{ "data": { "a": { "slug": "hy-memory-30b-a3b", "name": "Hy-Memory-30B-A3B", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "open-weights", "summary": "Hy-Memory-30B-A3B is Tencent Hunyuan's open-weight memory writer, a 30B mixture of experts with about 3B active, trained to decide what an agent's memory framework keeps and updates inside Mem0, MemOS and Hy-Memory, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json", "dns": "hy-memory-30b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-GGUF" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main, FP8 and GGUF repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 60.1 GB for the BF16 files, 30.6 GB in FP8 or 18.2 GB for Tencent's Q4_K_M GGUF (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "agent-memory", "specialist", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1524, "categoryRank": 54, "methodology": "0.8", "run": "2026-10-01", "scores": { "interface": 65, "reach": 85, "risk": 60, "upkeep": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.", "bestFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card", "strengths": [ "Made for one job, deciding what a memory framework stores and updates, and tested in four frameworks", "Fits a 24 GB card from Tencent's own Q4_K_M GGUF", "Runs in llama.cpp, vLLM, SGLang and Transformers from their releases", "Plain Apache 2.0, ungated, safetensors and GGUF only", "Tencent replied to three of the four discussions" ], "weaknesses": [ "Not a general assistant or a tool caller", "No limitations, safety results or guidance on untrusted text in what it reads", "Training data isn't described, and the card gives no context length", "The results are on a 20% subsample, run by Tencent", "No inference provider, so it has to be run locally" ], "agentNotes": [ "Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user", "Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does", "Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does", "Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented", "Treat what it stores as unverified. It keeps what a conversation says, true or not" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 65, "reach": 85, "risk": 60, "upkeep": 68 }, "provenanceScore": 75, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja\n# or, with vLLM, as the card names it:\nvllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"hy-memory\", \"temperature\": 0, \"messages\": [{\"role\": \"system\", \"content\": \"Extract explicit user facts and preferences. Return only a JSON object with a memories list of strings.\"}, {\"role\": \"user\", \"content\": \"I live in Seattle and I am vegetarian.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"no_think\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/hy-memory-30b-a3b" }, "sameCompany": [ "tencent-tokenhub", "hy4", "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "octop", "hy3", "wevisone-vl-2b", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent (Hunyuan)", "builtOn": "Not declared. The architecture is Tencent's hy_v3, as Hy3", "params": "30.1B", "active": "3B", "context": 262144, "goodFor": "The write path of an agent's memory framework. It reads a conversation and decides what to store and how to update it, in Mem0, MemOS or Hy-Memory, in place of a general model.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Hy-Memory-30B-A3B-Q4_K_M", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 18.24, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "The smallest file at 4 bits. Q3_K_M (14.45 GB) and the two files with experts at 1 or 2 bits are below the 4-bit line and are not listed" }, { "name": "Hy-Memory-30B-A3B-Q6_K", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q6_k", "sizeGB": 24.71, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B-FP8", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 30.58, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Hy-Memory-30B-A3B-Q8_0", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 31.99, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 60.13, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja", "runBuild": "Hy-Memory-30B-A3B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "Hy-Memory", "url": "https://www.anchorterminal.com/open-weights/#tencent", "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text, as on Hy3.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, five commits on 15 and 16 October 2026, the uploads, the card and the licence.", "The Hy3 listing found no security.txt on Tencent's domains and records Tencent's Security Response Center as the reporting route. unchecked on 10 November." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.json", "live": { "slug": "hy-memory-30b-a3b", "securityTxt": { "url": "https://tencent.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-14T16:19:02.098306985Z" }, "pages": [ { "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-14T19:07:23.808947078Z", "changedAt": "2026-11-14T19:07:23.808947078Z", "fingerprint": "2afac32d2df7" } ], "updatedAt": "2026-11-14T19:07:23.808947078Z" } }, "answer": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 2 of 4 scored categories. Inkling 2 Small leads on risk and agent interface.", "b": { "slug": "inkling-2-small", "name": "Inkling 2 Small", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "weights", "category": "open-weights", "summary": "Inkling 2 Small is Thinking Machines Lab's open-weight mixture-of-experts model, 594B total and 17B active parameters, taking text and images and returning text, with adjustable thinking effort, published in NVFP4 and MXFP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/inkling-2-small", "markdownUrl": "https://www.anchorterminal.com/tools/inkling-2-small.