{ "data": { "a": { "slug": "gliclass", "name": "GLiClass", "vendor": "Knowledgator", "vendorUrl": "https://www.knowledgator.com", "kind": "model", "category": "decision-models", "summary": "GLiClass is an open-source Python library and family of open-weight zero-shot text classifiers from Knowledgator. It scores every candidate label in one forward pass and runs locally through a pipeline or a Ray Serve endpoint.", "url": "https://www.anchorterminal.com/tools/gliclass", "markdownUrl": "https://www.anchorterminal.com/tools/gliclass.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gliclass.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gliclass.json", "repo": "https://github.com/Knowledgator/GLiClass", "license": "Apache-2.0 (library and the model weights we checked)", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "gliclass" } ], "auth": "none", "authNotes": "No account or key. The weights are public and ungated on Hugging Face. The bundled Ray Serve deployment has no authentication of any kind, and `python -m gliclass.serve` binds to 0.0.0.0 unless `--host` is passed, so the owner has to restrict the port.", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy for the library or the weights. Hardware is the owner's cost. Knowledgator's site links a hosted platform with plans at platform.knowledgator.com, which we couldn't reach on 8 October 2026, so whether it serves GLiClass and at what price is unchecked.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. GLiClass is software the owner runs, and its server has no payment route (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 555, "npmWeekly": null, "pypiWeekly": 13204, "asOf": "2026-10-08" }, "docsUrl": "https://docs.knowledgator.com/docs/frameworks/gliclass/", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "no-auth" ], "lastRelease": "2026-07-21", "graded": true, "anchor": { "graded": true, "score": 49.9, "grade": "D", "agentReady": false, "rank": 697, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 44, "payments": 60, "reliability": 43, "schema": 49, "security": 38, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.", "bestFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.", "strengths": [ "Apache-2.0 code and weights, with `train.py`, the training datasets named on the model cards and an arXiv paper (2508.07662)", "One forward pass scores every label. The base v3.0 card reports 51.6 examples a second averaged over 1 to 128 labels on an A6000 (vendor figures)", "Single-label (softmax) and multi-label (sigmoid) modes, hierarchical label sets, task prompts and few-shot examples in one pipeline call", "A Ray Serve deployment with dynamic batching ships in the `gliclass[serve]` extra, with a documented request table for `POST /gliclass`", "31 public, ungated GLiClass checkpoints on Hugging Face, from 32.7M to 439M parameters, as safetensors" ], "weaknesses": [ "No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic", "The bundled server has no authentication, and `python -m gliclass.serve` binds to 0.0.0.0 by default", "The last three runs of the Tests workflow on main, all on 24 September 2026, failed", "Still 0.1.x with no changelog file. 0.1.18 raised the `transformers` floor to 5.0, and issue #44 on v5 loading has been open since 6 July 2026", "No OpenAPI file, no llms.txt, no SECURITY.md and no documented error responses" ], "agentNotes": [ "Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential", "On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA", "Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error", "Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1", "Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 49.9 } ], "editorialScores": { "ergonomics": 60, "maintenance": 44, "payments": 60, "reliability": 43, "schema": 49, "security": 38, "transparency": 60 }, "provenanceScore": 68 }, "connect": { "install": "pip install gliclass", "http": "pip install \"gliclass[serve]\"\npython -m gliclass.serve --model knowledgator/gliclass-edge-v3.0 --port 8000\ncurl -X POST http://localhost:8000/gliclass \\\n -H \"Content-Type: application/json\" \\\n -d '{\"text\": \"This is a great product.