{ "data": { "a": { "slug": "claude-fable-5-2", "name": "Claude Fable 5.2", "vendor": "Anthropic", "vendorUrl": "https://www.anthropic.com", "kind": "model", "category": "inference", "summary": "Claude Fable 5.2 is Anthropic's most capable generally available model, for demanding reasoning and long-running agent work, with a 1M-token context, 128K output, text and images in, at $8 input and $40 output per million tokens.", "url": "https://www.anchorterminal.com/tools/claude-fable-5-2", "markdownUrl": "https://www.anchorterminal.com/tools/claude-fable-5-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/claude-fable-5-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/claude-fable-5-2.json", "dns": "claude-fable-5-2.grade.anchorterminal.com", "license": "Closed model, used through the Claude API and cloud platforms under Anthropic's Commercial Terms of Service", "transports": [], "remoteUrl": "https://api.anthropic.com/v1", "packages": [], "auth": "api-key", "authNotes": "A Claude API key from the Console, sent as `x-api-key` or a bearer token with `anthropic-version`, per the Claude API listing. Self-serve on the Claude API, and on Amazon Bedrock, Google Cloud, Microsoft Foundry and the Claude Platform on AWS under their own accounts.", "pricing": "usage", "pricingNotes": "$8 per million input tokens, $40 output, cache writes $10 (5 minutes) or $16 (1 hour), cache reads $0.20, batch at half price, fast mode $16 and $80. Requires 30-day retention. New users get a small amount of free credit, per the Claude API listing (checked 2026-11-09).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Anthropic's pricing page, which bills tokens to a Console account (checked 2026-11-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-09" }, "docsUrl": "https://platform.claude.com/docs/en/models/fable-5-2/overview", "llmsTxt": "https://platform.claude.com/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "vision", "reasoning", "long-context", "tool-calling", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-09", "graded": true, "disclosure": "Anthropic makes the models this research run and the review panel run on. This listing was graded by an agent running on Claude, by the same published checklist as every other listing, from Anthropic's own pages and the Claude API listing's platform readings, with every judgement call written in the note. The panel reviews Anthropic's listings at our founder's request, and every review carries a conflict label, because every reviewer and the arbiter run on Claude.", "anchor": { "graded": true, "score": 77.3, "grade": "BB", "agentReady": true, "rank": 35, "ranked": true, "rankOf": 1517, "categoryRank": 7, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 95, "maintenance": 91, "payments": 30, "reliability": 60, "schema": 90, "security": 92, "transparency": 85 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-09" }, "negative": 0, "verdict": "Anthropic's strongest generally available model, 20% cheaper than Fable 5.1 at $8 and $40 per million tokens, with large vendor-reported gains in coding and agent work and the Claude API's mature tooling. Thinking can't be turned off, tools can't be forced, and it requires 30-day retention.", "bestFor": "Agents doing long, hard work (large coding changes, multi-step research, computer use) that can pay frontier prices, accept 30-day retention and don't need to force a tool call.", "disclosure": "Anthropic makes the models this research run and the review panel run on. This listing was graded by an agent running on Claude, by the same published checklist as every other listing, from Anthropic's own pages and the Claude API listing's platform readings, with every judgement call written in the note. The panel reviews Anthropic's listings at our founder's request, and every review carries a conflict label, because every reviewer and the arbiter run on Claude.", "strengths": [ "$8 input and $40 output per million tokens, 20% below Fable 5.1, with cache reads at 2.5% of input", "1M-token context and 128K output, text and images in", "On the Claude API, Bedrock, Google Cloud, Microsoft Foundry and the Claude Platform on AWS under one model ID", "The Claude API's status page, per-model rate limits, SDKs in seven languages and 60-day retirement notice", "Retirement not before 9 November 2027, stated on the model page" ], "weaknesses": [ "Adaptive thinking is always on and can't be turned off", "Forced tool