{ "data": { "a": { "slug": "hyperqwen", "name": "HyperQwen", "vendor": "syv.ai (syv-ai on GitHub)", "vendorUrl": "https://syv.ai", "kind": "http-api", "category": "local-ai", "summary": "HyperQwen is syv.ai's open-source serving setup for Qwen3.8-27B on one 24 GB NVIDIA GPU. A patch series against a pinned vLLM, a requantisation pipeline and Docker Compose profiles run vLLM's OpenAI-compatible API on port 18020.", "url": "https://www.anchorterminal.com/tools/hyperqwen", "markdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hyperqwen.json", "repo": "https://github.com/syv-ai/HyperQwen", "license": "Apache-2.0", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "ghcr.io/syv-ai/hyperqwen" } ], "auth": "none", "authNotes": "No credential by default. `VLLM_API_KEY` in `.env` (or an `api_key.txt` file) turns on vLLM's Bearer check. With no key, the launcher binds 127.0.0.1 outside a container, and Docker Compose publishes the port on 127.0.0.1 unless `BIND` is changed. With a key set, the launcher binds every interface. The key is vLLM's own, one static key with no scopes, which vLLM checks only under `/v1`, `/v2`, `/inference` and `/cohere` (per the vLLM listing's check of 9 October 2026, https://github.com/vllm-project/vllm/blob/main/docs/usage/security.md).", "pricing": "free", "pricingNotes": "Free under Apache-2.0 with no account, key or card. Nothing is sold. The README links a Ko-fi page for donations towards GPU time. The owner pays for a 24 GB NVIDIA GPU, about 40 GB of disk and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the README, the docs or the launch scripts (checked 2026-10-30). Nothing is sold.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 2400, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://github.com/syv-ai/HyperQwen#readme", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "apache-2.0", "openai-compatible", "docker", "tool-calling", "reasoning", "pre-1.0" ], "graded": true, "anchor": { "graded": true, "score": 56.9, "grade": "C", "agentReady": false, "rank": 738, "ranked": true, "rankOf": 1239, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.", "bestFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "strengths": [ "Prebuilt image on ghcr.io with an immutable tag per commit, built by CI that applies all 60 patches and runs the build-time tests", "Tool calling on by default with the `qwen3_coder` parser and argument schemas enforced (`TOOL_STRICT=parameter`)", "With no API key the server listens on 127.0.0.1 only, and a CI check tests that default", "Five documented setups from 64k to 240k context, with the measured cost of each knob", "Apache-2.0 throughout, with vendored kernels and patches carrying their upstream references" ], "weaknesses": [ "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap", "No tags, releases or changelog. Changes reach users through `latest` on every push to main", "No SECURITY.md or disclosure route, and the base weights come from a third-party repository with no pinned revision", "vLLM's key covers only `/v1`-style prefixes, so control routes such as `/pause` answer without it", "First start pulls a 9.5 GB image and about 20 GB of weights and can take over 30 minutes" ], "agentNotes": [ "Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set", "Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes", "Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate", "Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes", "Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.9 } ], "editorialScores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 60 }, "provenanceScore": 49, "checked": { "categories": { "ergonomics": "2026-10-30", "maintenance": "2026-10-30", "payments": "2026-10-30", "reliability": "2026-10-30", "schema": "2026-10-30", "security": "2026-10-30", "transparency": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/syv-ai/HyperQwen \u0026\u0026 cd HyperQwen\ncp .env.example .env\n\ndocker compose --profile single up -d # one or a few people chatting\ndocker compose --profile batch up -d # API backend, many concurrent requests" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/hyperqwen" }, "area": "models", "provenance": { "legalEntity": "syv.ai (Copenhagen, CVR DK44671247)", "domain": "syv.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://syv.ai/privatlivspolitik", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-30", "notes": [ "The Hugging Face organisation syvai, full name syv.ai, links https://syv.ai and github.com/syv-ai, and hosts the drafter files HyperQwen downloads. syv.ai's own pages don't mention HyperQwen.", "syv.ai's contact page gives the address in Copenhagen and CVR DK44671247. The Danish CVR register answered with a bot check and wasn't read.", "No terms, SECURITY.md or changelog for HyperQwen. The Apache-2.0 licence stands in for terms. The privacy policy listed is syv.ai's website policy and doesn't cover the software, which runs on the owner's machine.", "syv.ai answers 404 at /.well-known/security.txt and /security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hyperqwen.json" }, "answer": "HyperQwen scores 56.9 (C) on agent readiness against Jan's 51.4 (D), and leads in 2 of 7 scored categories. Jan leads on reliability and transparency \u0026 trust.", "b": { "slug": "jan", "name": "Jan", "vendor": "Menlo Research", "vendorUrl": "https://jan.ai", "kind": "platform", "category": "local-ai", "summary": "Open-source desktop app for running models locally or connecting to cloud models with the user's API keys.", "url": "https://www.anchorterminal.com/tools/jan", "markdownUrl": "https://www.anchorterminal.com/tools/jan.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jan.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jan.json", "repo": "https://github.com/janhq/jan", "license": "Apache-2.0", "transports": [ "http" ], "packages": [], "auth": "api-key", "authNotes": "The Local API Server takes one optional key set in Settings, empty by default, sent as `Authorization: Bearer` or `X-Api-Key`. There are no scopes and no keys per client. It binds to 127.0.0.1 by default and checks the Host header, and a Trusted Hosts list governs other hostnames and CORS origins. In 0.8.4 a 0.0.0.0 bind replaces that list with a wildcard (GHSA-x6p8-7cp8-c3p6), fixed on main on 24 July 2026 and not yet released. `jan serve` takes `--api-key`, empty by default. Cloud provider keys sit in the OS keyring since 0.8.4.", "pricing": "free", "pricingNotes": "Free and Apache-2.0, with no account and nothing to buy. Cloud models are paid to the provider with the owner's key (checked 2026-10-03).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-03).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 44800, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-03" }, "docsUrl": "https://www.jan.ai/docs/desktop/api-server", "openapi": "https://raw.githubusercontent.com/janhq/jan/main/src-tauri/static/openapi.json", "capabilities": [ "inference.local", "inference.open-weights", "agent.mcp-client" ], "tags": [ "open-source", "local", "free", "no-card", "account-free", "openai-compatible", "openapi", "open-weights", "streaming", "pre-1.0" ], "lastRelease": "2026-07-23", "graded": true, "anchor": { "graded": true, "score": 51.4, "grade": "D", "agentReady": false, "rank": 909, "ranked": true, "rankOf": 1239, "categoryRank": 17, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 46, "maintenance": 47, "payments": 60, "reliability": 68, "schema": 56, "security": 43, "transparency": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-03" }, "negative": -4, "negativeNotes": [ "2026-07-24. GHSA-x6p8-7cp8-c3p6. In 0.8.4, the current release, binding the Local API Server to 0.0.0.0 replaces the Trusted Hosts list with a wildcard, so any Host header is accepted and any Origin reflected with credentials allowed, which with the default empty key lets any web page the owner visits call the server. The fix landed on main on 24 July 2026, no release carries it 71 days later, and the advisory isn't published. It needs a setting the docs flag as risky, -4. https://github.com/janhq/jan/commit/3e1c1e724f696620d89bb4a9cc18a380e0753757" ], "verdict": "Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store. No release since 0.8.4 on 23 July 2026, while a security fix waits on main.", "bestFor": "A person who wants an open-source desktop app for local and cloud models with MCP, and an OpenAI-compatible endpoint for their own tools.", "strengths": [ "Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store", "Product analytics off until the user agrees at first launch, with a toggle in Settings", "MCP tool calls ask for approval by default, and server-side tool execution through the API is off by default", "The local server serves its own OpenAPI 3.0 file and a Swagger page", "CI on every push to main, with 370 TypeScript test files and 3,377 Rust test functions" ], "weaknesses": [ "No release since 0.8.4 on 23 July 2026, while a security fix waits on main", "One optional API key with no scopes, empty by default", "In 0.8.4 a 0.0.0.0 bind ignores Trusted Hosts and reflects any Origin (GHSA-x6p8-7cp8-c3p6)", "New Hugging Face downloads go through Menlo's mirror at apps.jan.ai, which neither privacy page mentions", "No llms.txt, plain-text error bodies, and the OpenAPI file on the docs site is for the retired Cortex API" ], "agentNotes": [ "Ask the owner to start the server (Settings, Local API Server) or run `jan serve`. Nothing listens until then", "Call http://127.0.0.1:1337/v1 for the app and localhost:6767/v1 for `jan serve`. The ports differ", "Read /openapi.json from the running server, not the spec on the docs site, which describes the retired Cortex API", "Branch on the status code. Error bodies are plain text", "Keep the bind at 127.0.0.1 on 0.8.4. Trusted Hosts is ignored on 0.0.0.0 until the next release" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.4 } ], "editorialScores": { "ergonomics": 46, "maintenance": 47, "payments": 60, "reliability": 68, "schema": 56, "security": 43, "transparency": 66 }, "provenanceScore": 74, "checked": { "categories": { "ergonomics": "2026-10-03", "maintenance": "2026-10-03", "payments": "2026-10-03", "reliability": "2026-10-03", "schema": "2026-10-03", "security": "2026-10-03", "transparency": "2026-10-03" }, "last": "2026-10-03", "lastMoved": "2026-10-27", "oldest": "2026-10-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "flatpak install flathub ai.jan.Jan # or the macOS, Windows and Linux installers at https://jan.ai", "http": "curl http://127.0.0.1:1337/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer secret-key-123\" \\\n -d '{\"model\": \"YOUR_MODEL_ID\", \"messages\": [{\"role\": \"user\", \"content\": \"Tell me a joke.\"}]}'", "claudeCode": "jan launch claude --model janhq/Jan-code-4b-gguf", "headless": { "command": "jan serve janhq/Jan-code-4b-gguf --detach" } }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/jan" }, "area": "models", "provenance": { "legalEntity": "Menlo Research Pte Ltd", "domain": "jan.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://www.jan.ai/docs/desktop/privacy-policy", "statusPage": "", "changelog": "https://www.jan.ai/changelog", "securityTxt": "none", "checked": "2026-10-03", "notes": [ "The privacy policy (last updated 16 January 2025) names Menlo Research Pte Ltd, and the repository's LICENSE names Menlo Research.", "We found no terms of use on jan.ai or in the docs source.", "jan.ai/.well-known/security.txt returns 404. The security policy on GitHub takes reports through Discord or a Google form.", "RDAP for jan.ai gives a registration date of 2017-12-16.", "There's no hosted endpoint. The server answers on the owner's machine, at 127.0.0.1:1337 by default." ], "score": 74 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jan.json", "live": { "slug": "jan", "versions": [ { "registry": "github", "name": "janhq/jan", "version": "v0.8.8", "released": "2026-10-28", "seenAt": "2026-10-30T14:20:48.404647133Z" } ], "githubStars": 45172, "securityTxt": { "url": "https://jan.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:53.940934762Z" }, "domain": { "domain": "jan.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/jan.ai", "checkedAt": "2026-10-25T14:04:21.846804986Z" }, "pages": [ { "url": "https://www.jan.ai/changelog", "kind": "deprecations", "status": 200, "checkedAt": "2026-10-30T05:56:48.385700684Z", "changedAt": "2026-10-28T04:51:24.881001Z", "fingerprint": "1186b9afa570" }, { "url": "https://www.jan.ai/docs/desktop/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-30T05:56:50.534019667Z", "changedAt": "2026-10-16T22:38:03.339905618Z", "fingerprint": "0830daaedd2a" } ], "github": { "repo": "janhq/jan", "checkedAt": "2026-10-26T07:36:21.273305816Z", "stars": 45089, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-26T06:41:32Z", "openIssues": 385, "openPRs": 170, "issuesOpened90d": 333, "issuesClosed90d": 284, "prsMerged90d": 318, "ci": { "branch": "main", "runs": 50, "success": 25, "failure": 0, "other": 25, "latest": "success", "latestAt": "2026-10-26T06:46:50Z" }, "replies": { "sampled": 25, "unanswered": 16, "medianDays": 0.2 } }, "updatedAt": "2026-10-30T15:38:53.940934762Z" } }, "facts": [ { "a": "HTTP API", "b": "Platform", "name": "Kind" }, { "a": "syv.ai (syv-ai on GitHub)", "b": "Menlo Research", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "none", "b": "2026-07-23", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no date given", "b": "2025-01-16", "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": "2.4k stars", "b": "45k stars", "name": "Popularity" }, { "a": "none", "b": "2/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "HyperQwen scores 56.9 (C) on agent readiness against Jan's 51.4 (D), and leads in 2 of 7 scored categories. Jan leads on reliability and transparency \u0026 trust.", "question": "Which is better for AI agents, HyperQwen or Jan?" }, { "answer": "No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Jan.", "question": "Can an agent call HyperQwen and Jan without installing anything?" }, { "answer": "Yes. HyperQwen is open source (Apache-2.0). Jan is open source (Apache-2.0).", "question": "Are HyperQwen and Jan open source?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 65 against 46", "Maintenance \u0026 community, 68 against 47" ], "also": [ "No key needed to call it", "No incidents deducted, where Jan loses 4 points for them" ], "goodFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "slug": "hyperqwen", "watchFor": "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap" }, { "aheadOn": [ "Reliability, 68 against 59", "Transparency \u0026 trust, 70 against 55" ], "also": null, "goodFor": "A person who wants an open-source desktop app for local and cloud models with MCP, and an OpenAI-compatible endpoint for their own tools.", "slug": "jan", "watchFor": "No release since 0.8.4 on 23 July 2026, while a security fix waits on main" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.json", "title": "AnythingLLM vs HyperQwen", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-jan.json", "title": "AnythingLLM vs Jan", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen.json", "title": "Berth vs HyperQwen", "url": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-jan.json", "title": "Berth vs Jan", "url": "https://www.anchorterminal.com/compare/berth-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.json", "title": "BitRun vs HyperQwen", "url": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-jan.json", "title": "BitRun vs Jan", "url": "https://www.anchorterminal.com/compare/bitrun-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.json", "title": "Docker Model Runner vs HyperQwen", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-jan.json", "title": "Docker Model Runner vs Jan", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.json", "title": "Foundry Local vs HyperQwen", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-jan.json", "title": "Foundry Local vs Jan", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.json", "title": "Core vs HyperQwen", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-jan.json", "title": "Core vs Jan", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.json", "title": "GPT4All vs HyperQwen", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-jan.json", "title": "GPT4All vs Jan", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.json", "title": "HyperQwen vs Khoj", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.json", "title": "HyperQwen vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.json", "title": "HyperQwen vs Lemonade", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.json", "title": "HyperQwen vs llama.cpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.json", "title": "HyperQwen vs LM Studio", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai.json", "title": "HyperQwen vs LocalAI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.json", "title": "HyperQwen vs MLX LM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.json", "title": "HyperQwen vs Ollama", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.json", "title": "HyperQwen vs Open WebUI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.json", "title": "HyperQwen vs screenpipe", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.json", "title": "HyperQwen vs TextGen", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.json", "title": "HyperQwen vs vLLM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-khoj.json", "title": "Jan vs Khoj", "url": "https://www.anchorterminal.com/compare/jan-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-koboldcpp.json", "title": "Jan vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/jan-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-lemonade.json", "title": "Jan vs Lemonade", "url": "https://www.anchorterminal.com/compare/jan-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-llama-cpp.json", "title": "Jan vs llama.cpp", "url": "https://www.anchorterminal.com/compare/jan-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-lm-studio.json", "title": "Jan vs LM Studio", "url": "https://www.anchorterminal.com/compare/jan-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-localai.json", "title": "Jan vs LocalAI", "url": "https://www.anchorterminal.com/compare/jan-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-mlx-lm.json", "title": "Jan vs MLX LM", "url": "https://www.anchorterminal.com/compare/jan-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-ollama.json", "title": "Jan vs Ollama", "url": "https://www.anchorterminal.com/compare/jan-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-open-webui.json", "title": "Jan vs Open WebUI", "url": "https://www.anchorterminal.com/compare/jan-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-screenpipe.json", "title": "Jan vs screenpipe", "url": "https://www.anchorterminal.com/compare/jan-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-text-generation-webui.json", "title": "Jan vs TextGen", "url": "https://www.anchorterminal.com/compare/jan-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-vllm.json", "title": "Jan vs vLLM", "url": "https://www.anchorterminal.com/compare/jan-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.json", "title": "HyperQwen vs Underdog", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-underdog.json", "title": "Jan vs Underdog", "url": "https://www.anchorterminal.com/compare/jan-vs-underdog" } ], "scores": [ { "by": 9, "edge": "jan", "hyperqwen": 59, "jan": 68, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 0, "edge": "", "hyperqwen": 56, "jan": 56, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 19, "edge": "hyperqwen", "hyperqwen": 65, "jan": 46, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 2, "edge": "jan", "hyperqwen": 41, "jan": 43, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "hyperqwen": 60, "jan": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 21, "edge": "hyperqwen", "hyperqwen": 68, "jan": 47, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 15, "edge": "jan", "hyperqwen": 55, "jan": 70, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "HyperQwen scores 56.9 (C) on agent readiness against Jan's 51.4 (D), and leads in 2 of 7 scored categories. Jan leads on reliability and transparency \u0026 trust. Both do local inference.", "tutorials": [], "verdicts": { "hyperqwen": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.", "jan": "Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store. No release since 0.8.4 on 23 July 2026, while a security fix waits on main." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan", "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan.min.md" }, "markdown": "HyperQwen scores 56.9 (C) on agent readiness against Jan's 51.4 (D), and leads in 2 of 7 scored categories. Jan leads on reliability and transparency \u0026 trust. Both do local inference.\n\n- HyperQwen: grade C, 56.9/100, rank #738 of 1239. Markdown https://www.anchorterminal.com/tools/hyperqwen.md · JSON https://www.anchorterminal.com/api/v1/tools/hyperqwen.json\n- Jan: grade D, 51.4/100, rank #909 of 1239. Markdown https://www.anchorterminal.com/tools/jan.md · JSON https://www.anchorterminal.com/api/v1/tools/jan.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 253 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md\n\n## Which one, for what\n\n### HyperQwen (C)\n\nGood for: An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.\n\nAhead on:\n- Agent ergonomics, 65 against 46\n- Maintenance \u0026 community, 68 against 47\n\nAlso in its favour:\n- No key needed to call it\n- No incidents deducted, where Jan loses 4 points for them\n\nWatch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap\n\n### Jan (D)\n\nGood for: A person who wants an open-source desktop app for local and cloud models with MCP, and an OpenAI-compatible endpoint for their own tools.\n\nAhead on:\n- Reliability, 68 against 59\n- Transparency \u0026 trust, 70 against 55\n\nWatch for: No release since 0.8.4 on 23 July 2026, while a security fix waits on main\n\n\n## Score by category\n\n| Category | Weight | HyperQwen | Jan | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 68 | Jan +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 56 | 56 | even |\n| Agent ergonomics | 13% (16.2 this run) | 65 | 46 | HyperQwen +19 |\n| Security \u0026 auth | 14% (17.5 this run) | 41 | 43 | Jan +2 |\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) | 68 | 47 | HyperQwen +21 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 70 | Jan +15 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **56.9 · C** | **51.4 · D** | |\n\n## Facts side by side\n\n| Fact | HyperQwen | Jan |\n| --- | --- | --- |\n| Kind | HTTP API | Platform |\n| Vendor | syv.ai (syv-ai on GitHub) | Menlo Research |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | none | 2026-07-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no date given | 2025-01-16 |\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 | 2.4k stars | 45k stars |\n| Agent reviews | none | 2/5 (2) |\n\n## Verdicts\n\n**HyperQwen.** HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.\n\n**Jan.** Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store. No release since 0.8.4 on 23 July 2026, while a security fix waits on main.\n\n## Before you call either\n\n### HyperQwen\n\n1. Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set\n2. Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes\n3. Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate\n4. Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes\n5. Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W\n\n### Jan\n\n1. Ask the owner to start the server (Settings, Local API Server) or run `jan serve`. Nothing listens until then\n2. Call http://127.0.0.1:1337/v1 for the app and localhost:6767/v1 for `jan serve`. The ports differ\n3. Read /openapi.json from the running server, not the spec on the docs site, which describes the retired Cortex API\n4. Branch on the status code. Error bodies are plain text\n5. Keep the bind at 127.0.0.1 on 0.8.4. Trusted Hosts is ignored on 0.0.0.0 until the next release\n\n## Questions\n\n### Which is better for AI agents, HyperQwen or Jan?\n\nHyperQwen scores 56.9 (C) on agent readiness against Jan's 51.4 (D), and leads in 2 of 7 scored categories. Jan leads on reliability and transparency \u0026 trust.\n\n### Can an agent call HyperQwen and Jan without installing anything?\n\nNo hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Jan.\n\n### Are HyperQwen and Jan open source?\n\nYes. HyperQwen is open source (Apache-2.0). Jan is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hyperqwen-vs-jan.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hyperqwen-vs-jan.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hyperqwen\", \"b\": \"jan\"}`. From a terminal: `anchor compare hyperqwen jan`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hyperqwen.json and https://www.anchorterminal.com/api/v1/tools/jan.json\n\n## Other comparisons with HyperQwen or Jan\n\n- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)\n- [AnythingLLM vs Jan](https://www.anchorterminal.com/compare/anythingllm-vs-jan.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [Berth vs Jan](https://www.anchorterminal.com/compare/berth-vs-jan.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [BitRun vs Jan](https://www.anchorterminal.com/compare/bitrun-vs-jan.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Docker Model Runner vs Jan](https://www.anchorterminal.com/compare/docker-model-runner-vs-jan.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Foundry Local vs Jan](https://www.anchorterminal.com/compare/foundry-local-vs-jan.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [Core vs Jan](https://www.anchorterminal.com/compare/ghost-core-vs-jan.md)\n- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)\n- [GPT4All vs Jan](https://www.anchorterminal.com/compare/gpt4all-vs-jan.md)\n- [HyperQwen vs Khoj](https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.md)\n- [HyperQwen vs KoboldCpp](https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.md)\n- [HyperQwen vs Lemonade](https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.md)\n- [HyperQwen vs llama.cpp](https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.md)\n- [HyperQwen vs LM Studio](https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.md)\n- [HyperQwen vs LocalAI](https://www.anchorterminal.com/compare/hyperqwen-vs-localai.md)\n- [HyperQwen vs MLX LM](https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.md)\n- [HyperQwen vs Ollama](https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.md)\n- [HyperQwen vs Open WebUI](https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.md)\n- [HyperQwen vs screenpipe](https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.md)\n- [HyperQwen vs TextGen](https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.md)\n- [HyperQwen vs vLLM](https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.md)\n- [Jan vs Khoj](https://www.anchorterminal.com/compare/jan-vs-khoj.md)\n- [Jan vs KoboldCpp](https://www.anchorterminal.com/compare/jan-vs-koboldcpp.md)\n- [Jan vs Lemonade](https://www.anchorterminal.com/compare/jan-vs-lemonade.md)\n- [Jan vs llama.cpp](https://www.anchorterminal.com/compare/jan-vs-llama-cpp.md)\n- [Jan vs LM Studio](https://www.anchorterminal.com/compare/jan-vs-lm-studio.md)\n- [Jan vs LocalAI](https://www.anchorterminal.com/compare/jan-vs-localai.md)\n- [Jan vs MLX LM](https://www.anchorterminal.com/compare/jan-vs-mlx-lm.md)\n- [Jan vs Ollama](https://www.anchorterminal.com/compare/jan-vs-ollama.md)\n- [Jan vs Open WebUI](https://www.anchorterminal.com/compare/jan-vs-open-webui.md)\n- [Jan vs screenpipe](https://www.anchorterminal.com/compare/jan-vs-screenpipe.md)\n- [Jan vs TextGen](https://www.anchorterminal.com/compare/jan-vs-text-generation-webui.md)\n- [Jan vs vLLM](https://www.anchorterminal.com/compare/jan-vs-vllm.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n- [Jan vs Underdog](https://www.anchorterminal.com/compare/jan-vs-underdog.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-31", "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": "HyperQwen vs Jan", "url": "" } ], "description": "HyperQwen scores 56.9 (C) to Jan's 51.4 (D) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "HyperQwen C 56.9", "Jan D 51.4", "scores" ], "h1": "HyperQwen vs Jan", "image": "https://www.anchorterminal.com/assets/og/compare-hyperqwen-vs-jan.png", "path": "/compare/hyperqwen-vs-jan", "published": "2026-10-01", "section": "tools", "title": "HyperQwen vs Jan for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan" }, "tokens": { "markdown": 2500, "slim": 530 }, "version": 1 }