{ "data": { "a": { "slug": "inkling-2-small", "name": "Inkling 2 Small", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "model", "category": "inference", "summary": "Inkling 2 Small is Thinking Machines Lab's mixture-of-experts model, 594B total and 17B active parameters, taking text and images and returning text. It runs on Tinker for inference and fine-tuning, with open weights promised.", "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", "license": "Proprietary for now, used through Tinker under the Tinker Terms of Use and Thinking Machines' Model Acceptable Use Policy, per the model card. Thinking Machines says it will publish the weights under Apache 2.0 the week after launch, once outside safety testing is done", "transports": [], "remoteUrl": "https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1", "packages": [], "auth": "api-key", "authNotes": "A Tinker API key from the Tinker console, read from TINKER_API_KEY and passed as the OpenAI client's api_key on the OpenAI-compatible endpoint, or used by the Tinker Python SDK. Sign-up is a browser flow at auth.thinkingmachines.ai, and the quickstart asks for payment details in Billing before training, per the tinker listing.", "pricing": "usage", "pricingNotes": "Serverless inference (beta) is $0.30 per million input tokens, $0.03 cached and $1.00 output, at 256K context. On the training side prefill is the same $0.30, sampling $1.00, and training $1.50 per million at 64K or $3.00 at 256K. No free tier is listed (checked 2026-11-06).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Tinker's models and pricing page or its OpenAI-compatible API page, and Tinker bills against an account with payment details (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling-2-small/", "llmsTxt": "https://tinker-docs.thinkingmachines.ai/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "moe", "multimodal", "vision", "reasoning", "openai-compatible", "usage-priced", "card-required", "beta", "llms-txt", "new" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 52.1, "grade": "D", "agentReady": false, "rank": 899, "ranked": true, "rankOf": 1354, "categoryRank": 28, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 62, "maintenance": 75, "payments": 20, "reliability": 30, "schema": 70, "security": 57, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "A new multimodal mixture-of-experts model from Thinking Machines, callable today through Tinker's OpenAI-compatible endpoint at $0.30 and $1.00 per million tokens, with strong vendor-reported coding and agent scores. Tinker's serverless inference is beta, with no status page or SLA, and the weights aren't public yet.", "bestFor": "Developers who want a capable multimodal open-family model through an OpenAI-compatible API today, or to fine-tune it on Tinker, and who can live with a beta surface until the weights are published.", "strengths": [ "OpenAI-compatible chat and completions endpoint, so the OpenAI SDKs work with a Tinker key", "$0.30 input, $0.03 cached and $1.00 output per million tokens, published without a login", "Text and image input, a 256K context on Tinker and up to 1M per the model card", "reasoning_effort from none to xhigh or a number, with reasoning returned separately", "A model card with intended use, limitations, training data sources and safety testing that includes outside red-teaming", "Thinking Machines says Apache 2.0 weights follow a week after launch" ], "weaknesses": [ "Serverless inference and the OpenAI-compatible API are both labelled beta, with a waitlist for higher throughput", "No status page, SLA or published rate limits for serverless inference", "Tool-calling format and structured output aren't documented for this model", "No free tier, and sign-up is a browser flow with payment details", "Benchmark results are Thinking Machines' own, with no independent results yet" ], "agentNotes": [ "Call https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1 with an OpenAI client and TINKER_API_KEY, using /chat/completions", "The serverless model ID is thinkingmachines/Inkling-2-Small:peft:262144:sampling-nvfp4, per Tinker's pricing page. The OpenAI page's examples use your own checkpoint paths, so confirm it before relying on it", "Pass reasoning_effort as none to xhigh or 0.0 to 0.99 (default 0.9); reasoning arrives in reasoning_content unless separate_reasoning is false", "Keep images between 40 and 4096 px a side, per the model card", "Validate JSON yourself, since structured output and the tool-call format aren't documented", "Plan for a move to open weights. When they publish, the open-weights listing will carry sizes and runtimes" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.1 } ], "editorialScores": { "ergonomics": 62, "maintenance": 75, "payments": 20, "reliability": 30, "schema": 70, "security": 57, "transparency": 60 }, "provenanceScore": 68, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1/chat/completions \\\n -H \"Authorization: Bearer $TINKER_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"thinkingmachines/Inkling-2-Small:peft:262144:sampling-nvfp4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"low\"\n }'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/inkling-2-small" }, "sameCompany": [ "inkling", "inkling-small" ], "sameModel": [ "tinker" ], "area": "models", "provenance": { "legalEntity": "Thinking Machines Lab, Inc. (named as provider on the model card)", "domain": "thinkingmachines.ai", "domainRegistered": "", "domainNote": "Not looked up: RDAP isn't requested under the 1 November 2026 rules.", "endpointOnVendorDomain": true, "terms": "https://thinkingmachines.ai/legal/terms/", "privacy": "https://thinkingmachines.ai/legal/privacy/", "statusPage": "", "changelog": "https://tinker-docs.thinkingmachines.ai/changelog/", "securityTxt": "valid", "checked": "2026-11-06", "notes": [ "The launch post and model card are on thinkingmachines.ai and name Thinking Machines Lab, Inc. as provider. The OpenAI-compatible endpoint is on thinkingmachines.dev, which Tinker's own docs give as the production base URL, so it's read as a domain the vendor controls; its registration wasn't looked up.", "Thinking Machines' terms bar scraping the Services, which the 4 November re-check read as a ban and paused. The founder decided on 6 November 2026 that reading at a person's pace is fine, so four pages were read one at a time: the launch post, the model card, the models and pricing page and the OpenAI-compatible API page. tinker-docs.thinkingmachines.ai has no robots.txt; thinkingmachines.ai's has no rules.", "security.txt