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling-2-small.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json", "dns": "inkling-2-small.grade.anchorterminal.com", "repo": "https://huggingface.co/thinkingmachines/Inkling-2-Small", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "thinkingmachines/Inkling-2-Small" }, { "registry": "huggingface", "name": "thinkingmachines/Inkling-2-Small-MXFP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-12). Thinking Machines' Model Acceptable Use Policy, linked from the card, says that downloading or using the weights binds you to it.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with Thinking Machines' Model Acceptable Use Policy on top. Running it yourself costs the hardware, 393.5 GB of files for the NVFP4 checkpoint and 622.8 GB for MXFP8, which Thinking Machines puts at 500 GB and 800 GB of GPU memory. Thinking Machines' Tinker serves it as a beta serverless inference API at $0.30 per million input tokens, $0.03 cached and $1.00 output (read 2026-11-06).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-12).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-12" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling-2-small/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "nvfp4", "self-hosted", "new" ], "lastRelease": "2026-11-12", "graded": true, "anchor": { "graded": true, "score": 58.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1524, "categoryRank": 78, "methodology": "0.8", "run": "2026-10-01", "scores": { "interface": 85, "reach": 40, "risk": 65, "upkeep": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-12" }, "negative": 0, "verdict": "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.", "bestFor": "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.", "strengths": [ "Safetensors only, with a checksum on every LFS file in the publisher's own organisation", "Thinking effort set per request from none to xhigh, several tool calls a turn and a 1M-token context", "Two checkpoints from Thinking Machines, NVFP4 and MXFP8, under Apache 2.0 with no gate", "Thinking Machines' tml-renderers package renders prompts and parses tool calls for this model", "A training-content summary with sources, mix and an October 2026 collection cut-off", "Thinking Machines' own Tinker hosts it as beta serverless inference, and the card names seven more API providers" ], "weaknesses": [ "The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory", "No released version of vLLM, SGLang, Transformers, llama.cpp or Ollama runs it on 12 November 2026", "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time", "Safety results are Thinking Machines' own, with no harness to rerun them and no prompt-injection guidance", "Structured output isn't documented, and the card gives no dated knowledge cut-off", "No independent benchmark results were read, so Capability is pending and the total is capped at 61" ], "agentNotes": [ "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", "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`", "Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet", "Pass `reasoning_effort` as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99", "Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens", "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error", "Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part", "Use guided decoding or validate JSON yourself, since structured output isn't documented", "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", "Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.8 } ], "editorialScores": { "interface": 85, "reach": 40, "risk": 65, "upkeep": 67 }, "provenanceScore": 72, "checked": { "categories": { "interface": "2026-11-12", "reach": "2026-11-12", "risk": "2026-11-12", "upkeep": "2026-11-12" }, "last": "2026-11-12", "lastMoved": "2026-11-12", "oldest": "2026-11-12", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# per SGLang's cookbook, the NVFP4 checkpoint on 4 B300 GPUs. Not in an SGLang release yet,\n# so run it inside the lmsysorg/sglang:dev-inkling-2-small image:\nSGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096 sglang serve \\\n --model-path thinkingmachines/Inkling-2-Small --tp 4 --ep 4 \\\n --moe-a2a-backend megamoe --attention-backend fa4 --bf16-gemm-backend torch --dtype bfloat16 \\\n --page-size 128 --grammar-backend xgrammar --enable-multimodal \\\n --reasoning-parser inkling --tool-call-parser inkling2", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"thinkingmachines/Inkling-2-Small\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"low\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/inkling-2-small" }, "sameCompany": [ "inkling", "inkling-small" ], "sameModel": [ "tinker" ], "weights": { "trainedBy": "Thinking Machines Lab", "params": "594B", "active": "17B", "context": 1048576, "goodFor": "Mixture-of-experts generalist for coding, agents and tool use with images in, a 1M-token context and thinking effort set per request.", "runtimes": [], "builds": [ { "name": "Inkling-2-Small", "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small", "who": "publisher", "precision": "nvfp4", "sizeGB": 