{ "data": { "a": { "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": 1000, "ranked": true, "rankOf": 1515, "categoryRank": 34, "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": "2026-11-09T16:05:01.16816008Z" }, "domain": { "domain": "ifm.ai", "registered": "2022-09-06", "source": "https://rdap.identitydigital.services/rdap/domain/ifm.ai", "checkedAt": "2026-11-08T15:05:12.570760186Z" }, "updatedAt": "2026-11-09T16:05:01.16816008Z" } }, "answer": "Jais 3 and Muse Ember score within a point of each other on agent readiness, 52.1 (D) and 51.8 (D). Muse Ember leads on schema \u0026 documentation, agent ergonomics and security \u0026 auth.", "b": { "slug": "muse-ember", "name": "Muse Ember", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai", "kind": "model", "category": "inference", "summary": "Muse Ember is Meta's smallest and most efficient hosted model, built for sub-agent tasks, tool calls, routing and work over large collections of files and images, taking text, images, video, audio and PDF with a 1M-token context.", "url": "https://www.anchorterminal.com/tools/muse-ember", "markdownUrl": "https://www.anchorterminal.com/tools/muse-ember.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-ember.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-ember.json", "dns": "muse-ember.grade.anchorterminal.com", "license": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "transports": [], "remoteUrl": "https://api.meta.ai/v1", "packages": [], "auth": "api-key", "authNotes": "A bearer key from the API keys tab of the Model API dashboard, read from MODEL_API_KEY in Meta's examples, after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team and share its limits, with no scopes found, per the meta-model-api listing.", "pricing": "usage", "pricingNotes": "$0.10 per million input tokens, $0.004 cached and $0.50 output for requests up to 200K input tokens, and $0.30, $0.012 and $1.50 for the whole request above that. Standard mode only, no Contributor discount. Web search grounding $2.50 per 1,000 queries. Included in every Muse Code plan. No free API allowance found (checked 2026-11-09).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Meta's pricing page, which bills tokens to a team account with a card (checked 2026-11-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-09" }, "docsUrl": "https://dev.meta.ai/docs/muse-ember", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "vision", "reasoning", "long-context", "tool-calling", "openai-compatible", "anthropic-compatible", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-09", "graded": true, "anchor": { "graded": true, "score": 51.8, "grade": "D", "agentReady": false, "rank": 1017, "ranked": true, "rankOf": 1515, "categoryRank": 36, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-09" }, "negative": 0, "verdict": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.", "bestFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "strengths": [ "$0.10 input, $0.004 cached and $0.50 output per million tokens up to 200K input, published without a login", "Text, image, video, audio and PDF in, a 1,048,576-token context and up to 524,288 output tokens", "The same request body as Muse Spark and Muse Wonder on Responses, Chat Completions and Messages endpoints", "Tool calling, structured output, computer use and search grounding", "Reasoning effort from minimal to max, with clear guidance on what to hand it and what not to", "3,000 requests and 4,000,000 tokens a minute per team, published" ], "weaknesses": [ "No weights and no parameter count published, so it can't be run locally", "Requests over 200K input tokens are billed at three times the rate for the whole request", "Default reasoning effort is max, which spends output tokens on simple routing calls unless set lower", "No readable status page, SLA, deprecation policy or API changelog, per the meta-model-api listing", "Benchmark and cost figures are Meta's own, several on internal benchmarks" ], "agentNotes": [ "Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY", "Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing", "Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate", "Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises", "Use strict structured output for extraction and routing steps", "In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide" ], "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": 51.8 } ], "editorialScores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 64, "checked": { "categories": { "ergonomics": "2026-11-09", "maintenance": "2026-11-09", "payments": "2026-11-09", "reliability": "2026-11-09", "schema": "2026-11-09", "security": "2026-11-09", "transparency": "2026-11-09" }, "last": "2026-11-09", "oldest": "2026-11-09", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl -X POST \"https://api.meta.ai/v1/responses\" \\\n -H \"Authorization: Bearer $MODEL_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"muse-ember-1.0\",\n \"input\": \"Classify this support ticket as billing, bug, or feature request.