# Inkling 2 Small vs Qwen3-Coder-Next > Inkling 2 Small scores 58.8 (C) to Qwen3-Coder-Next's 57.6 (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-2-small-vs-qwen3-coder-next - Markdown: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next.md (~9,900 tokens) - Slim: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next.min.md (~730 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next.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-Coder-Next's 57.6 (C), and leads in 3 of 4 scored categories. Qwen3-Coder-Next 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-Coder-Next: grade C, 57.6/100, rank #108 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.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 50 - Agent interface, 85 against 75 - Documentation and upkeep, 67 against 41 Watch for: The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory ### Qwen3-Coder-Next (C) Good for: Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine. Ahead on: - Reach, 85 against 40 Watch for: No LICENSE file anywhere and a broken licence link on the card ## Why the edge ### Reach: Qwen3-Coder-Next +45 - 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-Coder-Next (85): Memory earns 15 of 30, since Alibaba's own Q4_K_M GGUF is 48.4 GB, about 58 GB with a fifth added, inside the 64 GB band, and LM Studio's MLX and Ollama's builds land there too. Runtimes reach the 25 cap on released vLLM (v0.33.0), SGLang (v0.5.23), llama.cpp, Ollama (its library entry, and llama.cpp b11963 pinned by Ollama 0.40.14 has qwen3next), LM Studio and Transformers. Alibaba's FP8 and GGUF builds give builds 15. The Apache 2.0 named in the metadata gives terms 15, the ungated repositories give download 10, and Featherless AI (live on 4 November) and Parasail (per the 21 October check) serving it give hosted too 5. 15+25+15+15+10+5 = 85. ### Documentation and upkeep: Inkling 2 Small +26 - 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-Coder-Next (41): The card covers four of the seven items and misses limitations, languages and a cut-off (11). The technical report names the data sources, the cut-off and the filtering with some sizes (15). The files last changed on 3 February 2026 (4 February in the GGUF repository), 274 days ago, within a year (5). Alibaba's staff answered none of the 40 discussions; the one reply from a Qwen organisation member is not the publisher's (0), and GitHub issues couldn't be counted. It's a first release (5), and reports of a broken shipped parser and the missing licence wait unanswered (5). 11+15+5+0+5+5 = 41. ### Risk: Inkling 2 Small +15 - 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-Coder-Next (50): Safetensors and GGUF only, with the two shipped parsers outside the documented loading path, gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Licence risk loses 5 because the card's licence link is broken and no LICENSE file exists anywhere. Safety evaluation, agent guidance and a security programme score 0, since the report's cybersecurity appendix measures skills rather than safe behaviour, nothing guides agent use and no reporting route was found. ### 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-Coder-Next (75): The template ships in the repository (20). Tool calling is the model's strength, and Alibaba documents how tools are declared, names and ships the qwen3_coder parser and describes the format in its report (25), with several calls per turn in a model trained on 21 harness formats (20). Context is 262,144 tokens native (10). Structured output scores 0, as nothing documents JSON output, and reasoning control scores 0 because the model has no thinking mode to control. ## Score by category | Category | Weight | Inkling 2 Small | Qwen3-Coder-Next | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 85 | Qwen3-Coder-Next +45 | | Risk | 20% (20 this run) | 65 | 50 | Inkling 2 Small +15 | | Agent interface | 10% (10 this run) | 85 | 75 | Inkling 2 Small +10 | | Documentation and upkeep | 10% (10 this run) | 67 | 41 | Inkling 2 Small +26 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **58.8 · C** | **57.6 · C** | | ## Facts side by side | Fact | Inkling 2 Small | Qwen3-Coder-Next | | --- | --- | --- | | 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 | 2026-02-03 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.8/5 (4) | ## How the scores have moved ### Inkling 2 Small - +6.7 on 12 November 2026 (D to C): product ### Qwen3-Coder-Next No score change since it was listed. ## 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-Coder-Next.** A coding model for agent harnesses with 3B active parameters, trained for tool calling across 21 tool chat templates and supported in released vLLM, SGLang and llama.cpp. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine. Its files haven't changed since February 2026, 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-Coder-Next 1. To start with Ollama, run `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files per the 21 October reading) 2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072` 3. Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36 4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start 5. There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content 6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40 7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf 8. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, Inkling 2 Small or Qwen3-Coder-Next? Inkling 2 Small scores 58.8 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 3 of 4 scored categories. Qwen3-Coder-Next leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling-2-small", "b": "qwen3-coder-next"}`. 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