lists security-reports@thinkingmachines.ai and expires 2029-07-13, per the 2 November reading.", "No status page was found." ], "score": 68 }, "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": "2026-11-06T09:47:32.967989265Z", "lastOk": true, "lastStatus": 404, "lastMs": 389, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 398, "p95ms24h": 429, "samples24h": 47, "samples30d": 47, "days": [ { "date": "2026-11-06", "probes": 47, "ok": 47 } ] }, "updatedAt": "2026-11-06T09:47:32.967989265Z" } }, "answer": "Inkling 2 Small and Jais 3 score within a point of each other on agent readiness, 52.1 (D) and 52.1 (D). Jais 3 leads on reliability and payments \u0026 pricing.", "b": { "slug": "jais-3", "name": "Jais 3", "vendor": "Institute of Foundation Models (MBZUAI) and Inception42", "vendorUrl": "https://ifm.ai/blog/jais3/", "kind": "model", "category": "inference", "summary": "Jais 3 is an open-weight family of Arabic and English reasoning models from MBZUAI's Institute of Foundation Models and Inception42, from 0.9B to 375B parameters with up to 512K tokens of context, released under Apache-2.0 for self-hosting.", "url": "https://www.anchorterminal.com/tools/jais-3", "markdownUrl": "https://www.anchorterminal.com/tools/jais-3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jais-3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jais-3.json", "dns": "jais-3.grade.anchorterminal.com", "repo": "https://huggingface.co/inception42/Jais-3-375B-A23B", "license": "Apache-2.0, declared in the metadata of every model card read. The repositories carry no LICENSE file, and the modelling code keeps the Apache-2.0 header of the Qwen3-MoE code it was generated from", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "No account or key. The inception42 and IFM repositories on Hugging Face are not gated and download anonymously. A local server has whatever authentication the owner adds, such as vLLM's `--api-key`. The cards' examples call the local server with `api_key=\"EMPTY\"`.", "pricing": "free", "pricingNotes": "Free to download and run under Apache-2.0, with the owner's GPUs as the cost. No hosted endpoint from IFM or Inception42 was found, and on 4 November 2026 Hugging Face listed no inference provider for any of the six main repositories.", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Jais 3 is a set of weights the owner serves, with no payment route and no hosted API from the makers found (checked 2026-10-17).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-17" }, "docsUrl": "https://huggingface.co/inception42/Jais-3-375B-A23B", "capabilities": [ "inference.llm", "inference.open-weights", "inference.local" ], "tags": [ "model", "open-weights", "self-hosted", "local", "free", "no-card", "apache-2.0", "openai-compatible", "gpu", "apple-silicon", "mlx", "new" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 52.1, "grade": "D", "agentReady": false, "rank": 898, "ranked": true, "rankOf": 1354, "categoryRank": 27, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 62, "payments": 60, "reliability": 48, "schema": 54, "security": 31, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": 0, "verdict": "Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code and no security contact is published.", "bestFor": "An agent or team that needs Arabic and English reasoning and tool use on its own hardware, from a laptop-sized 0.9B to a cluster-sized 375B, under Apache-2.0.", "strengths": [ "Six checkpoints from 0.9B (2.2 GB) to 375B-A23B (758 GB), with FP8, GGUF and MLX builds from IFM", "Apache-2.0 on every card read, ungated downloads, and the CPT, Cooldown, SFT and FTPO training checkpoints published as branches", "With the `k2_horizon` parsers, reasoning returns in `reasoning_content` and tool calls as `tool_calls`, in JSON, XML or typed XML formats", "Launch recipes in the vLLM and SGLang projects' own docs, and K2 Horizon support in llama.cpp's tagged builds since 6 October 2026", "The chat template validates tool schemas and rejects unknown options with named errors, such as an unsupported `reasoning_effort`" ], "weaknesses": [ "Every documented vLLM, SGLang, Transformers and MLX command passes `--trust-remote-code`, so the repository's Python runs on the host", "The 375B-A23B reference deployment is 16 H100 GPUs across two nodes, or one 8-GPU H200 node", "The code repository is marked 'coming soon', and the technical report, blog and press release on ifm.ai sit behind a bot check", "No LICENSE file in the repositories, no changelog, no deprecation policy and no security contact or security.txt found", "Released on 15 October 2026, with template fixes on 14 and 15 October, and mlx-lm v0.32.2 has no K2 Horizon support of its own" ], "agentNotes": [ "Start vLLM or SGLang with `--reasoning-parser k2_horizon`, and add `--tool-call-parser k2_horizon` (vLLM also `--enable-auto-tool-choice`) for tools, or tool calls come back as raw text", "Pass `reasoning_effort` (`high`, `medium` or `low`) and `tool_call_format` inside `chat_template_kwargs`. Other values return a template error", "Send each earlier assistant turn back with `reasoning_content` as a string, even an empty one. A missing or null thinking field makes the template raise an error", "Send your own system message, or set `default_system_prompt` to an empty string. Without one the template inserts an identity prompt naming IFM, Inception42 and Cerebras", "Budget at least 32,768 output tokens at `high` effort, which the cards recommend for every request" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.1 } ], "editorialScores": { "ergonomics": 65, "maintenance": 62, "payments": 60, "reliability": 48, "schema": 54, "security": 31, "transparency": 51 }, "provenanceScore": 58, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-10-27", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve inception42/Jais-3-32B \\\n --trust-remote-code \\\n --dtype bfloat16 \\\n --tensor-parallel-size 2 \\\n --reasoning-parser k2_horizon \\\n --enable-auto-tool-choice \\\n --tool-call-parser k2_horizon", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"inception42/Jais-3-32B\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Summarise this contract in Arabic.