393.5, "memoryGB": 512, "format": "safetensors", "note": "The main checkpoint, in the precision the model was trained in. Routed experts in NVFP4 for layers 0 to 39 and MXFP8 for layers 40 to 47, per SGLang's cookbook. Thinking Machines states at least 500 GB of GPU memory" }, { "name": "Inkling-2-Small-MXFP8", "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small-MXFP8", "who": "publisher", "precision": "mxfp8", "sizeGB": 622.8, "memoryGB": 1024, "format": "safetensors", "note": "Every routed expert in MXFP8. Thinking Machines states at least 800 GB of GPU memory and recommends the NVFP4 checkpoint. It's the only checkpoint Transformers' main branch loads" } ], "run": "SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096 sglang serve --model-path thinkingmachines/Inkling-2-Small --tp 4 --ep 4 --moe-a2a-backend megamoe --attention-backend fa4 --bf16-gemm-backend torch --dtype bfloat16 --page-size 128 --grammar-backend xgrammar --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling2", "runBuild": "Inkling-2-Small", "checked": "2026-11-12", "minMemoryGB": 512 }, "family": { "lab": "Thinking Machines Lab", "family": "Inkling 2", "url": "https://www.anchorterminal.com/open-weights/#thinking-machines" }, "area": "models", "provenance": { "legalEntity": "Thinking Machines Lab, Inc. (named as provider on its model card page and in its training-content summary)", "domain": "thinkingmachines.ai", "domainRegistered": "", "domainNote": "Not looked up: RDAP isn't requested under the 1 November 2026 rules.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/thinkingmachines/Inkling-2-Small/commits/main", "securityTxt": "valid", "checked": "2026-11-12", "notes": [ "Thinking Machines' model card page links both repositories in the thinkingmachines organisation on Hugging Face, which isn't verified by Hugging Face.", "Nothing is hosted for this listing, so the Apache 2.0 licence and the Model Acceptable Use Policy stand in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 3 commits on 12 November 2026, the upload, a library tag from a merged pull request and a card update.", "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, read on 12 November 2026, and the model card page links a coordinated vulnerability disclosure policy.", "Thinking Machines' terms bar scraping the Services. The founder decided on 6 November 2026 that reading at a person's pace is fine, so six pages were read one at a time on 12 November: the acceptable use policy, the training data page and its summary for this model, the model card page, and the tml-renderers and thinking effort guides. thinkingmachines.ai's robots.txt has no rules, and tinker-docs.thinkingmachines.ai answers 404 for robots.txt." ], "score": 72 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling-2-small.json", "live": { "slug": "inkling-2-small", "probe": { "target": "https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1", "method": "get", "lastAt": "2026-11-13T20:11:37.236512439Z", "lastOk": true, "lastStatus": 404, "lastMs": 439, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 238, "p95ms24h": 467, "samples24h": 245, "samples30d": 1854, "days": [ { "date": "2026-11-06", "probes": 186, "ok": 186 }, { "date": "2026-11-07", "probes": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 243, "ok": 243 }, { "date": "2026-11-10", "probes": 244, "ok": 244 }, { "date": "2026-11-11", "probes": 244, "ok": 244 }, { "date": "2026-11-12", "probes": 245, "ok": 245 }, { "date": "2026-11-13", "probes": 206, "ok": 206 } ], "since": "2026-11-06T00:00:00Z" }, "securityTxt": { "url": "https://thinkingmachines.ai/.well-known/security.txt", "state": "valid", "expires": "2029-07-13T07:00:00.000Z", "checkedAt": "2026-11-14T16:19:22.779006645Z" }, "llmsTxt": { "url": "https://tinker-docs.thinkingmachines.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-13T15:54:36.79283015Z" }, "domain": { "domain": "thinkingmachines.ai", "registered": "2024-07-09", "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai", "checkedAt": "2026-11-08T15:05:10.558189316Z" }, "pages": [ { "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-14T19:07:35.812797752Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "39971da7a686" }, { "url": "https://tinker-docs.thinkingmachines.ai/changelog/", "kind": "changelog", "status": 200, "checkedAt": "2026-11-13T18:32:58.088087394Z", "changedAt": "2026-11-13T18:32:58.088087394Z", "fingerprint": "c56006412e95" }, { "url": "https://thinkingmachines.ai/legal/privacy/", "kind": "privacy", "status": 200, "checkedAt": "2026-11-13T18:32:53.884197991Z", "changedAt": "2026-11-06T13:43:40.660240815Z", "fingerprint": "3c33a4a74c61" }, { "url": "https://thinkingmachines.ai/legal/terms/", "kind": "terms", "status": 200, "checkedAt": "2026-11-13T18:32:56.034092745Z", "changedAt": "2026-11-06T13:43:42.918165055Z", "fingerprint": "08c6ceca5dbb" } ], "updatedAt": "2026-11-14T19:07:35.812797752Z" }, "streak": { "days": 9, "from": "2026-11-06", "sinceWatching": true, "best": 9 } }, "edges": [ { "by": 45, "category": "reach", "leads": "hy-memory-30b-a3b", "name": "Reach", "reasons": { "a": "Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85.", "b": "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." }, "scores": { "a": 85, "b": 40 } }, { "by": 20, "category": "interface", "leads": "inkling-2-small", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.", "b": "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." }, "scores": { "a": 65, "b": 85 } }, { "by": 5, "category": "risk", "leads": "inkling-2-small", "name": "Risk", "reasons": { "a": "File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "b": "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." }, "scores": { "a": 60, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Tencent (Hy team)", "b": "Thinking Machines Lab", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-16", "b": "2026-11-12", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 2 of 4 scored categories. Inkling 2 Small leads on risk and agent interface.", "question": "Which is better for AI agents, Hy-Memory-30B-A3B or Inkling 2 Small?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 40" ], "also": null, "goodFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card", "slug": "hy-memory-30b-a3b", "watchFor": "Not a general assistant or a tool caller" }, { "aheadOn": [ "Risk, 65 against 60", "Agent interface, 85 against 65" ], "also": null, "goodFor": "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.", "slug": "inkling-2-small", "watchFor": "The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory" } ], "history": { "hy-memory-30b-a3b": null, "inkling-2-small": [ { "causes": [ "product" ], "date": "2026-11-12", "delta": 6.7, "from": 52.1, "fromGrade": "D", "to": 58.8, "toGrade": "C" } ] }, 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Small vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-olmo-3-7b-think.json", "title": "Inkling 2 Small vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-122b.json", "title": "Inkling 2 Small vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-35b-a3b.json", "title": "Inkling 2 Small vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-397b.json", "title": "Inkling 2 Small vs Ornith-1.5-397B", "url": 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"https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.json", "title": "Inkling 2 Small vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-0-8b.json", "title": "Inkling 2 Small vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-122b-a10b.json", "title": "Inkling 2 Small vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-2b.json", "title": "Inkling 2 Small vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-397b-a17b.json", "title": "Inkling 2 Small vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-4b.json", "title": "Inkling 2 Small vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-9b.json", "title": "Inkling 2 Small vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-6-27b.json", "title": "Inkling 2 Small vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-6-35b-a3b.json", "title": "Inkling 2 Small vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-8-27b.json", "title": "Inkling 2 Small vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-8-flash-next.json", "title": "Inkling 2 Small vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Inkling 2 Small vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next.json", "title": "Inkling 2 Small vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Inkling 2 Small vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-2b-instruct.json", "title": "Inkling 2 Small vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Inkling 2 Small vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-32b-instruct.json", "title": "Inkling 2 Small vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-4b-instruct.json", "title": "Inkling 2 Small vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-8b-instruct.json", "title": "Inkling 2 Small vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwopus3-8-27b-flash-v2.json", "title": "Inkling 2 Small vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-recursive-flint.json", "title": "Inkling 2 Small vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ripple-65b-preview.json", "title": "Inkling 2 Small vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-sensenova-loop-9b-preview.json", "title": "Inkling 2 Small vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-smollm3-3b.json", "title": "Inkling 2 Small vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-spark-x2-6-4b.json", "title": "Inkling 2 Small vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-step-3-7-flash.json", "title": "Inkling 2 Small vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-swift-1-5-qwen3-8-27b.json", "title": "Inkling 2 Small vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Inkling 2 Small vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-thinkingcap-qwen3-8-27b.json", "title": "Inkling 2 Small vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-unlimited-ocr-2.json", "title": "Inkling 2 Small vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-wevisone-vl-2b.json", "title": "Inkling 2 Small vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-wevisone-vl-4b.json", "title": "Inkling 2 Small vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-zaya1-8b.json", "title": "Inkling 2 Small vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-inkling-2-small.json", "title": "Inkling vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/inkling-vs-inkling-2-small" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 45, "edge": "hy-memory-30b-a3b", "hy-memory-30b-a3b": 85, "inkling-2-small": 40, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "edge": "inkling-2-small", "hy-memory-30b-a3b": 60, "inkling-2-small": 65, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 20, "edge": "inkling-2-small", "hy-memory-30b-a3b": 65, "inkling-2-small": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 1, "edge": "hy-memory-30b-a3b", "hy-memory-30b-a3b": 68, "inkling-2-small": 67, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 2 of 4 scored categories. Inkling 2 Small leads on risk and agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "hy-memory-30b-a3b": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling-2-small", "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling-2-small.