{ "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": "Strands Decider 2B scores 61.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on transparency \u0026 trust.", "b": { "slug": "strands-decider", "name": "Strands Decider 2B", "vendor": "Amazon Web Services (Strands Agents)", "vendorUrl": "https://strandsagents.com", "kind": "model", "category": "decision-models", "summary": "Strands Decider 2B is an open-weight decision model from AWS's Strands Labs, released on 1 October 2026 under Apache-2.0. It answers typed yes or no, choice and score questions with probabilities, and runs locally from a Python package.", "url": "https://www.anchorterminal.com/tools/strands-decider", "markdownUrl": "https://www.anchorterminal.com/tools/strands-decider.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/strands-decider.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/strands-decider.json", "repo": "https://github.com/strands-labs/strands-decider", "license": "Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "strands-decider" } ], "auth": "none", "authNotes": "No account. `strands-decider serve` binds to 127.0.0.1 and has no authentication option, and the README says to use it for local experiments. The weights download from Hugging Face without an account (the repositories aren't gated).", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy. You pay for your own hardware. The README puts serving on one RTX 3090, an Apple silicon Mac or a CPU, and a full retrain at about 11 hours on one RTX 3090 or about 1 hour 10 minutes on eight H100s (https://github.com/strands-labs/strands-decider). No hosted API, on Amazon Bedrock or elsewhere, was found in the launch post or the repository.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Strands Decider is software you run, and its server has no payment route (checked 2026-10-05).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-05" }, "docsUrl": "https://github.com/strands-labs/strands-decider#readme", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "pre-1.0" ], "lastRelease": "2026-10-05", "graded": true, "anchor": { "graded": true, "score": 61.3, "grade": "C", "agentReady": false, "rank": 427, "ranked": true, "rankOf": 842, "categoryRank": 8, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 69, "maintenance": 79, "payments": 60, "reliability": 50, "schema": 76, "security": 49, "transparency": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-05" }, "negative": 0, "verdict": "A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication.", "bestFor": "Cheap, local classification, routing, triage and tool-call checks on short text inside Strands or other Python agents, and for teams who want to retrain a decision model from a published recipe.", "strengths": [ "Apache-2.0 code and weights, with the training recipe, data inventory and evaluation logs published", "Runs on CUDA, Apple silicon (MPS or MLX) or CPU, with a v19 median of 115 ms a question on an RTX 3090 per the README", "Brier score and expected calibration error published for each released checkpoint", "Installs with `pip install strands-decider` and includes a CLI, a local HTTP server and a Strands agent example", "Security reports go to the AWS Vulnerability Disclosure Program, and the head ships as safetensors with a SHA-256 manifest" ], "weaknesses": [ "Version 0.1.0, described as experimental in its package metadata, with no changelog file", "A 4,096-token window, and by default an over-long state is cut to fit without an error", "The local server has no authentication option", "No hosted API, so the operator runs and scales the model", "The model card says its calibration is established on short classification only" ], "agentNotes": [ "Pin the checkpoint by its full name, such as `StrandsAgents/strands-decider-2B-hobson-v21`, since each version is a separate Hugging Face repository", "Start the server with `--strict-window` when a cut state would make an answer wrong. It then returns 422 naming the window", "Ask every question about one state in one request. The state is read once and each question adds only its own tokens", "Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option", "Measure thresholds on your own traffic before acting automatically. The card says confidence bands hold for short classification only" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 1, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.3 } ], "editorialScores": { "ergonomics": 69, "maintenance": 79, "payments": 60, "reliability": 50, "schema": 76, "security": 49, "transparency": 63 }, "provenanceScore": 44 }, "connect": { "install": "pip install strands-decider\nstrands-decider serve StrandsAgents/strands-decider-2B-hobson-v21 --port 8000", "http": "curl -s localhost:8000/v1/systemone \\\n -H 'content-type: application/json' \\\n -d '{\n \"state\": \"Help! My payouts have been failing for 3 days!\",\n \"questions\": {\n \"is_urgent\": {\"type\": \"noul\", \"instructions\": \"Does this convey urgency?\"}\n }\n }'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/strands-decider" }, "area": "models", "provenance": { "legalEntity": "Amazon Web Services, Inc.", "domain": "strandsagents.com", "domainRegistered": "2025-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-05", "notes": [ "The repository's SECURITY.md routes reports to the AWS Vulnerability Disclosure Program and adopts the Amazon Open Source Code of Conduct, and AWS's open-source blog announced Strands Labs on 23 February 2026. The legal entity is inferred from those pages. The licence names no copyright holder.", "strandsagents.com was registered on 15 May 2025 per RDAP. Its /.well-known/security.txt returned 404.", "Software you run, so there's no hosted endpoint, terms or privacy policy to check. The Apache-2.0 licence stands in for terms.", "No changelog file or GitHub release tags in the repository. Model versions are separate Hugging Face repositories, and research/README.md in the repository lists each training run." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/strands-decider.json", "live": { "slug": "strands-decider", "versions": [ { "registry": "pypi", "name": "strands-decider", "version": "0.1.0", "released": "2026-10-01", "seenAt": "2026-10-08T16:30:33.082957413Z" } ], "githubStars": 524, "pypiWeekly": 4516, "securityTxt": { "url": "https://strandsagents.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:09.928660277Z" }, "updatedAt": "2026-10-08T16:30:33.271392417Z" } }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Knowledgator", "b": "Amazon Web Services (Strands Agents)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (library and the model weights we checked)", "b": "Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-21", "b": "2026-10-05", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "555 stars, 13k PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "none", "b": "2/5 (1)", "name": "Agent reviews" } ], "faq": [ { "answer": "Strands Decider 2B scores 61.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on transparency \u0026 trust.", "question": "Which is better for AI agents, GLiClass or Strands Decider 2B?" }, { "answer": "Neither needs a key.", "question": "Do GLiClass and Strands Decider 2B need an API key?" }, { "answer": "No hosted endpoint is listed for GLiClass. No hosted endpoint is listed for Strands Decider 2B.", "question": "Can an agent call GLiClass and Strands Decider 2B without installing anything?" }, { "answer": "Yes. GLiClass is open source (Apache-2.0 (library and the model weights we checked)). Strands Decider 2B is open source (Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base).", "question": "Are GLiClass and Strands Decider 2B open source?" } ], "goodFor": [ { "aheadOn": [ "Transparency \u0026 trust, 64 against 54" ], "also": null, "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, 50 against 43", "Schema \u0026 documentation, 76 against 49", "Agent ergonomics, 69 against 60", "Security \u0026 auth, 49 against 38", "Maintenance \u0026 community, 79 against 44" ], "also": null, "goodFor": "Cheap, local classification, routing, triage and tool-call checks on short text inside Strands or other Python agents, and for teams who want to retrain a decision model from a published recipe.", "slug": "strands-decider", "watchFor": "Version 0.1.0, described as experimental in its package metadata, with no changelog file" } ], "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-strands-decider.json", "title": "Celeris-1 Decision vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-strands-decider" }, { "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-strands-decider.json", "title": "Clef vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-strands-decider" }, { "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-strands-decider.json", "title": "Laya vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider" }, { "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-strands-decider.json", "title": "Decider vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/decider-vs-strands-decider" }, { "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-typesafe-jev.json", "title": "GLiClass vs Jev", "url": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev" }, { "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-strands-decider.json", "title": "Kev vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider.json", "title": "Liquid d1 vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-strands-decider.json", "title": "OpenAI Decisions API vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-strands-decider" }, { "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/strands-decider-vs-vela.json", "title": "Strands Decider 2B vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/strands-decider-vs-vela" } ], "scores": [ { "by": 7, "edge": "strands-decider", "gliclass": 43, "key": "reliability", "name": "Reliability", "strands-decider": 50, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 27, "edge": "strands-decider", "gliclass": 49, "key": "schema", "name": "Schema \u0026 documentation", "strands-decider": 76, "weight": 13 }, { "by": 9, "edge": "strands-decider", "gliclass": 60, "key": "ergonomics", "name": "Agent ergonomics", "strands-decider": 69, "weight": 13 }, { "by": 11, "edge": "strands-decider", "gliclass": 38, "key": "security", "name": "Security \u0026 auth", "strands-decider": 49, "weight": 14 }, { "by": 0, "edge": "", "gliclass": 60, "key": "payments", "name": "Payments \u0026 pricing", "strands-decider": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 35, "edge": "strands-decider", "gliclass": 44, "key": "maintenance", "name": "Maintenance \u0026 community", "strands-decider": 79, "weight": 7 }, { "by": 10, "edge": "gliclass", "gliclass": 64, "key": "transparency", "name": "Transparency \u0026 trust", "strands-decider": 54, "weight": 7 } ], "summary": "Strands Decider 2B scores 61.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on 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.", "strands-decider": "A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider", "json": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.md", "slim": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.min.md" }, "markdown": "Strands Decider 2B scores 61.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on 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- Strands Decider 2B: grade C, 61.3/100, rank #427 of 842. Markdown https://www.anchorterminal.com/tools/strands-decider.md · JSON https://www.anchorterminal.com/api/v1/tools/strands-decider.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- Transparency \u0026 trust, 64 against 54\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### Strands Decider 2B (C)\n\nGood for: Cheap, local classification, routing, triage and tool-call checks on short text inside Strands or other Python agents, and for teams who want to retrain a decision model from a published recipe.\n\nAhead on:\n- Reliability, 50 against 43\n- Schema \u0026 documentation, 76 against 49\n- Agent ergonomics, 69 against 60\n- Security \u0026 auth, 49 against 38\n- Maintenance \u0026 community, 79 against 44\n\nWatch for: Version 0.1.0, described as experimental in its package metadata, with no changelog file\n\n\n## Score by category\n\n| Category | Weight | GLiClass | Strands Decider 2B | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 43 | 50 | Strands Decider 2B +7 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 49 | 76 | Strands Decider 2B +27 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 69 | Strands Decider 2B +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 38 | 49 | Strands Decider 2B +11 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 44 | 79 | Strands Decider 2B +35 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 64 | 54 | GLiClass +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.9 · D** | **61.3 · C** | |\n\n## Facts side by side\n\n| Fact | GLiClass | Strands Decider 2B |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Knowledgator | Amazon Web Services (Strands Agents) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (library and the model weights we checked) | Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-21 | 2026-10-05 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 555 stars, 13k PyPI/wk | none |\n| Agent reviews | none | 2/5 (1) |\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**Strands Decider 2B.** A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication.\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### Strands Decider 2B\n\n1. Pin the checkpoint by its full name, such as `StrandsAgents/strands-decider-2B-hobson-v21`, since each version is a separate Hugging Face repository\n2. Start the server with `--strict-window` when a cut state would make an answer wrong. It then returns 422 naming the window\n3. Ask every question about one state in one request. The state is read once and each question adds only its own tokens\n4. Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option\n5. Measure thresholds on your own traffic before acting automatically. The card says confidence bands hold for short classification only\n\n## Questions\n\n### Which is better for AI agents, GLiClass or Strands Decider 2B?\n\nStrands Decider 2B scores 61.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on transparency \u0026 trust.\n\n### Do GLiClass and Strands Decider 2B need an API key?\n\nNeither needs a key.\n\n### Can an agent call GLiClass and Strands Decider 2B without installing anything?\n\nNo hosted endpoint is listed for GLiClass. No hosted endpoint is listed for Strands Decider 2B.\n\n### Are GLiClass and Strands Decider 2B open source?\n\nYes. GLiClass is open source (Apache-2.0 (library and the model weights we checked)). Strands Decider 2B is open source (Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gliclass\", \"b\": \"strands-decider\"}`. From a terminal: `anchor compare gliclass strands-decider`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gliclass.json and https://www.anchorterminal.com/api/v1/tools/strands-decider.json\n\n## Other comparisons with GLiClass or Strands Decider 2B\n\n- [Celeris-1 Decision vs GLiClass](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.md)\n- [Celeris-1 Decision vs Strands Decider 2B](https://www.anchorterminal.com/compare/celeris-1-decision-vs-strands-decider.md)\n- [Clef vs GLiClass](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.md)\n- [Clef vs Strands Decider 2B](https://www.anchorterminal.com/compare/cloudflare-clef-vs-strands-decider.md)\n- [Laya vs GLiClass](https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.md)\n- [Laya vs Strands Decider 2B](https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider.md)\n- [Decider vs GLiClass](https://www.anchorterminal.com/compare/decider-vs-gliclass.md)\n- [Decider vs Strands Decider 2B](https://www.anchorterminal.com/compare/decider-vs-strands-decider.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 Jev](https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.md)\n- [GLiClass vs Vela 2.0](https://www.anchorterminal.com/compare/gliclass-vs-vela.md)\n- [Kev vs Strands Decider 2B](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-strands-decider.md)\n- [Liquid d1 vs Strands Decider 2B](https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider.md)\n- [OpenAI Decisions API vs Strands Decider 2B](https://www.anchorterminal.com/compare/openai-decisions-api-vs-strands-decider.md)\n- [Strands Decider 2B vs Jev](https://www.anchorterminal.com/compare/strands-decider-vs-typesafe-jev.md)\n- [Strands Decider 2B vs Vela 2.0](https://www.anchorterminal.com/compare/strands-decider-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 Strands Decider 2B", "url": "" } ], "description": "Strands Decider 2B scores 61.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. GLiClass leads on transparency \u0026 trust. Both do inference decision. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "GLiClass D 49.9", "Strands Decider 2B C 61.3", "scores" ], "h1": "GLiClass vs Strands Decider 2B", "image": "https://www.anchorterminal.com/assets/og/compare-gliclass-vs-strands-decider.png", "path": "/compare/gliclass-vs-strands-decider", "published": "2026-10-01", "section": "tools", "title": "GLiClass vs Strands Decider 2B for AI agents, D 49.9 vs C 61.3", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider" }, "tokens": { "markdown": 2350, "slim": 580 }, "version": 1 }