\"}],\n \"max_tokens\": 32768,\n \"chat_template_kwargs\": {\"reasoning_effort\": \"high\"}\n }'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/jais-3" }, "area": "models", "provenance": { "legalEntity": "Mohamed bin Zayed University of Artificial Intelligence (Institute of Foundation Models), with Inception42 (a G42 company)", "domain": "ifm.ai", "domainRegistered": "2022-09-06", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-17", "notes": [ "The chat template's default system prompt names the Institute of Foundation Models (IFM) at MBZUAI, with Inception42 (a G42 company) and Cerebras, and gives MBZUAI's Arabic name, Mohamed bin Zayed University of Artificial Intelligence. The citation names the IFM, Inception42 and Cerebras teams. No registered company name for Inception42 was read first-hand.", "RDAP gives a registration date of 6 September 2022 for ifm.ai. The weights are on huggingface.co under the inception42 and IFM organisations.", "The licence is Apache-2.0 in each card's metadata. There is no LICENSE file and no terms or privacy document governing the weights, and no input reaches the makers when the owner serves them.", "ifm.ai answered our reader with a Cloudflare bot check on the press release, the blog post and the sitemap, so they weren't read. inceptionai.ai and mbzuai.ac.ae answered with bot or firewall pages too. ifm.ai/.well-known/security.txt returned 404 on 17 October 2026.", "No hosted endpoint, so no status page. No changelog. The Hugging Face commit history is public and was read." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jais-3.json", "live": { "slug": "jais-3", "securityTxt": { "url": "https://ifm.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-05T15:55:15.982795298Z" }, "domain": { "domain": "ifm.ai", "registered": "2022-09-06", "source": "https://rdap.identitydigital.services/rdap/domain/ifm.ai", "checkedAt": "2026-11-01T14:11:01.629779131Z" }, "updatedAt": "2026-11-05T15:55:15.982795298Z" } }, "edges": [ { "by": 40, "category": "payments", "leads": "jais-3", "name": "Payments \u0026 pricing", "reasons": { "a": "Published rubric. No payment protocol (0). Per-million-token prices for serverless inference and training published without a login on 6 November (20). No free tier listed, and the quickstart asks for payment details before training, per the tinker listing (0). Sign-up is a browser flow (0). 0 + 20 + 0 + 0 = 20.", "b": "Self-hosted rule. Apache-2.0 weights with nothing to buy and no hosted option from the makers (20). Free to run with no card (20). Downloads are ungated and anonymous, so an agent can fetch the weights with no sign-up (20). No payment protocol (0). 20 + 20 + 20 + 0 = 60." }, "scores": { "a": 20, "b": 60 } }, { "by": 26, "category": "security", "leads": "inkling-2-small", "name": "Security \u0026 auth", "reasons": { "a": "Platform readings are the tinker listing's. Revocable API keys from the console, with no per-key scopes found (20). Org, team and project permissions with no read-only role (5). The model returns its own completions, no third-party content (10). get_audit_log() for admins (15). security.txt with a reporting address and no stated acknowledgement time (7). For the model reading, Tinker's terms commit not to use Customer Content for training, recorded under transparency. 20 + 5 + 10 + 15 + 7 = 57.", "b": "Read as software the owner runs. No account or key, ungated downloads, and server authentication is the owner's to add (12 of 30). A model that calls whatever tools the operator wires in. The cards rule high-stakes decisions without human oversight out of scope, but nothing in the model or template enforces approval (6 of 20). The template renders tool results in their own `tool` role, but string results go in as raw text (only non-string content is serialised with `tojson`), no escaping of special-token sequences in caller or tool text was found, and no injection guidance or results are published (4 of 15). No call log of its own. Visibility depends on the serving stack (5 of 15). Every launch command passes `--trust-remote-code`, so `modeling_k2_horizon.py` from the repository runs on the host. We read it and found only torch and transformers imports. No security.txt on ifm.ai (404 on 17 October, not re-read), no security contact on the cards and no safety evaluation found. Since 25 October the cards link a technical report on ifm.ai, which answered a Cloudflare bot check on 4 November, so what it covers is unread (4 of 20). 12 + 6 + 4 + 5 + 4 = 31." }, "scores": { "a": 57, "b": 31 } }, { "by": 18, "category": "reliability", "leads": "jais-3", "name": "Reliability", "reasons": { "a": "Model reading, for the surface it's called on, Tinker's serverless inference and OpenAI-compatible API. No status page found, per the tinker listing's checks of 1 October and 4 November (0), so no readable incident history (5). The only published limit is the in-flight sample cap of 2,000, per the tinker listing's 1 October check; the OpenAI-compatible page gives no numbers and says higher or more stable throughput needs the waitlist (10). The SDK retries sampling with stable request IDs, per the tinker listing's reading of the SDK repository; the OpenAI-compatible page has no retry guidance (15). No SLA found (0). Serverless inference is 'currently in beta' on the models and pricing page and the OpenAI-compatible API is labelled Beta, read 6 November (0). 0 + 5 + 10 + 15 + 0 + 0 = 30.", "b": "Read with the local-software lines, since the owner serves the weights. Ungated weights on Hugging Face with stated runtimes (vLLM 0.30.0 or later, SGLang 0.5.20 or later, Transformers validated at 5.15.0, mlx-lm with shipped model code), but every launch command trusts remote code. The GGUF card still says to build llama.cpp from master or wait for a release, though llama.cpp's own tags carry K2 Horizon support from b11454 (6 October 2026) (16 of 20, held). No CI or test suite of the makers' was found, and the code repository is 'coming soon'. SGLang's cookbook reports a validation pass of the tool-call parser on all six checkpoints, and IFM's MLX cards describe conversion checks against the PyTorch code (3 of 25). No crash reports. On 4 November the six main repositories held seven discussions, six of them closed citation updates and one open MoVA-36B-A4B thread from 26 October with no reply yet. Two template fixes for OpenAI-style message history landed on 14 October, before the launch (15 of 25). No changelog or version tags, only public commit messages (2 of 15). Released as named final checkpoints with no preview label, though mlx-lm v0.32.2 still has no K2 Horizon model code of its own (12 of 15). 