# Agents-A1-4B vs Inkling-Small

> Inkling-Small scores 66.5 (B) to Agents-A1-4B'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/agents-a1-4b-vs-inkling-small
- Markdown: https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.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 Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. Agents-A1-4B leads on reach. Both do open-weight models you run yourself.

- Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json
- 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
- 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

### Agents-A1-4B (C)

Good for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence

Ahead on:
- Reach, 90 against 55

Watch for: The base isn't declared, and the technical report doesn't cover the 4B

### 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 55
- Agent interface, 85 against 70
- Documentation and upkeep, 82 against 43

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


## Score by category

| Category | Weight | Agents-A1-4B | Inkling-Small | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 55 | Agents-A1-4B +35 |
| Risk | 20% (20 this run) | 55 | 70 | Inkling-Small +15 |
| Agent interface | 10% (10 this run) | 70 | 85 | Inkling-Small +15 |
| Documentation and upkeep | 10% (10 this run) | 43 | 82 | Inkling-Small +39 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **66.5 · B** | |

## Facts side by side

| Fact | Agents-A1-4B | Inkling-Small |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | InternScience (Shanghai AI Laboratory) | Thinking Machines Lab |
| 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 | 2026-07-14 | 2026-07-31 |
| 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) | 3.5/5 (4) |

## Verdicts

**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.

**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.

## Before you call either

### Agents-A1-4B

1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector
2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work
3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14
4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them
5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention
6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding
7. Validate JSON output yourself, since structured output isn't documented

### 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

## Questions

### Which is better for AI agents, Agents-A1-4B or Inkling-Small?

Inkling-Small scores 66.5 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. Agents-A1-4B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "agents-a1-4b", "b": "inkling-small"}`. From a terminal: `anchor compare agents-a1-4b inkling-small`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/inkling-small.json

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