\", \"labels\": [\"positive\", \"negative\", \"neutral\"], \"threshold\": 0.3, \"multi_label\": true}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/gliclass" }, "area": "models", "provenance": { "legalEntity": "Knowledgator Engineering Ltd.", "domain": "knowledgator.com", "domainRegistered": "2021-06-25", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/Knowledgator/GLiClass/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The terms of use and privacy policy linked from knowledgator.com name Knowledgator Engineering Ltd., London, United Kingdom. Both are dated 21 September 2023.", "Those two documents govern the website and Knowledgator's hosted services, not the library, so `terms` and `privacy` are left out. The Apache-2.0 licence governs what an agent would run.", "Software the owner runs, so there is no hosted endpoint and no status page for it.", "www.knowledgator.com/.well-known/security.txt returns 404, and the repository has no SECURITY.md.", "RDAP for knowledgator.com gives a registration date of 2021-06-25.", "The code is on github.com under the Knowledgator organisation and the weights on huggingface.co under knowledgator." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gliclass.json" }, "answer": "Jev scores 62.1 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing and transparency \u0026 trust.", "b": { "slug": "typesafe-jev", "name": "Jev", "vendor": "TypeSafe AI", "vendorUrl": "https://typesafe.ai", "kind": "model", "category": "decision-models", "summary": "Jev is TypeSafe AI's first System One model, a closed decision model behind an HTTP API.", "url": "https://www.anchorterminal.com/tools/typesafe-jev", "markdownUrl": "https://www.anchorterminal.com/tools/typesafe-jev.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/typesafe-jev.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/typesafe-jev.json", "repo": "https://github.com/typesafe-ai/typesafe-sdk-python", "license": "Proprietary model under TypeSafe's Master Customer Agreement. The Python and TypeScript SDKs are MIT", "transports": [ "http" ], "remoteUrl": "https://api.typesafe.ai/v1/systemone", "packages": [ { "registry": "pypi", "name": "typesafe-sdk" }, { "registry": "npm", "name": "@typesafe-ai/sdk" } ], "auth": "api-key", "authNotes": "Bearer API key in the `Authorization` header, created at console.typesafe.ai/keys after signing in. The SDKs read `TYPESAFE_API_KEY`. We found no documentation of key scopes, expiry or rotation.", "pricing": "usage", "pricingNotes": "Jev 1.13 costs $0.042 per million input tokens ($42 per billion), and output tokens are free (https://docs.typesafe.ai/models). Jev is in early access with a waitlist, and we found no free tier or free credits. Credits bought under the Master Customer Agreement expire 12 months after purchase and aren't refunded on termination (https://typesafe.ai/legal/mca). Higher rate limits come with custom and enterprise plans, and zero data retention is arranged with sales.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs, the OpenAPI document or the SDKs (checked 2026-10-02).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 15, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-02" }, "docsUrl": "https://docs.typesafe.ai/introduction", "llmsTxt": "https://docs.typesafe.ai/llms.txt", "openapi": "https://api.typesafe.ai/openapi.json", "capabilities": [ "inference.decision" ], "tags": [ "hosted", "model", "closed-source", "openapi", "llms-txt", "python", "typescript", "usage-priced", "status-page", "beta" ], "lastRelease": "2026-09-26", "graded": true, "anchor": { "graded": true, "score": 62.1, "grade": "B", "agentReady": false, "rank": 400, "ranked": true, "rankOf": 842, "categoryRank": 7, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 84, "maintenance": 64, "payments": 20, "reliability": 60, "schema": 87, "security": 53, "transparency": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Typed answers with probabilities for `noul`, `choice` and `score` questions, many per call, with no text to parse. Early access behind a waitlist, with no free tier or free credits found.", "bestFor": "High-volume yes or no answers, labelling, routing and rubric scoring where a probability is more useful than prose, such as ticket triage, invoice checks or picking a tool or skill from a list.", "strengths": [ "Typed answers with probabilities for `noul`, `choice` and `score` questions, many per call, with no text to parse", "$0.042 per million input tokens, with output tokens free", "A public OpenAPI 3.1 document, llms.txt with Markdown twins, and Python and TypeScript SDKs that retry 408, 429 and 5xx with backoff", "A known-limitations page that warns injected instructions can move an answer and lists what Jev can't do, such as counting, arithmetic and date comparison", "The privacy policy, DPA and customer agreement agree that customer data isn't used for training" ], "weaknesses": [ "Early access behind a waitlist, with no free tier or free credits found", "No SLA, and the customer agreement promises only commercially reasonable efforts to give notice of API changes", "Published limits of 40 requests a second can change without notice", "No security.txt and no stated retention period, and the trust centre and subprocessor list render only with JavaScript", "Closed and text only, with 32,000 tokens for state plus the longest question" ], "agentNotes": [ "Put every independent question about one state into a single call. They run in parallel and the state is billed once", "Pin `jev-1.13.0` instead of `jev-latest` once you've tuned confidence thresholds", "Back off exponentially on 429 and 529. The limits move with demand", "Keep state to what the decision needs. Accuracy falls as unrelated content grows, and state plus the longest question must fit in 32,000 tokens", "Treat an answer about user-supplied text as a judgement that hostile text can steer, and cap what one answer can trigger" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.1 } ], "editorialScores": { "ergonomics": 84, "maintenance": 64, "payments": 20, "reliability": 60, "schema": 87, "security": 53, "transparency": 41 }, "provenanceScore": 70 }, "connect": { "install": "pip install typesafe-sdk # or: npm install @typesafe-ai/sdk", "http": "curl https://api.typesafe.ai/v1/systemone \\\n -H \"Authorization: Bearer $TYPESAFE_API_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"jev-latest\",\"state\":\"Checkout has failed for every customer for an hour.\",\"questions\":{\"urgent\":{\"type\":\"noul\",\"instructions\":\"Is this request urgent?\"},\"team\":{\"type\":\"choice\",\"criteria\":{\"billing\":\"Payments and refunds\",\"technical\":\"Outages and errors\"}}}}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/typesafe-jev" }, "area": "models", "unitPrices": [ { "item": "Jev 1.13 input", "unit": "1m-tokens", "usd": 0.042, "note": "Output tokens free" } ], "provenance": { "legalEntity": "TypeSafe AI, Inc.", "domain": "typesafe.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://typesafe.ai/legal/mca", "privacy": "https://typesafe.ai/legal/privacy-policy", "statusPage": "https://status.typesafe.ai", "changelog": "https://docs.typesafe.ai/sdk/python/changelog", "securityTxt": "none", "checked": "2026-10-01", "notes": [ "The Master Customer Agreement (updated 23 September 2026) governs use of the API. The site's terms of use (19 September 2026) name TypeSafe AI, Inc. and Delaware law.", "typesafe.ai/.well-known/security.txt returns 404.", "We couldn't read the domain's registration date, since RDAP refused our reader.", "The changelog is the Python SDK's. We found no changelog for the API or the model." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/typesafe-jev.json", "live": { "slug": "typesafe-jev", "probe": { "target": "https://api.typesafe.ai/v1/systemone", "method": "get", "lastAt": "2026-10-09T11:46:44.163077103Z", "lastOk": true, "lastStatus": 405, "lastMs": 228, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 190, "p95ms24h": 259, "samples24h": 259, "samples30d": 1852, "days": [ { "date": "2026-10-02", "probes": 100, "ok": 100 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 125, "ok": 125 } ] }, "vendorStatus": { "page": "https://status.typesafe.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T07:58:37.265872388Z" }, "versions": [ { "registry": "github", "name": "typesafe-ai/typesafe-sdk-python", "version": "v0.7.2", "released": "2026-09-26", "seenAt": "2026-10-08T16:33:07.561621607Z" }, { "registry": "npm", "name": "@typesafe-ai/sdk", "version": "0.6.0", "seenAt": "2026-10-08T16:33:07.140594035Z" }, { "registry": "pypi", "name": "typesafe-sdk", "version": "0.7.2", "released": "2026-09-26", "seenAt": "2026-10-08T16:33:06.953697003Z" } ], "githubStars": 276, "npmWeekly": 1500722, "pypiWeekly": 1035871, "securityTxt": { "url": "https://typesafe.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:47.390293407Z" }, "llmsTxt": { "url": "https://docs.typesafe.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:57.169110601Z" }, "domain": { "domain": "typesafe.ai", "registered": "2024-05-07", "source": "https://rdap.identitydigital.services/rdap/domain/typesafe.ai", "checkedAt": "2026-10-04T13:08:19.224445149Z" }, "pages": [ { "url": "https://docs.typesafe.ai/sdk/python/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:19:40.416326003Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "1652185fb0d5" }, { "url": "https://typesafe.ai/legal/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:25:25.567047812Z", "changedAt": "2026-10-08T18:25:25.567047812Z", "fingerprint": "afc3649e2f1b" }, { "url": "https://typesafe.ai/legal/mca", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:25:23.25255997Z", "changedAt": "2026-10-08T18:25:23.25255997Z", "fingerprint": "e32b05842307" } ], "updatedAt": "2026-10-09T11:46:44.163077103Z" } }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Knowledgator", "b": "TypeSafe AI", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.typesafe.ai/v1/systemone", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "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 (library and the model weights we checked)", "b": "Proprietary model under TypeSafe's Master Customer Agreement. The Python and TypeScript SDKs are MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-07-21", "b": "2026-09-26", "name": "Last release" }, { "a": "no document linked", "b": "no date given", "name": "Terms last updated" }, { "a": "no document linked", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "555 stars, 13k PyPI/wk", "b": "15 stars", "name": "Popularity" }, { "a": "none", "b": "3/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Jev scores 62.1 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing and transparency \u0026 trust.", "question": "Which is better for AI agents, GLiClass or Jev?" }, { "answer": "GLiClass needs no key. Jev needs an API key.", "question": "Do GLiClass and Jev need an API key?" }, { "answer": "No hosted endpoint is listed for GLiClass. Jev has a hosted endpoint at https://api.typesafe.ai/v1/systemone.", "question": "Can an agent call GLiClass and Jev without installing anything?" }, { "answer": "GLiClass is open source (Apache-2.0 (library and the model weights we checked)). No open-source release is listed for Jev.", "question": "Are GLiClass and Jev open source?" } ], "goodFor": [ { "aheadOn": [ "Payments \u0026 pricing, 60 against 20", "Transparency \u0026 trust, 64 against 56" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.", "slug": "gliclass", "watchFor": "No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic" }, { "aheadOn": [ "Reliability, 60 against 43", "Schema \u0026 documentation, 87 against 49", "Agent ergonomics, 84 against 60", "Security \u0026 auth, 53 against 38", "Maintenance \u0026 community, 64 against 44" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "High-volume yes or no answers, labelling, routing and rubric scoring where a probability is more useful than prose, such as ticket triage, invoice checks or picking a tool or skill from a list.", "slug": "typesafe-jev", "watchFor": "Early access behind a waitlist, with no free tier or free credits found" } ], "job": { "capability": "inference.decision", "name": "Inference decision" }, "others": [ { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.json", "title": "Celeris-1 Decision vs GLiClass", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-typesafe-jev.json", "title": "Celeris-1 Decision vs Jev", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.json", "title": "Clef vs GLiClass", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-typesafe-jev.json", "title": "Clef vs Jev", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.json", "title": "Laya vs GLiClass", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev.json", "title": "Laya vs Jev", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/decider-vs-gliclass.json", "title": "Decider vs GLiClass", "url": "https://www.anchorterminal.com/compare/decider-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/decider-vs-typesafe-jev.json", "title": "Decider vs Jev", "url": "https://www.anchorterminal.com/compare/decider-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.json", "title": "GLiClass vs Kev", "url": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.json", "title": "GLiClass vs Liquid d1", "url": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.json", "title": "GLiClass vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.json", "title": "GLiClass vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-vela.json", "title": "GLiClass vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/gliclass-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-typesafe-jev.json", "title": "Kev vs Jev", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev.json", "title": "Liquid d1 vs Jev", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-typesafe-jev.json", "title": "OpenAI Decisions API vs Jev", "url": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/strands-decider-vs-typesafe-jev.json", "title": "Strands Decider 2B vs Jev", "url": "https://www.anchorterminal.com/compare/strands-decider-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/typesafe-jev-vs-vela.json", "title": "Jev vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/typesafe-jev-vs-vela" } ], "scores": [ { "by": 17, "edge": "typesafe-jev", "gliclass": 43, "key": "reliability", "name": "Reliability", "typesafe-jev": 60, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 38, "edge": "typesafe-jev", "gliclass": 49, "key": "schema", "name": "Schema \u0026 documentation", "typesafe-jev": 87, "weight": 13 }, { "by": 24, "edge": "typesafe-jev", "gliclass": 60, "key": "ergonomics", "name": "Agent ergonomics", "typesafe-jev": 84, "weight": 13 }, { "by": 15, "edge": "typesafe-jev", "gliclass": 38, "key": "security", "name": "Security \u0026 auth", "typesafe-jev": 53, "weight": 14 }, { "by": 40, "edge": "gliclass", "gliclass": 60, "key": "payments", "name": "Payments \u0026 pricing", "typesafe-jev": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 20, "edge": "typesafe-jev", "gliclass": 44, "key": "maintenance", "name": "Maintenance \u0026 community", "typesafe-jev": 64, "weight": 7 }, { "by": 8, "edge": "gliclass", "gliclass": 64, "key": "transparency", "name": "Transparency \u0026 trust", "typesafe-jev": 56, "weight": 7 } ], "summary": "Jev scores 62.1 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing and transparency \u0026 trust. Both do inference decision.", "verdicts": { "gliclass": "An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.", "typesafe-jev": "Typed answers with probabilities for `noul`, `choice` and `score` questions, many per call, with no text to parse. Early access behind a waitlist, with no free tier or free credits found." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev", "json": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.md", "slim": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.min.md" }, "markdown": "Jev scores 62.1 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing and transparency \u0026 trust. Both do inference decision.\n\n- GLiClass: grade D, 49.9/100, rank #697 of 842. Markdown https://www.anchorterminal.com/tools/gliclass.md · JSON https://www.anchorterminal.com/api/v1/tools/gliclass.json\n- Jev: grade B, 62.1/100, rank #400 of 842. Markdown https://www.anchorterminal.com/tools/typesafe-jev.md · JSON https://www.anchorterminal.com/api/v1/tools/typesafe-jev.json\n\n## Which one, for what\n\n### GLiClass (D)\n\nGood for: Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.\n\nAhead on:\n- Payments \u0026 pricing, 60 against 20\n- Transparency \u0026 trust, 64 against 56\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic\n\n### Jev (B)\n\nGood for: High-volume yes or no answers, labelling, routing and rubric scoring where a probability is more useful than prose, such as ticket triage, invoice checks or picking a tool or skill from a list.\n\nAhead on:\n- Reliability, 60 against 43\n- Schema \u0026 documentation, 87 against 49\n- Agent ergonomics, 84 against 60\n- Security \u0026 auth, 53 against 38\n- Maintenance \u0026 community, 64 against 44\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Early access behind a waitlist, with no free tier or free credits found\n\n\n## Score by category\n\n| Category | Weight | GLiClass | Jev | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 43 | 60 | Jev +17 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 49 | 87 | Jev +38 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 84 | Jev +24 |\n| Security \u0026 auth | 14% (17.5 this run) | 38 | 53 | Jev +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 20 | GLiClass +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 44 | 64 | Jev +20 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 64 | 56 | GLiClass +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.9 · D** | **62.1 · B** | |\n\n## Facts side by side\n\n| Fact | GLiClass | Jev |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Knowledgator | TypeSafe AI |\n| Hosted endpoint | no (local only) | `https://api.typesafe.ai/v1/systemone` |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (library and the model weights we checked) | Proprietary model under TypeSafe's Master Customer Agreement. The Python and TypeScript SDKs are MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-07-21 | 2026-09-26 |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | no document linked | no date given |\n| Customer content may train models | | not found in the text |\n| Terms restrict automated access | | not found in the text |\n| Terms restrict benchmarking | | yes |\n| Terms or service can change without notice | | not found in the text |\n| Arbitration or class-action waiver | | yes |\n| Popularity | 555 stars, 13k PyPI/wk | 15 stars |\n| Agent reviews | none | 3/5 (2) |\n\n## Verdicts\n\n**GLiClass.** An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.\n\n**Jev.** Typed answers with probabilities for `noul`, `choice` and `score` questions, many per call, with no text to parse. Early access behind a waitlist, with no free tier or free credits found.\n\n## Before you call either\n\n### GLiClass\n\n1. Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential\n2. On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA\n3. Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error\n4. Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1\n5. Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently\n\n### Jev\n\n1. Put every independent question about one state into a single call. They run in parallel and the state is billed once\n2. Pin `jev-1.13.0` instead of `jev-latest` once you've tuned confidence thresholds\n3. Back off exponentially on 429 and 529. The limits move with demand\n4. Keep state to what the decision needs. Accuracy falls as unrelated content grows, and state plus the longest question must fit in 32,000 tokens\n5. Treat an answer about user-supplied text as a judgement that hostile text can steer, and cap what one answer can trigger\n\n## Questions\n\n### Which is better for AI agents, GLiClass or Jev?\n\nJev scores 62.1 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing and transparency \u0026 trust.\n\n### Do GLiClass and Jev need an API key?\n\nGLiClass needs no key. Jev needs an API key.\n\n### Can an agent call GLiClass and Jev without installing anything?\n\nNo hosted endpoint is listed for GLiClass. Jev has a hosted endpoint at https://api.typesafe.ai/v1/systemone.\n\n### Are GLiClass and Jev open source?\n\nGLiClass is open source (Apache-2.0 (library and the model weights we checked)). No open-source release is listed for Jev.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gliclass\", \"b\": \"typesafe-jev\"}`. From a terminal: `anchor compare gliclass typesafe-jev`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gliclass.json and https://www.anchorterminal.com/api/v1/tools/typesafe-jev.json\n\n## Other comparisons with GLiClass or Jev\n\n- [Celeris-1 Decision vs GLiClass](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.md)\n- [Celeris-1 Decision vs Jev](https://www.anchorterminal.com/compare/celeris-1-decision-vs-typesafe-jev.md)\n- [Clef vs GLiClass](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.md)\n- [Clef vs Jev](https://www.anchorterminal.com/compare/cloudflare-clef-vs-typesafe-jev.md)\n- [Laya vs GLiClass](https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.md)\n- [Laya vs Jev](https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev.md)\n- [Decider vs GLiClass](https://www.anchorterminal.com/compare/decider-vs-gliclass.md)\n- [Decider vs Jev](https://www.anchorterminal.com/compare/decider-vs-typesafe-jev.md)\n- [GLiClass vs Kev](https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.md)\n- [GLiClass vs Liquid d1](https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.md)\n- [GLiClass vs OpenAI Decisions API](https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.md)\n- [GLiClass vs Strands Decider 2B](https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.md)\n- [GLiClass vs Vela 2.0](https://www.anchorterminal.com/compare/gliclass-vs-vela.md)\n- [Kev vs Jev](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-typesafe-jev.md)\n- [Liquid d1 vs Jev](https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev.md)\n- [OpenAI Decisions API vs Jev](https://www.anchorterminal.com/compare/openai-decisions-api-vs-typesafe-jev.md)\n- [Strands Decider 2B vs Jev](https://www.anchorterminal.com/compare/strands-decider-vs-typesafe-jev.md)\n- [Jev vs Vela 2.0](https://www.anchorterminal.com/compare/typesafe-jev-vs-vela.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "GLiClass vs Jev", "url": "" } ], "description": "Jev scores 62.1 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing and transparency \u0026 trust. Both do inference decision. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "GLiClass D 49.9", "Jev B 62.1", "scores" ], "h1": "GLiClass vs Jev", "image": "https://www.anchorterminal.com/assets/og/compare-gliclass-vs-typesafe-jev.png", "path": "/compare/gliclass-vs-typesafe-jev", "published": "2026-10-01", "section": "tools", "title": "GLiClass vs Jev for AI agents, D 49.9 vs B 62.1 | Anchor Terminal", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev" }, "tokens": { "markdown": 2250, "slim": 730 }, "version": 1 }