16 + 3 + 15 + 2 + 12 = 48." }, "scores": { "a": 30, "b": 48 } }, { "by": 16, "category": "schema", "leads": "inkling-2-small", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading. No OpenAPI file of its own; the OpenAI-compatible chat and completions endpoints and the typed Python SDK are the contract, as the tinker listing reads it (15). llms.txt on the docs host, per the tinker listing's 1 October check (10). The model card states intended use (agentic and tool-use systems, coding assistants, chatbots, RAG), inputs, outputs and limitations, and the launch post its benchmarks (16 of 20). Model IDs, context lengths and the reasoning_effort values (none to xhigh, or 0.0 to 0.99) are documented; the tool-call format and structured output aren't, for this model (8 of 15). A Python example and an HTTP 400 for models without reasoning_effort, with no error page (6 of 15). A dated Tinker changelog and a model deprecations page, per the tinker listing (15). 15 + 10 + 16 + 8 + 6 + 15 = 70.", "b": "Read for a model the owner serves. No specification of its own. The contract is the Jinja chat template, which validates tool JSON Schemas (required fields must exist in properties) and rejects unknown options, plus the OpenAI-compatible servers in vLLM and SGLang with the `k2_horizon` parsers (10 of 25). The cards are Markdown on Hugging Face, and SGLang's docs have an llms.txt, but no llms.txt or Markdown docs from IFM were found and ifm.ai couldn't be read (5 of 10). Each card states intended use, four out-of-scope uses, limitations and per-size serving advice (15 of 20). `reasoning_effort` and `tool_call_format` are enums enforced with errors, and tools take JSON Schema. The 375B card describes `low` as no reasoning and omits `medium`, which the template accepts, and structured output isn't documented (10 of 15). Python examples for a reasoning call and a tool loop, commands for five runtimes, and named template errors, but no error reference for a served model (11 of 15). No changelog or version scheme. Training-stage branches and public commit history (3 of 15). 10 + 5 + 15 + 10 + 11 + 3 = 54." }, "scores": { "a": 70, "b": 54 } }, { "by": 13, "category": "maintenance", "leads": "inkling-2-small", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. Released 5 November 2026 (30). Churn and notice in place of release counts: Inkling, Inkling-Small (30 July) and Inkling 2 Small in about four months, and a deprecations page with dated retirements and a promise to 'aim to give advance notice' by email, with no stated period, per the tinker listing (10 of 20). A public changelog and a support address and Discord, per the tinker listing (10 of 15). Current official SDKs, Tinker's Python SDK and the OpenAI-compatible endpoint (15). SDK and cookbook CI, per the tinker listing (10). 30 + 10 + 10 + 15 + 10 = 75.", "b": "Read for an open-weight model released on 15 October 2026. The last change to the template or code was on 15 October, within 30 days. The cards gained a technical report link on 25 October and a citation update on 27 October, which change no file a runtime loads (30). One family release, with dated builds before it (FP8 from 29 September, MLX from 1 October, GGUF on 12 and 13 October) and template fixes on 14 and 15 October, but no deprecation notices or successor history to judge. K2 Horizon came on 1 September and Jais 2 in December 2025 (10 of 20, the model reading of this line, held). One outside question, on MoVA-36B-A4B on 26 October, had no reply on 4 November, and IFM's GitHub repositories weren't read. The template fixes match the problem IFM's MLX cards describe (7 of 25, held). Recipes in vLLM's and SGLang's own docs, K2 Horizon support in llama.cpp's tagged builds from b11454 and FP8, GGUF and MLX builds from IFM, but no native mlx-lm support (11 of 15). Current dependencies (Transformers 5.13 to 5.15, vLLM 0.30), with no CI visible (4 of 10). 30 + 10 + 7 + 11 + 4 = 62." }, "scores": { "a": 75, "b": 62 } }, { "by": 9, "category": "transparency", "leads": "inkling-2-small", "name": "Transparency \u0026 trust", "reasons": { "a": "Editorial half. Closed for now under the Tinker Terms of Use and the Model Acceptable Use Policy, per the model card, with Apache 2.0 weights promised the week after launch (15). Terms commit not to use Customer Content for training, keep it only as needed, with a DPA and deletion on request; the privacy notice wasn't re-read, per the tinker listing (15). Dated deprecation notices with no stated period (15). The DPA annex names Google Cloud, Cloudflare, WorkOS and OpenAI with service and location (15). 15 + 15 + 15 + 15 = 60.", "b": "Apache-2.0 in every card's metadata, with staged training checkpoints as branches and training logs linked on Weights \u0026 Biases (not opened). There is no LICENSE file and the code repository is still 'coming soon'. A technical report is linked from 25 October but sits behind a bot check, so its data description is unread (24 of 30). Inputs stay on the owner's hardware because nothing is hosted, but no statement says so and no privacy document governs the weights (15 of 30). No deprecation policy, dated notices or version scheme (0 of 20). Safetensors weights carry no code, and the remote modelling and configuration code we read imports only torch, transformers and huggingface_hub, with no network calls. No telemetry statement (12 of 20). 24 + 15 + 0 + 12 = 51." }, "scores": { "a": 64, "b": 55 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Thinking Machines Lab", "b": "Institute of Foundation Models (MBZUAI) and Inception42", "name": "Vendor" }, { "a": "https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary for now, used through Tinker under the Tinker Terms of Use and Thinking Machines' Model Acceptable Use Policy, per the model card. Thinking Machines says it will publish the weights under Apache 2.0 the week after launch, once outside safety testing is done", "b": "Apache-2.0, declared in the metadata of every model card read. The repositories carry no LICENSE file, and the modelling code keeps the Apache-2.0 header of the Qwen3-MoE code it was generated from", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-05", "b": "2026-10-15", "name": "Last release" }, { "a": "not read yet", "b": "no document linked", "name": "Terms last updated" }, { "a": "not read yet", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Inkling 2 Small and Jais 3 score within a point of each other on agent readiness, 52.1 (D) and 52.1 (D). Jais 3 leads on reliability and payments \u0026 pricing.", "question": "Which is better for AI agents, Inkling 2 Small or Jais 3?" }, { "answer": "Inkling 2 Small needs an API key. Jais 3 needs no key.", "question": "Do Inkling 2 Small and Jais 3 need an API key?" }, { "answer": "Inkling 2 Small has a hosted endpoint at https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1. No