# Inkling-Small vs SmolLM3-3B

> Inkling-Small scores 66.5 (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-small-vs-smollm3-3b
- Markdown: https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.md (~6,000 tokens)
- Slim: https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/inkling-small-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-Small scores 66.5 (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-Small: grade B, 66.5/100, rank #14 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.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-Small (B)

Good for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.

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 205 GB

### 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 55

Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json


## Score by category

| Category | Weight | Inkling-Small | SmolLM3-3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 85 | SmolLM3-3B +30 |
| Risk | 20% (20 this run) | 70 | 65 | Inkling-Small +5 |
| Agent interface | 10% (10 this run) | 85 | 80 | Inkling-Small +5 |
| Documentation and upkeep | 10% (10 this run) | 82 | 54 | Inkling-Small +28 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.5 · B** | **61 · C** | |

## Facts side by side

| Fact | Inkling-Small | 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 | 2026-07-31 | 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 | 3.5/5 (4) | 3.3/5 (4) |

## Verdicts

**Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. 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-Small

1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4
2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself
3. 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 and SGLang's cookbook list xhigh
4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens
5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card
7. Use guided decoding or validate JSON yourself, since structured output isn't documented
8. 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-Small or SmolLM3-3B?

Inkling-Small scores 66.5 (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-small-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling-small", "b": "smollm3-3b"}`. From a terminal: `anchor compare inkling-small smollm3-3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-small.json and https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json

## Other comparisons with Inkling-Small or SmolLM3-3B

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