\",\n \"reasoning\": {\"effort\": \"low\"}\n}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/muse-ember" }, "sameCompany": [ "muse-spark-open", "muse-code", "muse-glimmer-30b" ], "sameModel": [ "meta-model-api" ], "area": "models", "unitPrices": [ { "item": "Muse Ember input", "unit": "1m-tokens", "usd": 0.1, "note": "Up to 200K input tokens a request; $0.30 above, for the whole request" }, { "item": "Muse Ember cached input", "unit": "1m-tokens", "usd": 0.004, "note": "$0.012 above 200K input tokens" }, { "item": "Muse Ember output", "unit": "1m-tokens", "usd": 0.5, "note": "$1.50 above 200K input tokens" } ], "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": true, "terms": "https://dev.meta.ai/legal/terms-of-service", "privacy": "https://www.facebook.com/privacy/policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-09", "notes": [ "Company and platform facts are the meta-model-api listing's (checked 4 November 2026): Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, under the 9 October 2026 terms; no readable status page; no Model API changelog; no security.txt on dev.meta.ai.", "dev.meta.ai's and research.meta.ai's robots.txt allow our reader on the pages read on 9 November. domainRegistered is carried from the meta-model-api listing's RDAP reading." ], "score": 64 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-ember.json", "live": { "slug": "muse-ember", "probe": { "target": "https://api.meta.ai/v1", "method": "get", "lastAt": "2026-11-09T20:50:02.257490788Z", "lastOk": true, "lastStatus": 404, "lastMs": 46, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 48, "p95ms24h": 72, "samples24h": 10, "samples30d": 10, "days": [ { "date": "2026-11-09", "probes": 10, "ok": 10 } ], "since": "2026-11-09T19:55:51.172718582Z" }, "updatedAt": "2026-11-09T20:50:02.257490788Z" }, "streak": { "days": 0, "from": "2026-11-09", "sinceWatching": true, "best": 0 } }, "edges": [ { "by": 40, "category": "payments", "leads": "jais-3", "name": "Payments \u0026 pricing", "reasons": { "a": "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.", "b": "Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20." }, "scores": { "a": 60, "b": 20 } }, { "by": 33, "category": "security", "leads": "muse-ember", "name": "Security \u0026 auth", "reasons": { "a": "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.", "b": "Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64." }, "scores": { "a": 31, "b": 64 } }, { "by": 16, "category": "maintenance", "leads": "jais-3", "name": "Maintenance \u0026 community", "reasons": { "a": "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.", "b": "Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46." }, "scores": { "a": 62, "b": 46 } }, { "by": 8, "category": "reliability", "leads": "jais-3", "name": "Reliability", "reasons": { "a": "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.", "b": "Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40." }, "scores": { "a": 48, "b": 40 } }, { "by": 7, "category": "ergonomics", "leads": "muse-ember", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, throughput, defaults and SDKs, errors). OpenAI-format tools come back as `tool_calls` in three selectable wire formats, with a multi-turn loop example. Forced `tool_choice` and parallel calls aren't documented (15 of 20). JSON mode and schemas only through each runtime's guided decoding, not documented by the makers (5 of 15). Prefix caching is the runtime's, and SGLang's page covers HiCache (7 of 10). 524,288 tokens on five of six models, 131,072 on the 0.9B (14 of 15). Uno adapters claim 2.15 times faster decoding for 0.9B to 32B but need IFM's own engine. SGLang lists n-gram speculative decoding and PD disaggregation. No batch mode of its own (7 of 10). Any OpenAI SDK works against a local server, and the 0.9B to 7B fit one GPU. But the cards ask for high effort and 32,768 output tokens on every request, the template inserts an identity system prompt when none is sent, and earlier assistant turns must carry a string thinking field (8 of 15). The template raises named errors for bad options, string-encoded tool arguments and malformed schemas. Server errors are the runtime's (9 of 15). 15 + 5 + 7 + 14 + 7 + 8 + 9 = 65.", "b": "Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72." }, "scores": { "a": 65, "b": 72 } }, { "by": 6, "category": "schema", "leads": "muse-ember", "name": "Schema \u0026 documentation", "reasons": { "a": "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.", "b": "Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60." }, "scores": { "a": 54, "b": 60 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Institute of Foundation Models (MBZUAI) and Inception42", "b": "Meta", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.meta.ai/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "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", "b": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-15", "b": "2026-11-09", "name": "Last release" }, { "a": "no document linked", "b": "2026-10-02", "name": "Terms last updated" }, { "a": "no document linked", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "not read yet", "b": "yes", "name": "Customer content may train models" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not read yet", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not read yet", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Jais 3 and Muse Ember score within a point of each other on agent readiness, 52.1 (D) and 51.8 (D). Muse Ember leads on schema \u0026 documentation, agent