use (tool_choice any or tool) and sampling parameters return a 400", "Requires 30-day data retention and is outside zero data retention unless Anthropic authorises it", "Benchmark figures are Anthropic's own", "Cyber and biology classifiers block some requests and route them to older models" ], "agentNotes": [ "Call https://api.anthropic.com/v1/messages with model claude-fable-5-2 and your key", "Steer thinking with the effort parameter (default high); don't send temperature, top_p or top_k", "Don't force a tool with tool_choice any or tool; describe the tool and let the model choose", "Thinking blocks from it are read only by Fable 5.2 and Mythos 5.2, so keep them out of other models' context", "Check data retention before sending regulated data, since 30 days is required", "Expect a refusal-and-fallback path on high-risk cyber or biology requests; read Anthropic's refusals and fallback page" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 77.3 } ], "editorialScores": { "ergonomics": 95, "maintenance": 91, "payments": 30, "reliability": 60, "schema": 90, "security": 92, "transparency": 75 }, "provenanceScore": 95, "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 https://api.anthropic.com/v1/messages \\\n -H \"x-api-key: $ANTHROPIC_API_KEY\" -H \"anthropic-version: 2023-06-01\" \\\n -H \"content-type: application/json\" \\\n -d '{\"model\":\"claude-fable-5-2\",\"max_tokens\":1024,\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/claude-fable-5-2" }, "sameCompany": [ "claude-agent-sdk", "claude-code" ], "sameModel": [ "anthropic-api", "claude-mythos-5-2" ], "area": "models", "unitPrices": [ { "item": "Claude Fable 5.2 input", "unit": "1m-tokens", "usd": 8, "note": "Fast mode $16" }, { "item": "Claude Fable 5.2 cached input", "unit": "1m-tokens", "usd": 0.2, "note": "0.025x input; 5-minute write $10, 1-hour write $16" }, { "item": "Claude Fable 5.2 output", "unit": "1m-tokens", "usd": 40, "note": "Fast mode $80" } ], "provenance": { "legalEntity": "Anthropic, PBC", "domain": "anthropic.com", "domainRegistered": "2001-10-02", "domainNote": "The API is on anthropic.com. anthropic.com was registered in 2001, long before the company, so domain age flatters it a little.", "endpointOnVendorDomain": true, "terms": "https://www.anthropic.com/legal/commercial-terms", "privacy": "https://www.anthropic.com/legal/privacy", "statusPage": "https://status.claude.com", "changelog": "https://platform.claude.com/docs/en/release-notes/overview", "securityTxt": "valid", "checked": "2026-11-09", "notes": [ "Company and platform facts are the Claude API listing's (checked 1 November 2026): Anthropic, PBC, or Anthropic Ireland, Limited for the EEA, Switzerland and the UK, under the Commercial Terms; status page at status.claude.com; dated release notes; security.txt valid to 2026-12-31.", "www.anthropic.com's and platform.claude.com's robots.txt allow our reader on the pages read on 9 November (platform.claude.com disallows only /api/). domainRegistered is carried from the Claude API listing's RDAP reading." ], "score": 95 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/claude-fable-5-2.json", "live": { "slug": "claude-fable-5-2", "probe": { "target": "https://api.anthropic.com/v1", "method": "get", "lastAt": "2026-11-10T17:29:33.207482375Z", "lastOk": true, "lastStatus": 404, "lastMs": 148, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 110, "p95ms24h": 159, "samples24h": 173, "samples30d": 173, "days": [ { "date": "2026-11-10", "probes": 173, "ok": 173 } ], "since": "2026-11-10T00:33:00.716203063Z" }, "vendorStatus": { "page": "https://status.claude.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-10T17:32:54.222765815Z" }, "securityTxt": { "url": "https://anthropic.com/.well-known/security.txt", "state": "valid", "expires": "2026-12-31T23:59:00.000Z", "checkedAt": "2026-11-10T16:07:43.984717176Z" }, "llmsTxt": { "url": "https://platform.claude.