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling-2-small.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling-2-small.min.md" }, "markdown": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 2 of 4 scored categories. Inkling 2 Small leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Hy-Memory-30B-A3B: grade C, 61/100, rank #68 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json\n- 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\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6472 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Hy-Memory-30B-A3B (C)\n\nGood for: Agents with an external memory framework that want a local model for the write path on a 24 GB card\n\nAhead on:\n- Reach, 85 against 40\n\nWatch for: Not a general assistant or a tool caller\n\n### Inkling 2 Small (C)\n\nGood 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.\n\nAhead on:\n- Risk, 65 against 60\n- Agent interface, 85 against 65\n\nWatch for: The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory\n\n\n## Why the edge\n\n### Reach: Hy-Memory-30B-A3B +45\n\n- Hy-Memory-30B-A3B (85): Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85.\n- 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.\n\n### Agent interface: Inkling 2 Small +20\n\n- Hy-Memory-30B-A3B (65): Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.\n- 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.\n\n### Risk: Inkling 2 Small +5\n\n- Hy-Memory-30B-A3B (60): File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n- 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.\n\n## Score by category\n\n| Category | Weight | Hy-Memory-30B-A3B | Inkling 2 Small | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 40 | Hy-Memory-30B-A3B +45 |\n| Risk | 20% (20 this run) | 60 | 65 | Inkling 2 Small +5 |\n| Agent interface | 10% (10 this run) | 65 | 85 | Inkling 2 Small +20 |\n| Documentation and upkeep | 10% (10 this run) | 68 | 67 | Hy-Memory-30B-A3B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **58.8 · C** | |\n\n## Facts side by side\n\n| Fact | Hy-Memory-30B-A3B | Inkling 2 Small |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | Thinking Machines Lab |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-16 | 2026-11-12 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## How the scores have moved\n\n### Hy-Memory-30B-A3B\n\nNo score change since it was listed.\n\n### Inkling 2 Small\n\n- +6.7 on 12 November 2026 (D to C): product\n\n## Verdicts\n\n**Hy-Memory-30B-A3B.** A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.\n\n**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.\n\n## Before you call either\n\n### Hy-Memory-30B-A3B\n\n1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user\n2. Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does\n3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does\n4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented\n5. Treat what it stores as unverified. It keeps what a conversation says, true or not\n\n### Inkling 2 Small\n\n1. 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\n2. 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`\n3. Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet\n4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99\n5. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens\n6. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n7. Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part\n8. Use guided decoding or validate JSON yourself, since structured output isn't documented\n9. 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\n10. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\n\n## Questions\n\n### Which is better for AI agents, Hy-Memory-30B-A3B or Inkling 2 Small?\n\nHy-Memory-30B-A3B scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 2 of 4 scored categories. Inkling 2 Small leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling-2-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling-2-small.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy-memory-30b-a3b\", \"b\": \"inkling-2-small\"}`. From a terminal: `anchor compare hy-memory-30b-a3b inkling-2-small`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json\n\n## Other comparisons with Hy-Memory-30B-A3B or Inkling 2 Small\n\n- [Agents-A1-4B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.md)\n- [Agents-A1-4B vs Inkling 2 Small](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-2-small.md)\n- [Apertus v1.5 8B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.md)\n- [Apertus v1.5 8B vs Inkling 2 Small](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-2-small.md)\n- [Apodex-1.1-mini vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md)\n- [Apodex-1.1-mini vs Inkling 2 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