ergonomics and security \u0026 auth.", "question": "Which is better for AI agents, Jais 3 or Muse Ember?" }, { "answer": "Jais 3 needs no key. Muse Ember needs an API key.", "question": "Do Jais 3 and Muse Ember need an API key?" }, { "answer": "No hosted endpoint is listed for Jais 3. Muse Ember has a hosted endpoint at https://api.meta.ai/v1.", "question": "Can an agent call Jais 3 and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 48 against 40", "Payments \u0026 pricing, 60 against 20", "Maintenance \u0026 community, 62 against 46" ], "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" }, { "aheadOn": [ "Schema \u0026 documentation, 60 against 54", "Agent ergonomics, 72 against 65", "Security \u0026 auth, 64 against 31" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "slug": "muse-ember", "watchFor": "No weights and no parameter count published, so it can't be run locally" } ], "history": { "jais-3": [ { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.4, "from": 51.7, "fromGrade": "D", "to": 52.1, "toGrade": "D" } ], "muse-ember": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-jais-3.json", "title": "Amazon Bedrock vs Jais 3", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.json", "title": "Amazon Bedrock vs Muse Ember", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember" }, { "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/anthropic-api-vs-muse-ember.json", "title": "Claude API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember" }, { "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/antseed-vs-muse-ember.json", "title": "Antseed vs Muse Ember", "url": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-jais-3.json", "title": "Azure OpenAI in Foundry Models vs Jais 3", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.json", "title": "Azure OpenAI in Foundry Models vs Muse Ember", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember" }, { "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/blockrun-ai-vs-muse-ember.json", "title": "BlockRun.AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-jais-3.json", "title": "Cerebras Inference vs Jais 3", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.json", "title": "Cerebras Inference vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-jais-3.json", "title": "Cloudflare AI Gateway vs Jais 3", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember.json", "title": "Cloudflare AI Gateway vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-jais-3.json", "title": "Cloudflare Workers AI vs Jais 3", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember.json", "title": "Cloudflare Workers AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-jais-3.json", "title": "Cohere Chat API (Command models) vs Jais 3", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.json", "title": "Cohere Chat API (Command models) vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-jais-3.json", "title": "DeepInfra vs Jais 3", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.json", "title": "DeepInfra vs Muse Ember", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-jais-3.json", "title": "DeepSeek API vs Jais 3", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.json", "title": "DeepSeek API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.json", "title": "Eden AI vs Jais 3", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember.json", "title": "Eden AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-jais-3.json", "title": "Glimpse vs Jais 3", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember.json", "title": "Glimpse vs Muse Ember", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-jais-3.json", "title": "Fireworks AI Inference vs Jais 3", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-jais-3" }, { "json": 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"https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-jais-3.json", "title": "Hugging Face Inference Providers vs Jais 3", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.json", "title": "Hugging Face Inference Providers vs Muse Ember", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3.json", "title": "Inkling 2 Small vs Jais 3", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember.json", "title": "Inkling 2 Small vs Muse Ember", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.json", "title": "Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-xai-api.json", "title": "Jais 3 vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/jais-3-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.json", "title": "Meta Model API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.json", "title": "MiniMax API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.json", "title": "Mistral AI API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.json", "title": "Muse Ember vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.json", "title": "Muse Ember vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.json", "title": "Muse Ember vs Novita AI", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json", "title": "Muse Ember vs OpenAI API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.json", "title": "Muse Ember vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.json", "title": "Muse Ember vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.json", "title": "Muse Ember vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "title": "Muse Ember vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.json", "title": "Muse Ember vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json", "title": "Muse Ember vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.json", "title": "Muse Ember vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json", "title": "Muse Ember vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.json", "title": "Muse Ember vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.json", "title": "Muse Ember vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.json", "title": "Muse Ember vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.json", "title": "Muse Ember vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.json", "title": "Muse Ember vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.json", "title": "Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.json", "title": "Muse Ember vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api" } ], "panel": null, "scores": [ { "by": 8, "edge": "jais-3", "jais-3": 48, "key": "reliability", "muse-ember": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 6, "edge": "muse-ember", "jais-3": 54, "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 7, "edge": "muse-ember", "jais-3": 65, "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 33, "edge": "muse-ember", "jais-3": 31, "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 40, "edge": "jais-3", "jais-3": 60, "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 16, "edge": "jais-3", "jais-3": 62, "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 2, "edge": "jais-3", "jais-3": 55, "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Jais 3 and Muse Ember score within a point of each other on agent readiness, 52.1 (D) and 51.8 (D). Muse Ember leads on schema \u0026 documentation, agent ergonomics and security \u0026 auth. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "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.", "muse-ember": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.min.md" }, "markdown": "Jais 3 and Muse Ember score within a point of each other on agent readiness, 52.1 (D) and 51.8 (D). Muse Ember leads on schema \u0026 documentation, agent ergonomics and security \u0026 auth. Both do llm inference.\n\n- Jais 3: grade D, 52.1/100, rank #1000 of 1515. Markdown https://www.anchorterminal.com/tools/jais-3.md · JSON https://www.anchorterminal.com/api/v1/tools/jais-3.json\n- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\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 40\n- Payments \u0026 pricing, 60 against 20\n- Maintenance \u0026 community, 62 against 46\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### Muse Ember (D)\n\nGood for: Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.\n\nAhead on:\n- Schema \u0026 documentation, 60 against 54\n- Agent ergonomics, 72 against 65\n- Security \u0026 auth, 64 against 31\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n\n## Why the edge\n\n### Payments \u0026 pricing: Jais 3 +40\n\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- Muse Ember (20): Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20.\n\n### Security \u0026 auth: Muse Ember +33\n\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- Muse Ember (64): Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.\n\n### Maintenance \u0026 community: Jais 3 +16\n\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- Muse Ember (46): Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.\n\n### Reliability: Jais 3 +8\n\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- Muse Ember (40): Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.\n\n### Agent ergonomics: Muse Ember +7\n\n- Jais 3 (65): Model reading of the checklist (tool use, structured output, caching, context, throughput, defaults and SDKs, errors). OpenAI-format tools come back as `tool_calls` in three selectable wire formats, with a multi-turn loop example. Forced `tool_choice` and parallel calls aren't documented (15 of 20). JSON mode and schemas only through each runtime's guided decoding, not documented by the makers (5 of 15). Prefix caching is the runtime's, and SGLang's page covers HiCache (7 of 10). 524,288 tokens on five of six models, 131,072 on the 0.9B (14 of 15). Uno adapters claim 2.15 times faster decoding for 0.9B to 32B but need IFM's own engine. SGLang lists n-gram speculative decoding and PD disaggregation. No batch mode of its own (7 of 10). Any OpenAI SDK works against a local server, and the 0.9B to 7B fit one GPU. But the cards ask for high effort and 32,768 output tokens on every request, the template inserts an identity system prompt when none is sent, and earlier assistant turns must carry a string thinking field (8 of 15). The template raises named errors for bad options, string-encoded tool arguments and malformed schemas. Server errors are the runtime's (9 of 15). 15 + 5 + 7 + 14 + 7 + 8 + 9 = 65.\n- Muse Ember (72): Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.\n\n### Schema \u0026 documentation: Muse Ember +6\n\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- Muse Ember (60): Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.\n\n## Score by category\n\n| Category | Weight | Jais 3 | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 48 | 40 | Jais 3 +8 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 54 | 60 | Muse Ember +6 |\n| Agent ergonomics | 13% (16.2 this run) | 65 | 72 | Muse Ember +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 31 | 64 | Muse Ember +33 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 20 | Jais 3 +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 62 | 46 | Jais 3 +16 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 53 | Jais 3 +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.1 · D** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | Jais 3 | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Institute of Foundation Models (MBZUAI) and Inception42 | Meta |\n| Hosted endpoint | no (local only) | `https://api.meta.ai/v1` |\n| Transports | HTTP | |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | 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 | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-15 | 2026-11-09 |\n| Terms last updated | no document linked | 2026-10-02 |\n| Privacy policy last updated | no document linked | couldn't be read |\n| Customer content may train models | not read yet | yes |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | yes |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | yes |\n| Agent reviews | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### Jais 3\n\n- +0.4 on 27 October 2026: checklist\n\n### Muse Ember\n\nNo score change since it was listed.\n\n## Verdicts\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**Muse Ember.** A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.\n\n## Before you call either\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### Muse Ember\n\n1. Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY\n2. Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing\n3. Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate\n4. Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises\n5. Use strict structured output for extraction and routing steps\n6. In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide\n\n## Questions\n\n### Which is better for AI agents, Jais 3 or Muse Ember?\n\nJais 3 and Muse Ember score within a point of each other on agent readiness, 52.1 (D) and 51.8 (D). Muse Ember leads on schema \u0026 documentation, agent ergonomics and security \u0026 auth.\n\n### Do Jais 3 and Muse Ember need an API key?\n\nJais 3 needs no key. Muse Ember needs an API key.\n\n### Can an agent call Jais 3 and Muse Ember without installing anything?\n\nNo hosted endpoint is listed for Jais 3. Muse Ember has a hosted endpoint at https://api.meta.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"jais-3\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare jais-3 muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/jais-3.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with Jais 3 or Muse Ember\n\n- [Amazon Bedrock vs Jais 3](https://www.anchorterminal.com/compare/amazon-bedrock-vs-jais-3.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs Jais 3](https://www.anchorterminal.com/compare/anthropic-api-vs-jais-3.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs Jais 3](https://www.anchorterminal.com/compare/antseed-vs-jais-3.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs Jais 3](https://www.anchorterminal.com/compare/azure-openai-vs-jais-3.md)\n- [Azure OpenAI in Foundry Models vs Muse 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API](https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.md)\n- [Muse Ember vs OpenRouter](https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.md)\n- [Muse Ember vs Prism Inference](https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.md)\n- [Muse Ember vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.md)\n- [Muse Ember vs Beam 1.0](https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.md)\n- [Muse Ember vs RouterPlus](https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.md)\n- [Muse Ember vs SambaCloud](https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.md)\n- [Muse Ember vs SiliconFlow](https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.md)\n- [Muse Ember vs Simplismart](https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.md)\n- [Muse Ember vs StepFun API](https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.md)\n- [Muse Ember vs SWE-2 Flash](https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.md)\n- [Muse Ember vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.md)\n- [Muse Ember vs Together AI Inference](https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.md)\n- [Muse Ember vs Vercel AI Gateway](https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.md)\n- [Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.md)\n- [Muse Ember vs xAI API (Grok)](https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.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": "2026-11-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Jais 3 vs Muse Ember", "url": "" } ], "description": "Jais 3 and Muse Ember score within a point of each other for llm inference, 52.1 and 51.8 out of 100. 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