https://www.anchorterminal.com/agents/#mcp