{ "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 and Ollama score within a point of each other on agent readiness, 56.9 (C) and 56.6 (C). Ollama leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.", "b": { "slug": "ollama", "name": "Ollama", "vendor": "Ollama Inc.", "vendorUrl": "https://ollama.com", "kind": "http-api", "category": "local-ai", "summary": "Open-source model runner for macOS, Windows and Linux, with a local API and a library of downloadable models.", "url": "https://www.anchorterminal.com/tools/ollama", "markdownUrl": "https://www.anchorterminal.com/tools/ollama.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ollama.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ollama.json", "repo": "https://github.com/ollama/ollama", "license": "MIT (server, CLI and desktop app). Ollama Cloud is a closed service under the ollama.com terms, and each model carries its own licence", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "docker.io/ollama/ollama" }, { "registry": "pypi", "name": "ollama" }, { "registry": "npm", "name": "ollama" } ], "auth": "none", "authNotes": "The local API at http://localhost:11434 takes no credential. It binds 127.0.0.1, answers a foreign Host header with 403 while bound to loopback, and allows cross-origin calls from 127.0.0.1 and 0.0.0.0 unless `OLLAMA_ORIGINS` adds more. Anything that reaches the port can generate, pull, push, create, copy and delete models. Cloud models through the local server need `ollama signin`, which signs requests with the install's own key. Direct calls to https://ollama.com/api and /v1 need a Bearer API key from ollama.com/settings/keys, which doesn't expire and has no scopes, and is revoked from the same page (https://github.com/ollama/ollama/blob/main/docs/api/authentication.mdx).", "pricing": "freemium", "pricingNotes": "The server, CLI and desktop app are free under MIT with no account. Ollama Cloud has five plans on ollama.com/pricing. Free ($0, starter usage credits, starter models, 1 concurrent request), Pro ($20 a month or $200 a year, $60 of usage credits a month, 3 concurrent requests), Max ($100 a month, $300 of credits, 10 concurrent requests), Team ($500 a month, $1,000 of shared credits, unlimited users) and Enterprise (custom). Usage is priced per model by the token, and the page doesn't say whether the Free plan needs a card (checked 2026-10-03).", "priceSummary": "$20 / mo", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs, the pricing page or the source (checked 2026-10-03).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 181200, "npmWeekly": 871543, "pypiWeekly": null, "asOf": "2026-10-03" }, "docsUrl": "https://docs.ollama.com", "llmsTxt": "https://docs.ollama.com/llms.txt", "openapi": "https://raw.githubusercontent.com/ollama/ollama/main/docs/openapi.yaml", "capabilities": [ "inference.local", "inference.open-weights", "inference.llm", "embed.text", "inference.decision", "web.search", "web.fetch" ], "tags": [ "open-source", "local", "self-hosted", "hosted", "freemium", "no-card", "openai-compatible", "openapi", "llms-txt", "docker", "go", "python", "typescript", "pre-1.0", "no-auth" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 56.6, "grade": "C", "agentReady": false, "rank": 748, "ranked": true, "rankOf": 1239, "categoryRank": 12, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 81, "payments": 60, "reliability": 53, "schema": 79, "security": 28, "transparency": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-03" }, "negative": -4, "negativeNotes": [ "2026-04-29. CERT Polska published CVE-2026-42248 and CVE-2026-42249 (9.8 each). The Windows app accepted downloaded updates without a signature check and took the file name from the server's response, and it installs updates silently, so whoever could answer the update request could run code on the machine. CERT Polska tested 0.12.10 to 0.17.5, and the Windows check stayed a stub returning success until v0.23.3 on 12 May 2026, whose notes list the fix only as `app: harden update flows`. CERT Polska says the maintainers didn't respond with details or the vulnerable range, and Ollama published no advisory. Fixed, but not disclosed by the vendor, -4. https://cert.pl/en/posts/2026/04/CVE-2026-42248/; https://github.com/ollama/ollama/releases/tag/v0.23.3" ], "verdict": "An OpenAPI 3.1 file for the 15 native operations and llms.txt with 68 links to Markdown pages. No credential on the local API, and any caller that reaches it can pull, push, create and delete models.", "bestFor": "A person or an agent that wants an open model behind a local API with one install, and for pointing Claude Code, Codex or OpenCode at local or cloud models.", "strengths": [ "An OpenAPI 3.1 file for the 15 native operations and llms.txt with 68 links to Markdown pages", "Native, OpenAI-compatible and Anthropic-compatible routes on one local port, with `ollama launch` for Claude Code, Codex and OpenCode", "28 releases in the 90 days to 3 October 