# Inkling 2 Small vs Qwen3-4B-Instruct-2507 > Inkling 2 Small scores 58.8 (C) to Qwen3-4B-Instruct-2507's 54.5 (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-2-small-vs-qwen3-4b-instruct-2507 - Markdown: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.md (~10,250 tokens) - Slim: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.min.md (~730 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-12 Inkling 2 Small scores 58.8 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 3 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach. Both do open-weight models you run yourself. - Inkling 2 Small: grade C, 58.8/100, rank #102 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-2-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json - Qwen3-4B-Instruct-2507: grade C, 54.5/100, rank #131 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-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 6472 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Inkling 2 Small (C) Good for: Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image. Ahead on: - Risk, 65 against 55 - Agent interface, 85 against 75 - Documentation and upkeep, 67 against 26 Watch for: The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory ### Qwen3-4B-Instruct-2507 (C) Good for: Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context. Ahead on: - Reach, 90 against 40 Watch for: No safety results, prompt-injection guidance or security contact ## Why the edge ### Reach: Qwen3-4B-Instruct-2507 +50 - Inkling 2 Small (40): Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line. Runtimes 0, no released version of the six runs it; vLLM and SGLang serve it from their own images and Transformers has it on its main branch only. Builds 15, two precisions from the publisher (NVFP4 and MXFP8). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and Together is live on Hugging Face. 0 + 0 + 15 + 10 + 10 + 5 = 40. - Qwen3-4B-Instruct-2507 (90): Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3 architecture, Ollama's released build pins a llama.cpp that has it, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 0, on 4 November 2026 the Hub's provider mapping showed only Featherless AI with status error, Nscale was gone and Alibaba's Model Studio doesn't list it. 30 + 25 + 10 + 15 + 10 + 0 = 90. ### Documentation and upkeep: Inkling 2 Small +41 - Inkling 2 Small (67): Model card 17 of 20, everything but a dated knowledge cut-off. Training data 15, an EU training-content summary with sources, mix, an October 2026 collection cut-off and filtering. Last change 20, the files were uploaded on 12 November 2026. Responsiveness 0, the repository has no questions yet, which reads as none answered. Version policy 5, a first release. File health 10, the files agree and no broken-file report waits. 17 + 15 + 20 + 0 + 5 + 10 = 67. - Qwen3-4B-Instruct-2507 (26): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files last changed on 17 September 2025 (Hub lastModified on 4 November), more than a year ago. Responsiveness 0, counted by script on 4 November, Qwen answered none of the 20 questions and reports. Version policy 5, the older Qwen3-4B stays in its own repository without a note. File health 5, the files agree with the card in substance but reports wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26. ### Risk: Inkling 2 Small +10 - Inkling 2 Small (65): File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT, FORTRESS, HoneyBench and DistressBench results and described outside red-teaming, but nothing an outside party published or a harness to rerun. Agent guidance 5, the card names safeguards to add but doesn't say what its refusals cover, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent. 25 + 15 + 10 + 5 + 5 + 5 = 65. - Qwen3-4B-Instruct-2507 (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3 still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55. ### Agent interface: Inkling 2 Small +10 - Inkling 2 Small (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back for this model, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to xhigh, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens. 20 + 25 + 20 + 0 + 10 + 10 = 85. - Qwen3-4B-Instruct-2507 (75): Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75. ## Score by category | Category | Weight | Inkling 2 Small | Qwen3-4B-Instruct-2507 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 90 | Qwen3-4B-Instruct-2507 +50 | | Risk | 20% (20 this run) | 65 | 55 | Inkling 2 Small +10 | | Agent interface | 10% (10 this run) | 85 | 75 | Inkling 2 Small +10 | | Documentation and upkeep | 10% (10 this run) | 67 | 26 | Inkling 2 Small +41 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **58.8 · C** | **54.5 · C** | | ## Facts side by side | Fact | Inkling 2 Small | Qwen3-4B-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 | 2026-11-12 | 2025-09-17 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 3/5 (4) | ## How the scores have moved ### Inkling 2 Small - +6.7 on 12 November 2026 (D to C): product ### Qwen3-4B-Instruct-2507 - -1.2 on 4 November 2026: product ## Verdicts **Inkling 2 Small.** Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change. **Qwen3-4B-Instruct-2507.** A small dense instruct model for instruction following, short tool-calling jobs and long documents, with no thinking step. Its 4-bit builds need about 3 GB under Apache 2.0, but no live inference provider was found for it on 4 November 2026. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published. ## Before you call either ### Inkling 2 Small 1. No released runtime runs it yet. Serve the NVFP4 checkpoint from SGLang's dev-inkling-2-small image with the cookbook's recipe for your hardware, or from vLLM's vllm/vllm-openai:inkling-2-small image 2. In vLLM pass `--reasoning-parser inkling --enable-auto-tool-choice --tool-call-parser inkling`. In SGLang the tool-call parser is `inkling2` and the reasoning parser `inkling` 3. Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet 4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99 5. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens 6. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error 7. Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part 8. Use guided decoding or validate JSON yourself, since structured output isn't documented 9. Without the hardware, call it on Tinker's OpenAI-compatible endpoint with a Tinker key, at $0.30 input and $1.00 output per million tokens as read on 6 November 2026 10. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal ### Qwen3-4B-Instruct-2507 1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one 2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes` 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. Drop the context to 32,768 tokens if memory runs out, as the card suggests 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 2 Small or Qwen3-4B-Instruct-2507? Inkling 2 Small scores 58.8 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 3 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling-2-small", "b": "qwen3-4b-instruct-2507"}`. 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[Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b.md) - [Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2.md) - [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.md) - [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b.md) - [Qwen3-4B-Instruct-2507 vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp