# Inkling vs SmolLM3-3B > Inkling scores 64.2 (B) to SmolLM3-3B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b - Markdown: https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.md (~5,700 tokens) - Slim: https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/inkling-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 Inkling scores 64.2 (B) on agent readiness against SmolLM3-3B's 61 (C), and leads in 3 of 4 scored categories. SmolLM3-3B leads on reach. Both do open-weight models you run yourself. - Inkling: grade B, 64.2/100, rank #20 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling.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 ### Inkling (B) Good for: Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves. Ahead on: - Risk, 70 against 65 - Agent interface, 85 against 80 - Documentation and upkeep, 82 against 54 Watch for: The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation ### 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 50 Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json ## Score by category | Category | Weight | Inkling | SmolLM3-3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 85 | SmolLM3-3B +35 | | Risk | 20% (20 this run) | 70 | 65 | Inkling +5 | | Agent interface | 10% (10 this run) | 85 | 80 | Inkling +5 | | Documentation and upkeep | 10% (10 this run) | 82 | 54 | Inkling +28 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **64.2 · B** | **61 · C** | | ## Facts side by side | Fact | Inkling | SmolLM3-3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Thinking Machines Lab | Hugging Face | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-23 | 2025-09-10 | | Terms last updated | no document linked | 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 | 3/5 (4) | 3.3/5 (4) | ## Verdicts **Inkling.** Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results. **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 ### Inkling 1. Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it 2. On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling` 3. Use the inkling tool parser, not Hermes, and the inkling reasoning parser 4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh 5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error 6. Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card 7. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal ### 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, Inkling or SmolLM3-3B? Inkling scores 64.2 (B) on agent readiness against SmolLM3-3B's 61 (C), and leads in 3 of 4 scored categories. SmolLM3-3B leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling", "b": "smollm3-3b"}`. From a terminal: `anchor compare inkling smollm3-3b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling.json and https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json ## Other comparisons with Inkling or SmolLM3-3B - [Agents-A1-4B vs Inkling](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.md) - [Agents-A1-4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-smollm3-3b.md) - [Apertus v1.5 8B vs Inkling](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.md) - [Apertus v1.5 8B vs SmolLM3-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.md) - [Apodex-1.1-mini vs Inkling](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling.md) - [Apodex-1.1-mini vs SmolLM3-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-smollm3-3b.md) - [BigBang-v1 vs Inkling](https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling.md) - [BigBang-v1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b.md) - 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[SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp