# Inkling-Small vs Qwen3.5-4B

> Inkling-Small scores 66.5 (B) to Qwen3.5-4B's 61.2 (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-qwen3-5-4b
- Markdown: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-4b.md (~6,000 tokens)
- Slim: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-4b.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 Qwen3.5-4B's 61.2 (C), and leads in 4 of 5 scored categories. Qwen3.5-4B 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
- Qwen3.5-4B: grade C, 61.2/100, rank #33 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.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:
- Capability, 63 against 53
- Risk, 70 against 55
- Agent interface, 85 against 70
- Documentation and upkeep, 82 against 34

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5

Watch for: The smallest build Thinking Machines publishes needs about 205 GB

### Qwen3.5-4B (C)

Good for: Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU

Ahead on:
- Reach, 85 against 55

Watch for: No quantised build from Alibaba for this size


## Score by category

| Category | Weight | Inkling-Small | Qwen3.5-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 63 | 53 | Inkling-Small +10 |
| Reach | 25% (25 this run) | 55 | 85 | Qwen3.5-4B +30 |
| Risk | 20% (20 this run) | 70 | 55 | Inkling-Small +15 |
| Agent interface | 10% (10 this run) | 85 | 70 | Inkling-Small +15 |
| Documentation and upkeep | 10% (10 this run) | 82 | 34 | Inkling-Small +48 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.5 · B** | **61.2 · C** | |

## Facts side by side

| Fact | Inkling-Small | Qwen3.5-4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Thinking Machines Lab | Alibaba (Qwen team) |
| 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 | 2026-03-02 |
| 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) | 3/5 (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.

**Qwen3.5-4B.** A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.

## 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

### Qwen3.5-4B

1. Smallest start is `ollama run qwen3.5:4b` (Q4_K_M, about 4 GB with context)
2. Full precision `vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported
4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` without thinking
5. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
6. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, Inkling-Small or Qwen3.5-4B?

Inkling-Small scores 66.5 (B) on agent readiness against Qwen3.5-4B's 61.2 (C), and leads in 4 of 5 scored categories. Qwen3.5-4B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling-small", "b": "qwen3-5-4b"}`. From a terminal: `anchor compare inkling-small qwen3-5-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-small.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.json

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