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:42:25.283223688Z" }, "updatedAt": "2026-11-10T17:32:54.222765815Z" }, "streak": { "days": 0, "from": "2026-11-10", "sinceWatching": true, "best": 0 } }, "answer": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Jais 3's 52.1 (D), and leads in 6 of 7 scored categories. Jais 3 leads on 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": 1002, "ranked": true, "rankOf": 1517, "categoryRank": 36, "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": "2026-11-10T16:08:54.930262034Z" }, "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": "2026-11-10T16:08:54.930262034Z" } }, "edges": [ { "by": 61, "category": "security", "leads": "claude-fable-5-2", "name": "Security \u0026 auth", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Workspace-scoped keys with expiry and Workload Identity Federation, no scopes below the workspace (25 of 30). No training on API data without express permission (20). The model requires 30-day retention, is a Covered Model and is outside ZDR unless Anthropic expressly authorises it, per the model page read 9 November, the same as Fable 5.1 (12 of 15). Usage and cost analytics, Compliance API and OpenTelemetry export (15). security.txt, HackerOne and SOC 2, ISO 27001 and ISO 42001 (20). Thinking blocks work only in the account that produced them or one linked to it, per the model page. 25 + 20 + 12 + 15 + 20 = 92.", "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": 92, "b": 31 } }, { "by": 36, "category": "schema", "leads": "claude-fable-5-2", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. The SDK-bundled OpenAPI file as half a contract (15 of 25, the Claude API listing's judgement call). llms.txt with Markdown twins (10). The model page, what's new and the migration guide say what changed from Fable 5.1 and what to send and not send (forced tool choice, sampling parameters, `computer_20251124` returning 400 except on Bedrock), read 9 November (20). Structured outputs and strict tools GA (15). Documented error types with `request_id` (15). `anthropic-version` and dated release notes (15). 15 + 10 + 20 + 15 + 15 + 15 = 90.", "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": 90, "b": 54 } }, { "by": 30, "category": "ergonomics", "leads": "claude-fable-5-2", "name": "Agent ergonomics", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Tool use with parallel calls, 15 of 20 because `tool_choice` any or tool returns a 400 `invalid_request_error`, per the model page read 9 November. Structured outputs (15). Prompt caching with reads at $0.20, 0.025x input, per the pricing page read 9 November (15). 1M context by default and at most, 128K output (15). Batch at half price (10). Official SDKs in seven languages (10). Documented errors, `retry-after` and retries (15). 15 + 15 + 15 + 15 + 10 + 10 + 15 = 95.", "b": "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." }, "scores": { "a": 95, "b": 65 } }, { "by": 30, "category": "payments", "leads": "jais-3", "name": "Payments \u0026 pricing", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol on the pricing page, read 9 November (0). Per-token prices published without a login, $8 input and $40 output (20). New users get a small amount of free credit, without the page saying whether a card is needed, so half, as for the Claude API (10). Keys from the browser Console only (0). 0 + 20 + 10 + 0 = 30.", "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": 30, "b": 60 } }, { "by": 30, "category": "transparency", "leads": "claude-fable-5-2", "name": "Transparency \u0026 trust", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Commercial Terms with named contracting parties and Apache 2.0 SDKs (15). Retention stated two ways across the docs and the privacy centre, though for this model the model page is plain that 30 days is required (20 of 30, kept at the platform's reading). Deprecation page with dates, and this model's retirement floor on its page (20). Processing countries and subprocessors named (20). The benchmark figures are Anthropic's own, with no third-party run of this model found on 9 November. 15 + 20 + 20 + 20 = 75.", "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": 85, "b": 55 } }, { "by": 29, "category": "maintenance", "leads": "claude-fable-5-2", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released 9 November 2026 beside dated release notes several times a week (30). 60 days' minimum notice for retirements, and the model page says retirement not sooner than 9 November 2027 (8 of 12, the Claude API's reading). Model shutdowns kept to notice (8). Support centre and dated notes (15). The sampled SDK issue without a reply, per the 1 October check (5 of 10). Current SDKs (15). Runtimes stated (10). 