hosted endpoint is listed for Jais 3.", "question": "Can an agent call Inkling 2 Small and Jais 3 without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 70 against 54", "Security \u0026 auth, 57 against 31", "Maintenance \u0026 community, 75 against 62", "Transparency \u0026 trust, 64 against 55" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Developers who want a capable multimodal open-family model through an OpenAI-compatible API today, or to fine-tune it on Tinker, and who can live with a beta surface until the weights are published.", "slug": "inkling-2-small", "watchFor": "Serverless inference and the OpenAI-compatible API are both labelled beta, with a waitlist for higher throughput" }, { "aheadOn": [ "Reliability, 48 against 30", "Payments \u0026 pricing, 60 against 20" ], "also": [ "No key needed to call it", "Free to start without a card" ], "goodFor": "An agent or team that needs Arabic and English reasoning and tool use on its own hardware, from a laptop-sized 0.9B to a cluster-sized 375B, under Apache-2.0.", "slug": "jais-3", "watchFor": "Every documented vLLM, SGLang, Transformers and MLX command passes `--trust-remote-code`, so the repository's Python runs on the host" } ], "history": { "inkling-2-small": null, "jais-3": [ { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.4, "from": 51.7, "fromGrade": "D", "to": 52.1, "toGrade": "D" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-inkling-2-small.json", "title": "Claude API vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-jais-3.json", "title": "Claude API vs Jais 3", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-inkling-2-small.json", "title": "Antseed vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/antseed-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-jais-3.json", "title": "Antseed vs Jais 3", "url": "https://www.anchorterminal.com/compare/antseed-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-inkling-2-small.json", "title": "BlockRun.AI vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-jais-3.json", "title": "BlockRun.AI vs Jais 3", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-inkling-2-small.json", "title": "Cerebras Inference vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-inkling-2-small" }, { 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Jais 3", "url": "https://www.anchorterminal.com/compare/groq-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-meta-model-api.json", "title": "Inkling 2 Small vs Meta Model API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-api.json", "title": "Inkling 2 Small vs MiniMax API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mistral-api.json", "title": "Inkling 2 Small vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-spark-open.json", "title": "Inkling 2 Small vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-spark-open" }, { "json": 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"url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ramp-router.json", "title": "Inkling 2 Small vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-reflection-beam.json", "title": "Inkling 2 Small vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-routerplus.json", "title": "Inkling 2 Small vs RouterPlus", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-sambanova.json", "title": "Inkling 2 Small vs SambaCloud", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-siliconflow.json", "title": "Inkling 2 Small vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-simplismart.json", "title": "Inkling 2 Small vs Simplismart", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-stepfun-api.json", "title": "Inkling 2 Small vs StepFun API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-swe-2-flash.json", "title": "Inkling 2 Small vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-tencent-tokenhub.json", "title": "Inkling 2 Small vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-together-inference.json", "title": "Inkling 2 Small vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-vercel-ai-gateway.json", "title": "Inkling 2 Small vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-meta-model-api.json", "title": "Jais 3 vs Meta Model API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-minimax-api.json", "title": "Jais 3 vs MiniMax API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-mistral-api.json", "title": "Jais 3 vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open.json", "title": "Jais 3 vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-nemotron-3-5-super-vl.json", "title": "Jais 3 vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/jais-3-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-novita-ai.json", "title": "Jais 3 vs Novita AI", "url": "https://www.anchorterminal.com/compare/jais-3-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-openai-api.json", "title": "Jais 3 vs OpenAI API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-openrouter.json", "title": "Jais 3 vs OpenRouter", "url": "https://www.anchorterminal.com/compare/jais-3-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-prism-inference.json", "title": "Jais 3 vs Prism Inference", "url": "https://www.anchorterminal.com/compare/jais-3-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-ramp-router.json", "title": "Jais 3 vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/jais-3-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-reflection-beam.json", "title": "Jais 3 vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/jais-3-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-routerplus.json", "title": "Jais 3 vs RouterPlus", "url": "https://www.anchorterminal.com/compare/jais-3-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-sambanova.json", "title": "Jais 3 vs SambaCloud", "url": "https://www.anchorterminal.com/compare/jais-3-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-siliconflow.json", "title": "Jais 3 vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/jais-3-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-simplismart.json", "title": "Jais 3 vs Simplismart", "url": "https://www.anchorterminal.com/compare/jais-3-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-stepfun-api.json", "title": "Jais 3 vs StepFun API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-swe-2-flash.json", "title": "Jais 3 vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/jais-3-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-tencent-tokenhub.json", "title": "Jais 3 vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/jais-3-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-together-inference.json", "title": "Jais 3 vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/jais-3-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-vercel-ai-gateway.json", "title": "Jais 3 vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/jais-3-vs-vercel-ai-gateway" } ], "panel": null, "scores": [ { "by": 18, "edge": "jais-3", "inkling-2-small": 30, "jais-3": 48, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 16, "edge": "inkling-2-small", "inkling-2-small": 70, "jais-3": 54, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 3, "edge": "jais-3", "inkling-2-small": 62, "jais-3": 65, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 26, "edge": "inkling-2-small", "inkling-2-small": 57, "jais-3": 31, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 40, "edge": "jais-3", "inkling-2-small": 20, "jais-3": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 13, "edge": "inkling-2-small", "inkling-2-small": 75, "jais-3": 62, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 9, "edge": "inkling-2-small", "inkling-2-small": 64, "jais-3": 55, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Inkling 2 Small and Jais 3 score within a point of each other on agent readiness, 52.1 (D) and 52.1 (D). Jais 3 leads on reliability and payments \u0026 pricing. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "inkling-2-small": "A new multimodal mixture-of-experts model from Thinking Machines, callable today through Tinker's OpenAI-compatible endpoint at $0.30 and $1.00 per million tokens, with strong vendor-reported coding and agent scores. Tinker's serverless inference is beta, with no status page or SLA, and the weights aren't public yet.", "jais-3": "Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code and no security contact is published." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3", "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3.min.md" }, "markdown": "Inkling 2 Small and Jais 3 score within a point of each other on agent readiness, 52.1 (D) and 52.1 (D). Jais 3 leads on reliability and payments \u0026 pricing. Both do llm inference.\n\n- Inkling 2 Small: grade D, 52.1/100, rank #899 of 1354. Markdown https://www.anchorterminal.com/tools/inkling-2-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json\n- Jais 3: grade D, 52.1/100, rank #898 of 1354. Markdown https://www.anchorterminal.com/tools/jais-3.md · JSON https://www.anchorterminal.com/api/v1/tools/jais-3.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 596 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Inkling 2 Small (D)\n\nGood for: Developers who want a capable multimodal open-family model through an OpenAI-compatible API today, or to fine-tune it on Tinker, and who can live with a beta surface until the weights are published.\n\nAhead on:\n- Schema \u0026 documentation, 70 against 54\n- Security \u0026 auth, 57 against 31\n- Maintenance \u0026 community, 75 against 62\n- Transparency \u0026 trust, 64 against 55\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Serverless inference and the OpenAI-compatible API are both labelled beta, with a waitlist for higher throughput\n\n### Jais 3 (D)\n\nGood for: An agent or team that needs Arabic and English reasoning and tool use on its own hardware, from a laptop-sized 0.9B to a cluster-sized 375B, under Apache-2.0.\n\nAhead on:\n- Reliability, 48 against 30\n- Payments \u0026 pricing, 60 against 20\n\nAlso in its favour:\n- No key needed to call it\n- Free to start without a card\n\nWatch for: Every documented vLLM, SGLang, Transformers and MLX command passes `--trust-remote-code`, so the repository's Python runs on the host\n\n\n## Why the edge\n\n### Payments \u0026 pricing: Jais 3 +40\n\n- Inkling 2 Small (20): Published rubric. No payment protocol (0). Per-million-token prices for serverless inference and training published without a login on 6 November (20). No free tier listed, and the quickstart asks for payment details before training, per the tinker listing (0). Sign-up is a browser flow (0). 0 + 20 + 0 + 0 = 20.\n- Jais 3 (60): Self-hosted rule. Apache-2.0 weights with nothing to buy and no hosted option from the makers (20). Free to run with no card (20). Downloads are ungated and anonymous, so an agent can fetch the weights with no sign-up (20). No payment protocol (0). 20 + 20 + 20 + 0 = 60.\n\n### Security \u0026 auth: Inkling 2 Small +26\n\n- Inkling 2 Small (57): Platform readings are the tinker listing's. Revocable API keys from the console, with no per-key scopes found (20). Org, team and project permissions with no read-only role (5). The model returns its own completions, no third-party content (10). get_audit_log() for admins (15). security.txt with a reporting address and no stated acknowledgement time (7). For the model reading, Tinker's terms commit not to use Customer Content for training, recorded under transparency. 20 + 5 + 10 + 15 + 7 = 57.\n- Jais 3 (31): Read as software the owner runs. No account or key, ungated downloads, and server authentication is the owner's to add (12 of 30). A model that calls whatever tools the operator wires in. The cards rule high-stakes decisions without human oversight out of scope, but nothing in the model or template enforces approval (6 of 20). The template renders tool results in their own `tool` role, but string results go in as raw text (only non-string content is serialised with `tojson`), no escaping of special-token sequences in caller or tool text was found, and no injection guidance or results are published (4 of 15). No call log of its own. Visibility depends on the serving stack (5 of 15). Every launch command passes `--trust-remote-code`, so `modeling_k2_horizon.py` from the repository runs on the host. We read it and found only torch and transformers imports. No security.txt on ifm.ai (404 on 17 October, not re-read), no security contact on the cards and no safety evaluation found. Since 25 October the cards link a technical report on ifm.ai, which answered a Cloudflare bot check on 4 November, so what it covers is unread (4 of 20). 12 + 6 + 4 + 5 + 4 = 31.\n\n### Reliability: Jais 3 +18\n\n- Inkling 2 Small (30): Model reading, for the surface it's called on, Tinker's serverless inference and OpenAI-compatible API. No status page found, per the tinker listing's checks of 1 October and 4 November (0), so no readable incident history (5). The only published limit is the in-flight sample cap of 2,000, per the tinker listing's 1 October check; the OpenAI-compatible page gives no numbers and says higher or more stable throughput needs the waitlist (10). The SDK retries sampling with stable request IDs, per the tinker listing's reading of the SDK repository; the OpenAI-compatible page has no retry guidance (15). No SLA found (0). Serverless inference is 'currently in beta' on the models and pricing page and the OpenAI-compatible API is labelled Beta, read 6 November (0). 0 + 5 + 10 + 15 + 0 + 0 = 30.\n- Jais 3 (48): Read with the local-software lines, since the owner serves the weights. Ungated weights on Hugging Face with stated runtimes (vLLM 0.30.0 or later, SGLang 0.5.20 or later, Transformers validated at 5.15.0, mlx-lm with shipped model code), but every launch command trusts remote code. The GGUF card still says to build llama.cpp from master or wait for a release, though llama.cpp's own tags carry K2 Horizon support from b11454 (6 October 2026) (16 of 20, held). No CI or test suite of the makers' was found, and the code repository is 'coming soon'. SGLang's cookbook reports a validation pass of the tool-call parser on all six checkpoints, and IFM's MLX cards describe conversion checks against the PyTorch code (3 of 25). No crash reports. On 4 November the six main repositories held seven discussions, six of them closed citation updates and one open MoVA-36B-A4B thread from 26 October with no reply yet. Two template fixes for OpenAI-style message history landed on 14 October, before the launch (15 of 25). No changelog or version tags, only public commit messages (2 of 15). Released as named final checkpoints with no preview label, though mlx-lm v0.32.2 still has no K2 Horizon model code of its own (12 of 15). 16 + 3 + 15 + 2 + 12 = 48.\n\n### Schema \u0026 documentation: Inkling 2 Small +16\n\n- Inkling 2 Small (70): Model reading. No OpenAPI file of its own; the OpenAI-compatible chat and completions endpoints and the typed Python SDK are the contract, as the tinker listing reads it (15). llms.txt on the docs host, per the tinker listing's 1 October check (10). The model card states intended use (agentic and tool-use systems, coding assistants, chatbots, RAG), inputs, outputs and limitations, and the launch post its benchmarks (16 of 20). Model IDs, context lengths and the reasoning_effort values (none to xhigh, or 0.0 to 0.99) are documented; the tool-call format and structured output aren't, for this model (8 of 15). A Python example and an HTTP 400 for models without reasoning_effort, with no error page (6 of 15). A dated Tinker changelog and a model deprecations page, per the tinker listing (15). 15 + 10 + 16 + 8 + 6 + 15 = 70.\n- Jais 3 (54): Read for a model the owner serves. No specification of its own. The contract is the Jinja chat template, which validates tool JSON Schemas (required fields must exist in properties) and rejects unknown options, plus the OpenAI-compatible servers in vLLM and SGLang with the `k2_horizon` parsers (10 of 25). The cards are Markdown on Hugging Face, and SGLang's docs have an llms.txt, but no llms.txt or Markdown docs from IFM were found and ifm.ai couldn't be read (5 of 10). Each card states intended use, four out-of-scope uses, limitations and per-size serving advice (15 of 20). `reasoning_effort` and `tool_call_format` are enums enforced with errors, and tools take JSON Schema. The 375B card describes `low` as no reasoning and omits `medium`, which the template accepts, and structured output isn't documented (10 of 15). Python examples for a reasoning call and a tool loop, commands for five runtimes, and named template errors, but no error reference for a served model (11 of 15). No changelog or version scheme. Training-stage branches and public commit history (3 of 15). 10 + 5 + 15 + 10 + 11 + 3 = 54.\n\n### Maintenance \u0026 community: Inkling 2 Small +13\n\n- Inkling 2 Small (75): Model reading. Released 5 November 2026 (30). Churn and notice in place of release counts: Inkling, Inkling-Small (30 July) and Inkling 2 Small in about four months, and a deprecations page with dated retirements and a promise to 'aim to give advance notice' by email, with no stated period, per the tinker listing (10 of 20). A public changelog and a support address and Discord, per the tinker listing (10 of 15). Current official SDKs, Tinker's Python SDK and the OpenAI-compatible endpoint (15). SDK and cookbook CI, per the tinker listing (10). 30 + 10 + 10 + 15 + 10 = 75.\n- Jais 3 (62): Read for an open-weight model released on 15 October 2026. The last change to the template or code was on 15 October, within 30 days. The cards gained a technical report link on 25 October and a citation update on 27 October, which change no file a runtime loads (30). One family release, with dated builds before it (FP8 from 29 September, MLX from 1 October, GGUF on 12 and 13 October) and template fixes on 14 and 15 October, but no deprecation notices or successor history to judge. K2 Horizon came on 1 September and Jais 2 in December 2025 (10 of 20, the model reading of this line, held). One outside question, on MoVA-36B-A4B on 26 October, had no reply on 4 November, and IFM's GitHub repositories weren't read. The template fixes match the problem IFM's MLX cards describe (7 of 25, held). Recipes in vLLM's and SGLang's own docs, K2 Horizon support in llama.cpp's tagged builds from b11454 and FP8, GGUF and MLX builds from IFM, but no native mlx-lm support (11 of 15). Current dependencies (Transformers 5.13 to 5.15, vLLM 0.30), with no CI visible (4 of 10). 30 + 10 + 7 + 11 + 4 = 62.\n\n### Transparency \u0026 trust: Inkling 2 Small +9\n\n- Inkling 2 Small (64): Editorial half. Closed for now under the Tinker Terms of Use and the Model Acceptable Use Policy, per the model card, with Apache 2.0 weights promised the week after launch (15). Terms commit not to use Customer Content for training, keep it only as needed, with a DPA and deletion on request; the privacy notice wasn't re-read, per the tinker listing (15). Dated deprecation notices with no stated period (15). The DPA annex names Google Cloud, Cloudflare, WorkOS and OpenAI with service and location (15). 15 + 15 + 15 + 15 = 60.\n- Jais 3 (55): Apache-2.0 in every card's metadata, with staged training checkpoints as branches and training logs linked on Weights \u0026 Biases (not opened). There is no LICENSE file and the code repository is still 'coming soon'. A technical report is linked from 25 October but sits behind a bot check, so its data description is unread (24 of 30). Inputs stay on the owner's hardware because nothing is hosted, but no statement says so and no privacy document governs the weights (15 of 30). No deprecation policy, dated notices or version scheme (0 of 20). Safetensors weights carry no code, and the remote modelling and configuration code we read imports only torch, transformers and huggingface_hub, with no network calls. No telemetry statement (12 of 20). 24 + 15 + 0 + 12 = 51.\n\n## Score by category\n\n| Category | Weight | Inkling 2 Small | Jais 3 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 30 | 48 | Jais 3 +18 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 70 | 54 | Inkling 2 Small +16 |\n| Agent ergonomics | 13% (16.2 this run) | 62 | 65 | Jais 3 +3 |\n| Security \u0026 auth | 14% (17.5 this run) | 57 | 31 | Inkling 2 Small +26 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 60 | Jais 3 +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 75 | 62 | Inkling 2 Small +13 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 64 | 55 | Inkling 2 Small +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.1 · D** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Inkling 2 Small | Jais 3 |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Thinking Machines Lab | Institute of Foundation Models (MBZUAI) and Inception42 |\n| Hosted endpoint | `https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1` | no (local only) |\n| Transports | | HTTP |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | Proprietary for now, used through Tinker under the Tinker Terms of Use and Thinking Machines' Model Acceptable Use Policy, per the model card. Thinking Machines says it will publish the weights under Apache 2.0 the week after launch, once outside safety testing is done | Apache-2.0, declared in the metadata of every model card read. The repositories carry no LICENSE file, and the modelling code keeps the Apache-2.0 header of the Qwen3-MoE code it was generated from |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-05 | 2026-10-15 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | not read yet | no document linked |\n| Agent reviews | none | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Inkling 2 Small\n\nNo score change since it was listed.\n\n### Jais 3\n\n- +0.4 on 27 October 2026: checklist\n\n## Verdicts\n\n**Inkling 2 Small.** A new multimodal mixture-of-experts model from Thinking Machines, callable today through Tinker's OpenAI-compatible endpoint at $0.30 and $1.00 per million tokens, with strong vendor-reported coding and agent scores. Tinker's serverless inference is beta, with no status page or SLA, and the weights aren't public yet.\n\n**Jais 3.** Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code and no security contact is published.\n\n## Before you call either\n\n### Inkling 2 Small\n\n1. Call https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1 with an OpenAI client and TINKER_API_KEY, using /chat/completions\n2. The serverless model ID is thinkingmachines/Inkling-2-Small:peft:262144:sampling-nvfp4, per Tinker's pricing page. The OpenAI page's examples use your own checkpoint paths, so confirm it before relying on it\n3. Pass reasoning_effort as none to xhigh or 0.0 to 0.99 (default 0.9); reasoning arrives in reasoning_content unless separate_reasoning is false\n4. Keep images between 40 and 4096 px a side, per the model card\n5. Validate JSON yourself, since structured output and the tool-call format aren't documented\n6. Plan for a move to open weights. When they publish, the open-weights listing will carry sizes and runtimes\n\n### Jais 3\n\n1. Start vLLM or SGLang with `--reasoning-parser k2_horizon`, and add `--tool-call-parser k2_horizon` (vLLM also `--enable-auto-tool-choice`) for tools, or tool calls come back as raw text\n2. Pass `reasoning_effort` (`high`, `medium` or `low`) and `tool_call_format` inside `chat_template_kwargs`. Other values return a template error\n3. Send each earlier assistant turn back with `reasoning_content` as a string, even an empty one. A missing or null thinking field makes the template raise an error\n4. Send your own system message, or set `default_system_prompt` to an empty string. Without one the template inserts an identity prompt naming IFM, Inception42 and Cerebras\n5. Budget at least 32,768 output tokens at `high` effort, which the cards recommend for every request\n\n## Questions\n\n### Which is better for AI agents, Inkling 2 Small or Jais 3?\n\nInkling 2 Small and Jais 3 score within a point of each other on agent readiness, 52.1 (D) and 52.1 (D). Jais 3 leads on reliability and payments \u0026 pricing.\n\n### Do Inkling 2 Small and Jais 3 need an API key?\n\nInkling 2 Small needs an API key. Jais 3 needs no key.\n\n### Can an agent call Inkling 2 Small and Jais 3 without installing anything?\n\nInkling 2 Small has a hosted endpoint at https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1. No hosted endpoint is listed for Jais 3.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"inkling-2-small\", \"b\": \"jais-3\"}`. From a terminal: `anchor compare inkling-2-small jais-3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json and https://www.anchorterminal.com/api/v1/tools/jais-3.json\n\n## Other comparisons with Inkling 2 Small or Jais 3\n\n- [Claude API vs Inkling 2 Small](https://www.anchorterminal.com/compare/anthropic-api-vs-inkling-2-small.md)\n- [Claude API vs Jais 3](https://www.anchorterminal.com/compare/anthropic-api-vs-jais-3.md)\n- [Antseed vs Inkling 2 Small](https://www.anchorterminal.com/compare/antseed-vs-inkling-2-small.md)\n- [Antseed vs Jais 3](https://www.anchorterminal.com/compare/antseed-vs-jais-3.md)\n- [BlockRun.AI vs Inkling 2 Small](https://www.anchorterminal.com/compare/blockrun-ai-vs-inkling-2-small.md)\n- [BlockRun.AI vs Jais 3](https://www.anchorterminal.com/compare/blockrun-ai-vs-jais-3.md)\n- [Cerebras Inference vs Inkling 2 Small](https://www.anchorterminal.com/compare/cerebras-inference-vs-inkling-2-small.md)\n- [Cerebras 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API](https://www.anchorterminal.com/compare/jais-3-vs-stepfun-api.md)\n- [Jais 3 vs SWE-2 Flash](https://www.anchorterminal.com/compare/jais-3-vs-swe-2-flash.md)\n- [Jais 3 vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/jais-3-vs-tencent-tokenhub.md)\n- [Jais 3 vs Together AI Inference](https://www.anchorterminal.com/compare/jais-3-vs-together-inference.md)\n- [Jais 3 vs Vercel AI Gateway](https://www.anchorterminal.com/compare/jais-3-vs-vercel-ai-gateway.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-06", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": 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