2026, and official Python and JavaScript libraries released on 28 September", "Local prompts stay on the machine, and `OLLAMA_NO_CLOUD=1` turns off cloud models and web search", "Binds 127.0.0.1 by default and refuses foreign Host headers while bound to loopback" ], "weaknesses": [ "No credential on the local API, and any caller that reaches it can pull, push, create and delete models", "No GitHub security advisory, against 12 CVEs on NVD since October 2025", "The Windows updater installed unsigned files until v0.23.3 on 12 May 2026, fixed under a release note that didn't mention security", "The desktop app checks ollama.com every hour with a signed request, even with automatic updates off, and no documented way to stop it", "A default context of 4k tokens below 24 GiB of VRAM, where the docs say agents need 64,000" ], "agentNotes": [ "Send `\"stream\": false` for one JSON body. The native routes stream NDJSON by default", "Set `OLLAMA_CONTEXT_LENGTH=64000` or `options.num_ctx` before agent work. The default is 4k below 24 GiB of VRAM", "Back off on a 503. It means the queue (512 by default) is full", "Put an authenticating proxy in front before binding past 127.0.0.1. The server checks no credential", "Expect model names with a `cloud` tag to run on Ollama's servers. They need `ollama signin` and fail with `OLLAMA_NO_CLOUD=1`" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.6 } ], "editorialScores": { "ergonomics": 75, "maintenance": 81, "payments": 60, "reliability": 53, "schema": 79, "security": 28, "transparency": 66 }, "provenanceScore": 59, "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": "curl -fsSL https://ollama.com/install.sh | sh # macOS and Linux; Windows: irm https://ollama.com/install.ps1 | iex\nollama pull gemma4:e2b", "http": "curl http://localhost:11434/api/chat \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"gemma4:e2b\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Say hello in one sentence.\"}],\n \"stream\": false\n }'", "claudeCode": "ollama launch claude # or: ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_API_KEY=\"\" ANTHROPIC_BASE_URL=http://localhost:11434 claude --model qwen3.5" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/ollama" }, "area": "models", "unitPrices": [ { "item": "Ollama Cloud Pro", "unit": "month", "usd": 20, "note": "$60 of usage credits a month, 3 concurrent requests. $200 a year" }, { "item": "Ollama Cloud Max", "unit": "month", "usd": 100, "note": "$300 of usage credits a month, 10 concurrent requests" }, { "item": "Ollama Cloud Team", "unit": "month", "usd": 500, "note": "$1,000 of shared usage credits a month, unlimited users, 10 concurrent requests" } ], "provenance": { "legalEntity": "Ollama Inc.", "domain": "ollama.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://ollama.com/terms", "privacy": "https://ollama.com/privacy", "statusPage": "", "changelog": "https://github.com/ollama/ollama/releases", "securityTxt": "none", "checked": "2026-10-03", "notes": [ "The terms (last updated May 2026) name Ollama Inc., under California law with arbitration in San Francisco. The privacy policy was last updated in March 2026.", "ollama.com/.well-known/security.txt returns 404. SECURITY.md sends reports to hello@ollama.com.", "status.ollama.com doesn't resolve, and we found no other status page for Ollama Cloud.", "The API an agent calls runs on the owner's machine, so there's no shared endpoint to check. Ollama Cloud answers at https://ollama.com/api and /v1." ], "score": 59 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ollama.json", "live": { "slug": "ollama", "versions": [ { "registry": "github", "name": "ollama/ollama", "version": "v0.40.13", "released": "2026-10-28", "seenAt": "2026-10-30T14:32:47.443287356Z" }, { "registry": "npm", "name": "ollama", "version": "0.6.4", "seenAt": "2026-10-30T14:32:46.59335447Z" }, { "registry": "pypi", "name": "ollama", "version": "0.6.3", "released": "2026-09-29", "seenAt": "2026-10-30T14:32:46.481819234Z" } ], "githubStars": 185340, "npmWeekly": 1076764, "pypiWeekly": 2863544, "securityTxt": { "url": "https://ollama.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:59.65285075Z" }, "llmsTxt": { "url": "https://docs.ollama.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-30T15:08:13.260940618Z" }, "domain": { "domain": "ollama.com", "registered": "2017-05-08", "source": "https://rdap.verisign.com/com/v1/domain/ollama.com", "checkedAt": "2026-10-25T14:05:57.352530132Z" }, "pages": [ { "url": "https://ollama.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-30T05:44:34.820534977Z", "changedAt": "2026-10-30T05:44:34.820534977Z", "fingerprint": "8cdc2700f37b" }, { "url": "https://ollama.com/terms", "kind": "terms", "status": 200, "checkedAt": "2026-10-30T05:44:37.073987084Z", "changedAt": "2026-10-30T05:44:37.073987084Z", "fingerprint": "cfc2232f1189" } ], "github": { "repo": "ollama/ollama", "checkedAt": "2026-10-26T07:51:02.501424425Z", "stars": 184802, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-26T04:25:58Z", "openIssues": 2600, "openPRs": 1832, "issuesOpened90d": 671, "issuesClosed90d": 412, "prsMerged90d": 284, "replies": { "sampled": 18, "unanswered": 8, "medianDays": 0.2 } }, "updatedAt": "2026-10-30T15:38:59.65285075Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "syv.ai (syv-ai on GitHub)", "b": "Ollama Inc.", "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": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "MIT (server, CLI and desktop app). Ollama Cloud is a closed service under the ollama.com terms, and each model carries its own licence", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "none", "b": "2026-10-01", "name": "Last release" }, { "a": "no document linked", "b": "2026-05-01", "name": "Terms last updated" }, { "a": "no date given", "b": "2026-03-01", "name": "Privacy policy last updated" }, { "a": "", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "", "b": "yes", "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": "2.4k stars", "b": "181k stars, 872k npm/wk", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "HyperQwen and Ollama score within a point of each other on agent readiness, 56.9 (C) and 56.6 (C). Ollama leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, HyperQwen or Ollama?" }, { "answer": "Neither needs a key.", "question": "Do HyperQwen and Ollama need an API key?" }, { "answer": "No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Ollama.", "question": "Can an agent call HyperQwen and Ollama without installing anything?" }, { "answer": "Yes. HyperQwen is open source (Apache-2.0). Ollama is open source (MIT (server, CLI and desktop app). Ollama Cloud is a closed service under the ollama.com terms, and each model carries its own licence).", "question": "Are HyperQwen and Ollama open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 59 against 53", "Security \u0026 auth, 41 against 28" ], "also": [ "No incidents deducted, where Ollama 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": [ "Schema \u0026 documentation, 79 against 56", "Agent ergonomics, 75 against 65", "Maintenance \u0026 community, 81 against 68", "Transparency \u0026 trust, 63 against 55" ], "also": null, "goodFor": "A person or an agent that wants an open model behind a local API with one install, and for pointing Claude Code, Codex or OpenCode at local or cloud models.", "slug": "ollama", "watchFor": "No credential on the local API, and any caller that reaches it can pull, push, create and delete models" } ], "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-ollama.json", "title": "AnythingLLM vs Ollama", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-ollama" }, { "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-ollama.json", "title": "Berth vs Ollama", "url": "https://www.anchorterminal.com/compare/berth-vs-ollama" }, { "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-ollama.json", "title": "BitRun vs Ollama", "url": "https://www.anchorterminal.com/compare/bitrun-vs-ollama" }, { "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-ollama.json", "title": "Docker Model Runner vs Ollama", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-ollama" }, { "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-ollama.json", "title": "Foundry Local vs Ollama", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-ollama" }, { "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-ollama.json", "title": "Core vs Ollama", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-ollama" }, { "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-ollama.json", "title": "GPT4All vs Ollama", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan.json", "title": "HyperQwen vs Jan", "url": "https://www.anchorterminal.com/compare/hyperqwen-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-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-ollama.json", "title": "Jan vs Ollama", "url": "https://www.anchorterminal.com/compare/jan-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-ollama.json", "title": "Khoj vs Ollama", "url": "https://www.anchorterminal.com/compare/khoj-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-ollama.json", "title": "KoboldCpp vs Ollama", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-ollama.json", "title": "Lemonade vs Ollama", "url": "https://www.anchorterminal.com/compare/lemonade-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-ollama.json", "title": "llama.cpp vs Ollama", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/lm-studio-vs-ollama.json", "title": "LM Studio vs Ollama", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-ollama.json", "title": "LocalAI vs Ollama", "url": "https://www.anchorterminal.com/compare/localai-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.json", "title": "MLX LM vs Ollama", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-open-webui.json", "title": "Ollama vs Open