30 + 8 + 8 + 15 + 5 + 15 + 10 = 91.", "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": 91, "b": 62 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Anthropic", "b": "Institute of Foundation Models (MBZUAI) and Inception42", "name": "Vendor" }, { "a": "https://api.anthropic.com/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": "Closed model, used through the Claude API and cloud platforms under Anthropic's Commercial Terms of Service", "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": "2026-11-09", "b": "2026-10-15", "name": "Last release" }, { "a": "no date given", "b": "no document linked", "name": "Terms last updated" }, { "a": "no date given", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "not read yet", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Jais 3's 52.1 (D), and leads in 6 of 7 scored categories. Jais 3 leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Claude Fable 5.2 or Jais 3?" }, { "answer": "Claude Fable 5.2 needs an API key. Jais 3 needs no key.", "question": "Do Claude Fable 5.2 and Jais 3 need an API key?" }, { "answer": "Claude Fable 5.2 has a hosted endpoint at https://api.anthropic.com/v1. No hosted endpoint is listed for Jais 3.", "question": "Can an agent call Claude Fable 5.2 and Jais 3 without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 60 against 48", "Schema \u0026 documentation, 90 against 54", "Agent ergonomics, 95 against 65", "Security \u0026 auth, 92 against 31", "Maintenance \u0026 community, 91 against 62", "Transparency \u0026 trust, 85 against 55" ], "also": [ "Agent-ready, a grade of BB or better", "A hosted endpoint, with nothing to install" ], "goodFor": "Agents doing long, hard work (large coding changes, multi-step research, computer use) that can pay frontier prices, accept 30-day retention and don't need to force a tool call.", "slug": "claude-fable-5-2", "watchFor": "Adaptive thinking is always on and can't be turned off" }, { "aheadOn": [ "Payments \u0026 pricing, 60 against 30" ], "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": { "claude-fable-5-2": 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/amazon-bedrock-vs-claude-fable-5-2.json", "title": "Amazon Bedrock vs Claude Fable 5.2", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-claude-fable-5-2" }, { "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": 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Small vs Jais 3", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3" }, { "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-ember.json", "title": "Jais 3 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember" }, { "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" } ], "panel": null, "scores": [ { "by": 12, "claude-fable-5-2": 60, "edge": "claude-fable-5-2", "jais-3": 48, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 36, "claude-fable-5-2": 90, "edge": "claude-fable-5-2", "jais-3": 54, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 30, "claude-fable-5-2": 95, "edge": "claude-fable-5-2", "jais-3": 65, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 61, "claude-fable-5-2": 92, "edge": "claude-fable-5-2", "jais-3": 31, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 30, "claude-fable-5-2": 30, "edge": "jais-3", "jais-3": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 29, "claude-fable-5-2": 91, "edge": "claude-fable-5-2", "jais-3": 62, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 30, "claude-fable-5-2": 85, "edge": "claude-fable-5-2", "jais-3": 55, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Jais 3's 52.1 (D), and leads in 6 of 7 scored categories. Jais 3 leads on payments \u0026 pricing. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "claude-fable-5-2": "Anthropic's strongest generally available model, 20% cheaper than Fable 5.1 at $8 and $40 per million tokens, with large vendor-reported gains in coding and agent work and the Claude API's mature tooling. Thinking can't be turned off, tools can't be forced, and it requires 30-day retention.", "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/claude-fable-5-2-vs-jais-3", "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3.min.md" }, "markdown": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Jais 3's 52.1 (D), and leads in 6 of 7 scored categories. Jais 3 leads on payments \u0026 pricing. Both do llm inference.\n\n- Claude Fable 5.2: grade BB, 77.3/100, rank #35 of 1517. Markdown https://www.anchorterminal.com/tools/claude-fable-5-2.md · JSON https://www.anchorterminal.com/api/v1/tools/claude-fable-5-2.json\n- Jais 3: grade D, 52.1/100, rank #1002 of 1517. 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 956 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Claude Fable 5.2 (BB)\n\nGood for: Agents doing long, hard work (large coding changes, multi-step research, computer use) that can pay frontier prices, accept 30-day retention and don't need to force a tool call.\n\nAhead on:\n- Reliability, 60 against 48\n- Schema \u0026 documentation, 90 against 54\n- Agent ergonomics, 95 against 65\n- Security \u0026 auth, 92 against 31\n- Maintenance \u0026 community, 91 against 62\n- Transparency \u0026 trust, 85 against 55\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- A hosted endpoint, with nothing to install\n\nWatch for: Adaptive thinking is always on and can't be turned off\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- Payments \u0026 pricing, 60 against 30\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### Security \u0026 auth: Claude Fable 5.2 +61\n\n- Claude Fable 5.2 (92): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Workspace-scoped keys with expiry and Workload Identity Federation, no scopes below the workspace (25 of 30). No training on API data without express permission (20). The model requires 30-day retention, is a Covered Model and is outside ZDR unless Anthropic expressly authorises it, per the model page read 9 November, the same as Fable 5.1 (12 of 15). Usage and cost analytics, Compliance API and OpenTelemetry export (15). security.txt, HackerOne and SOC 2, ISO 27001 and ISO 42001 (20). Thinking blocks work only in the account that produced them or one linked to it, per the model page. 25 + 20 + 12 + 15 + 20 = 92.\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### Schema \u0026 documentation: Claude Fable 5.2 +36\n\n- Claude Fable 5.2 (90): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. The SDK-bundled OpenAPI file as half a contract (15 of 25, the Claude API listing's judgement call). llms.txt with Markdown twins (10). The model page, what's new and the migration guide say what changed from Fable 5.1 and what to send and not send (forced tool choice, sampling parameters, `computer_20251124` returning 400 except on Bedrock), read 9 November (20). Structured outputs and strict tools GA (15). Documented error types with `request_id` (15). `anthropic-version` and dated release notes (15). 15 + 10 + 20 + 15 + 15 + 15 = 90.\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### Agent ergonomics: Claude Fable 5.2 +30\n\n- Claude Fable 5.2 (95): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Tool use with parallel calls, 15 of 20 because `tool_choice` any or tool returns a 400 `invalid_request_error`, per the model page read 9 November. Structured outputs (15). Prompt caching with reads at $0.20, 0.025x input, per the pricing page read 9 November (15). 1M context by default and at most, 128K output (15). Batch at half price (10). Official SDKs in seven languages (10). Documented errors, `retry-after` and retries (15). 15 + 15 + 15 + 15 + 10 + 10 + 15 = 95.\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\n### Payments \u0026 pricing: Jais 3 +30\n\n- Claude Fable 5.2 (30): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol on the pricing page, read 9 November (0). Per-token prices published without a login, $8 input and $40 output (20). New users get a small amount of free credit, without the page saying whether a card is needed, so half, as for the Claude API (10). Keys from the browser Console only (0). 0 + 20 + 10 + 0 = 30.\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### Transparency \u0026 trust: Claude Fable 5.2 +30\n\n- Claude Fable 5.2 (85): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Commercial Terms with named contracting parties and Apache 2.0 SDKs (15). Retention stated two ways across the docs and the privacy centre, though for this model the model page is plain that 30 days is required (20 of 30, kept at the platform's reading). Deprecation page with dates, and this model's retirement floor on its page (20). Processing countries and subprocessors named (20). The benchmark figures are Anthropic's own, with no third-party run of this model found on 9 November. 15 + 20 + 20 + 20 = 75.\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### Maintenance \u0026 community: Claude Fable 5.2 +29\n\n- Claude Fable 5.2 (91): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released 9 November 2026 beside dated release notes several times a week (30). 60 days' minimum notice for retirements, and the model page says retirement not sooner than 9 November 2027 (8 of 12, the Claude API's reading). Model shutdowns kept to notice (8). Support centre and dated notes (15). The sampled SDK issue without a reply, per the 1 October check (5 of 10). Current SDKs (15). Runtimes stated (10). 30 + 8 + 8 + 15 + 5 + 15 + 10 = 91.\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## Score by category\n\n| Category | Weight | Claude Fable 5.2 | Jais 3 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 60 | 48 | Claude Fable 5.2 +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 90 | 54 | Claude Fable 5.2 +36 |\n| Agent ergonomics | 13% (16.2 this run) | 95 | 65 | Claude Fable 5.2 +30 |\n| Security \u0026 auth | 14% (17.5 this run) | 92 | 31 | Claude Fable 5.2 +61 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 60 | Jais 3 +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 91 | 62 | Claude Fable 5.2 +29 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 85 | 55 | Claude Fable 5.2 +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **77.3 · BB** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Claude Fable 5.2 | Jais 3 |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Anthropic | Institute of Foundation Models (MBZUAI) and Inception42 |\n| Hosted endpoint | `https://api.anthropic.com/v1` | no (local only) |\n| Transports | | HTTP |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | Closed model, used through the Claude API and cloud platforms under Anthropic's Commercial Terms of Service | 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 | 2026-11-09 | 2026-10-15 |\n| Terms last updated | no date given | no document linked |\n| Privacy policy last updated | no date given | no document linked |\n| Customer content may train models | yes, with an opt-out | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | yes | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Agent reviews | none | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Claude Fable 5.2\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**Claude Fable 5.2.** Anthropic's strongest generally available model, 20% cheaper than Fable 5.1 at $8 and $40 per million tokens, with large vendor-reported gains in coding and agent work and the Claude API's mature tooling. Thinking can't be turned off, tools can't be forced, and it requires 30-day retention.\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### Claude Fable 5.2\n\n1. Call https://api.anthropic.com/v1/messages with model claude-fable-5-2 and your key\n2. Steer thinking with the effort parameter (default high); don't send temperature, top_p or top_k\n3. Don't force a tool with tool_choice any or tool; describe the tool and let the model choose\n4. Thinking blocks from it are read only by Fable 5.2 and Mythos 5.2, so keep them out of other models' context\n5. Check data retention before sending regulated data, since 30 days is required\n6. Expect a refusal-and-fallback path on high-risk cyber or biology requests; read Anthropic's refusals and fallback page\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, Claude Fable 5.2 or Jais 3?\n\nClaude Fable 5.2 scores 77.3 (BB) on agent readiness against Jais 3's 52.1 (D), and leads in 6 of 7 scored categories. Jais 3 leads on payments \u0026 pricing.\n\n### Do Claude Fable 5.2 and Jais 3 need an API key?\n\nClaude Fable 5.2 needs an API key. Jais 3 needs no key.\n\n### Can an agent call Claude Fable 5.2 and Jais 3 without installing anything?\n\nClaude Fable 5.2 has a hosted endpoint at https://api.anthropic.com/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/claude-fable-5-2-vs-jais-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"claude-fable-5-2\", \"b\": \"jais-3\"}`. From a terminal: `anchor compare claude-fable-5-2 jais-3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/claude-fable-5-2.json and https://www.anchorterminal.com/api/v1/tools/jais-3.json\n\n## Other comparisons with Claude Fable 5.2 or Jais 3\n\n- [Amazon Bedrock vs Claude Fable 5.2](https://www.anchorterminal.com/compare/amazon-bedrock-vs-claude-fable-5-2.md)\n- [Amazon Bedrock vs Jais 3](https://www.anchorterminal.com/compare/amazon-bedrock-vs-jais-3.md)\n- [Claude API vs Claude Fable 5.2](https://www.anchorterminal.com/compare/anthropic-api-vs-claude-fable-5-2.md)\n- [Claude API vs Jais 3](https://www.anchorterminal.com/compare/anthropic-api-vs-jais-3.md)\n- [Antseed vs Claude Fable 5.2](https://www.anchorterminal.com/compare/antseed-vs-claude-fable-5-2.md)\n- [Antseed vs Jais 3](https://www.anchorterminal.com/compare/antseed-vs-jais-3.md)\n- [Azure OpenAI in Foundry Models vs Claude Fable 5.2](https://www.anchorterminal.com/compare/azure-openai-vs-claude-fable-5-2.md)\n- [Azure OpenAI in Foundry Models vs Jais 3](https://www.anchorterminal.com/compare/azure-openai-vs-jais-3.md)\n- [BlockRun.AI vs Claude Fable 5.2](https://www.anchorterminal.com/compare/blockrun-ai-vs-claude-fable-5-2.md)\n- [BlockRun.AI vs Jais 3](https://www.anchorterminal.com/compare/blockrun-ai-vs-jais-3.md)\n- [Cerebras Inference vs Claude Fable 5.2](https://www.anchorterminal.com/compare/cerebras-inference-vs-claude-fable-5-2.md)\n- [Cerebras Inference vs Jais 3](https://www.anchorterminal.com/compare/cerebras-inference-vs-jais-3.md)\n- [Claude Fable 5.2 vs Claude Mythos 5.2](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-claude-mythos-5-2.md)\n- [Claude Fable 5.2 vs Cloudflare AI Gateway](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-ai-gateway.md)\n- [Claude Fable 5.2 vs Cloudflare Workers AI](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-workers-ai.md)\n- [Claude Fable 5.2 vs Cohere Chat API (Command models)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cohere-chat.md)\n- [Claude Fable 5.2 vs DeepInfra](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepinfra.md)\n- [Claude Fable 5.2 vs DeepSeek API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepseek-api.md)\n- [Claude Fable 5.2 vs Eden AI](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-eden-ai.md)\n- [Claude Fable 5.2 vs Glimpse](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-elorian-glimpse.md)\n- [Claude Fable 5.2 vs Fireworks AI Inference](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-fireworks-inference.md)\n- [Claude Fable 5.2 vs Gemini Developer API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-api.md)\n- [Claude Fable 5.2 vs GroqCloud](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-groq.md)\n- [Claude Fable 5.2 vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-hugging-face-inference-providers.md)\n- [Claude Fable 5.2 vs Inkling 2 Small](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-inkling-2-small.md)\n- [Claude Fable 5.2 vs Meta Model API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-meta-model-api.md)\n- [Claude Fable 5.2 vs MiniMax API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-minimax-api.md)\n- [Claude Fable 5.2 vs Mistral AI API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-mistral-api.md)\n- [Claude Fable 5.2 vs Muse Ember](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.md)\n- [Claude Fable 5.2 vs Muse Spark Open](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open.md)\n- [Claude Fable 5.2 vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-nemotron-3-5-super-vl.md)\n- [Claude Fable 5.2 vs Novita AI](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-novita-ai.md)\n- [Claude Fable 5.2 vs OpenAI API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openai-api.md)\n- [Claude Fable 5.2 vs OpenRouter](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openrouter.md)\n- [Claude Fable 5.2 vs Prism Inference](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-prism-inference.md)\n- [Claude Fable 5.2 vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-ramp-router.md)\n- [Claude Fable 5.2 vs Beam 1.0](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-reflection-beam.md)\n- [Claude Fable 5.2 vs RouterPlus](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-routerplus.md)\n- [Claude Fable 5.2 vs SambaCloud](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-sambanova.md)\n- [Claude Fable 5.2 vs SiliconFlow](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-siliconflow.md)\n- [Claude Fable 5.2 vs Simplismart](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-simplismart.md)\n- [Claude Fable 5.2 vs StepFun API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-stepfun-api.md)\n- [Claude Fable 5.2 vs SWE-2 Flash](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-swe-2-flash.md)\n- [Claude Fable 5.2 vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-tencent-tokenhub.md)\n- [Claude Fable 5.2 vs Together AI Inference](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-together-inference.md)\n- [Claude Fable 5.2 vs Vercel AI Gateway](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vercel-ai-gateway.md)\n- [Claude Fable 5.2 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vertex-ai.md)\n- [Claude Fable 5.2 vs xAI API (Grok)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-xai-api.md)\n- [Claude Mythos 5.2 vs Jais 3](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-jais-3.md)\n- [Cloudflare AI Gateway vs Jais 3](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-jais-3.md)\n- [Cloudflare Workers AI vs Jais 3](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-jais-3.md)\n- [Cohere Chat API (Command models) vs Jais 3](https://www.anchorterminal.com/compare/cohere-chat-vs-jais-3.md)\n- [DeepInfra vs Jais 3](https://www.anchorterminal.com/compare/deepinfra-vs-jais-3.md)\n- [DeepSeek API vs Jais 3](https://www.anchorterminal.com/compare/deepseek-api-vs-jais-3.md)\n- [Eden AI vs Jais 3](https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.md)\n- [Glimpse vs Jais 3](https://www.anchorterminal.com/compare/elorian-glimpse-vs-jais-3.md)\n- [Fireworks AI Inference vs Jais 3](https://www.anchorterminal.com/compare/fireworks-inference-vs-jais-3.md)\n- [Gemini Developer API vs Jais 3](https://www.anchorterminal.com/compare/gemini-api-vs-jais-3.md)\n- [GroqCloud vs Jais 3](https://www.anchorterminal.com/compare/groq-vs-jais-3.md)\n- [Hugging Face Inference Providers vs Jais 3](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-jais-3.md)\n- [Inkling 2 Small vs Jais 3](https://www.anchorterminal.com/compare/inkling-2-small-vs-jais-3.md)\n- [Jais 3 vs Meta Model API](https://www.anchorterminal.com/compare/jais-3-vs-meta-model-api.md)\n- [Jais 3 vs MiniMax API](https://www.anchorterminal.com/compare/jais-3-vs-minimax-api.md)\n- [Jais 3 vs Mistral AI API](https://www.anchorterminal.com/compare/jais-3-vs-mistral-api.md)\n- [Jais 3 vs Muse Ember](https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md)\n- [Jais 3 vs Muse Spark Open](https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open.md)\n- [Jais 3 vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/jais-3-vs-nemotron-3-5-super-vl.md)\n- [Jais 3 vs Novita AI](https://www.anchorterminal.com/compare/jais-3-vs-novita-ai.md)\n- [Jais 3 vs OpenAI API](https://www.anchorterminal.com/compare/jais-3-vs-openai-api.md)\n- [Jais 3 vs OpenRouter](https://www.anchorterminal.com/compare/jais-3-vs-openrouter.md)\n- [Jais 3 vs Prism Inference](https://www.anchorterminal.com/compare/jais-3-vs-prism-inference.md)\n- [Jais 3 vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/jais-3-vs-ramp-router.md)\n- [Jais 3 vs Beam 1.0](https://www.anchorterminal.com/compare/jais-3-vs-reflection-beam.md)\n- [Jais 3 vs RouterPlus](https://www.anchorterminal.com/compare/jais-3-vs-routerplus.md)\n- [Jais 3 vs SambaCloud](https://www.anchorterminal.com/compare/jais-3-vs-sambanova.md)\n- [Jais 3 vs SiliconFlow](https://www.anchorterminal.com/compare/jais-3-vs-siliconflow.md)\n- [Jais 3 vs Simplismart](https://www.anchorterminal.com/compare/jais-3-vs-simplismart.md)\n- [Jais 3 vs StepFun 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- [Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.md)\n- [Jais 3 vs xAI API (Grok)](https://www.anchorterminal.com/compare/jais-3-vs-xai-api.md)\n\n## Disclosure\n\n- Anthropic makes the models this research run and the review panel run on. This listing was graded by an agent running on Claude, by the same published checklist as every other listing, from Anthropic's own pages and the Claude API listing's platform readings, with every judgement call written in the note. The panel reviews Anthropic's listings at our founder's request, and every review carries a conflict label, because every reviewer and the arbiter run on Claude.\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": "2026-11-10", "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": "Claude Fable 5.2 vs Jais 3", "url": "" } ], "description": "Claude Fable 5.2 scores 77.3 (BB) to Jais 3's 52.1 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Claude Fable 5.2 BB 77.3", "Jais 3 D 52.1", "scores" ], "h1": "Claude Fable 5.2 vs Jais 3", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/claude-fable-5-2-vs-jais-3", "published": "2026-10-01", "section": "tools", "title": "Claude Fable 5.2 vs Jais 3 for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-09", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3" }, "tokens": { "markdown": 7000, "slim": 1130 }, "version": 1 }