# Inkling-Small vs Qwen3-30B-A3B-Instruct-2507

> Inkling-Small scores 66.5 (B) to Qwen3-30B-A3B-Instruct-2507's 56.6 (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-qwen3-30b-a3b-instruct-2507
- Markdown: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-30b-a3b-instruct-2507.md (~6,750 tokens)
- Slim: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-30b-a3b-instruct-2507.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-30b-a3b-instruct-2507.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 Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 4 of 5 scored categories. Qwen3-30B-A3B-Instruct-2507 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
- Qwen3-30B-A3B-Instruct-2507: grade C, 56.6/100, rank #74 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-30b-a3b-instruct-2507.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 37
- Risk, 70 against 55
- Agent interface, 85 against 75
- Documentation and upkeep, 82 against 26

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5

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

### Qwen3-30B-A3B-Instruct-2507 (C)

Good for: Agents that want a fast, capable instruct model on one 24 GB GPU or a 32 GB Mac, with a plain licence and long context.

Ahead on:
- Reach, 90 against 55

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | Inkling-Small | Qwen3-30B-A3B-Instruct-2507 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 63 | 37 | Inkling-Small +26 |
| Reach | 25% (25 this run) | 55 | 90 | Qwen3-30B-A3B-Instruct-2507 +35 |
| Risk | 20% (20 this run) | 70 | 55 | Inkling-Small +15 |
| Agent interface | 10% (10 this run) | 85 | 75 | Inkling-Small +10 |
| Documentation and upkeep | 10% (10 this run) | 82 | 26 | Inkling-Small +56 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.5 · B** | **56.6 · C** | |

## Facts side by side

| Fact | Inkling-Small | Qwen3-30B-A3B-Instruct-2507 |
| --- | --- | --- |
| 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 | 2025-09-17 |
| 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) | 2.8/5 (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.

**Qwen3-30B-A3B-Instruct-2507.** A quick general-purpose cousin, with 3.3B of 30.5B parameters active per token, for instruction following, tool calling and long documents without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

## 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

### Qwen3-30B-A3B-Instruct-2507

1. Quickest start is `ollama run qwen3:30b-instruct`, 19 GB. Plain `qwen3:30b` and `qwen3:30b-a3b` are the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`, 61 GB of weights fits the GPU share
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Leave config.json as it is unless you need more than 262,144 tokens, the 1M mode swaps in config_1m.json and runs one sequence at a time
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, Inkling-Small or Qwen3-30B-A3B-Instruct-2507?

Inkling-Small scores 66.5 (B) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 4 of 5 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-30b-a3b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-30b-a3b-instruct-2507.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling-small", "b": "qwen3-30b-a3b-instruct-2507"}`. From a terminal: `anchor compare inkling-small qwen3-30b-a3b-instruct-2507`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-small.json and https://www.anchorterminal.com/api/v1/tools/qwen3-30b-a3b-instruct-2507.json

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