WebUI", "url": "https://www.anchorterminal.com/compare/ollama-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-screenpipe.json", "title": "Ollama vs screenpipe", "url": "https://www.anchorterminal.com/compare/ollama-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-text-generation-webui.json", "title": "Ollama vs TextGen", "url": "https://www.anchorterminal.com/compare/ollama-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-vllm.json", "title": "Ollama vs vLLM", "url": "https://www.anchorterminal.com/compare/ollama-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/ollama-vs-underdog.json", "title": "Ollama vs Underdog", "url": "https://www.anchorterminal.com/compare/ollama-vs-underdog" } ], "scores": [ { "by": 6, "edge": "hyperqwen", "hyperqwen": 59, "key": "reliability", "name": "Reliability", "ollama": 53, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 23, "edge": "ollama", "hyperqwen": 56, "key": "schema", "name": "Schema \u0026 documentation", "ollama": 79, "weight": 13 }, { "by": 10, "edge": "ollama", "hyperqwen": 65, "key": "ergonomics", "name": "Agent ergonomics", "ollama": 75, "weight": 13 }, { "by": 13, "edge": "hyperqwen", "hyperqwen": 41, "key": "security", "name": "Security \u0026 auth", "ollama": 28, "weight": 14 }, { "by": 0, "edge": "", "hyperqwen": 60, "key": "payments", "name": "Payments \u0026 pricing", "ollama": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 13, "edge": "ollama", "hyperqwen": 68, "key": "maintenance", "name": "Maintenance \u0026 community", "ollama": 81, "weight": 7 }, { "by": 8, "edge": "ollama", "hyperqwen": 55, "key": "transparency", "name": "Transparency \u0026 trust", "ollama": 63, "weight": 7 } ], "summary": "HyperQwen and Ollama score within a point of each other on agent readiness, 56.9 (C) and 56.6 (C). Ollama leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community 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.", "ollama": "An OpenAPI 3.1 file for the 15 native operations and llms.txt with 68 links to Markdown pages. No credential on the local API, and any caller that reaches it can pull, push, create and delete models." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama", "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.min.md" }, "markdown": "HyperQwen and Ollama score within a point of each other on agent readiness, 56.9 (C) and 56.6 (C). Ollama leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community 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- Ollama: grade C, 56.6/100, rank #748 of 1239. Markdown https://www.anchorterminal.com/tools/ollama.md · JSON https://www.anchorterminal.com/api/v1/tools/ollama.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- Reliability, 59 against 53\n- Security \u0026 auth, 41 against 28\n\nAlso in its favour:\n- No incidents deducted, where Ollama 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### Ollama (C)\n\nGood for: A person or an agent that wants an open model behind a local API with one install, and for pointing Claude Code, Codex or OpenCode at local or cloud models.\n\nAhead on:\n- Schema \u0026 documentation, 79 against 56\n- Agent ergonomics, 75 against 65\n- Maintenance \u0026 community, 81 against 68\n- Transparency \u0026 trust, 63 against 55\n\nWatch for: No credential on the local API, and any caller that reaches it can pull, push, create and delete models\n\n\n## Score by category\n\n| Category | Weight | HyperQwen | Ollama | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 53 | HyperQwen +6 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 56 | 79 | Ollama +23 |\n| Agent ergonomics | 13% (16.2 this run) | 65 | 75 | Ollama +10 |\n| Security \u0026 auth | 14% (17.5 this run) | 41 | 28 | HyperQwen +13 |\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 | 81 | Ollama +13 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 63 | Ollama +8 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **56.9 · C** | **56.6 · C** | |\n\n## Facts side by side\n\n| Fact | HyperQwen | Ollama |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | syv.ai (syv-ai on GitHub) | Ollama Inc. |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | None |\n| Pricing | Free | Freemium |\n| x402 | no | no |\n| Licence | Apache-2.0 | MIT (server, CLI and desktop app). Ollama Cloud is a closed service under the ollama.com terms, and each model carries its own licence |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | none | 2026-10-01 |\n| Terms last updated | no document linked | 2026-05-01 |\n| Privacy policy last updated | no date given | 2026-03-01 |\n| Customer content may train models | | not found in the text |\n| Terms restrict automated access | | yes |\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 | 2.4k stars | 181k stars, 872k npm/wk |\n| Agent reviews | none | 2.5/5 (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**Ollama.** An OpenAPI 3.1 file for the 15 native operations and llms.txt with 68 links to Markdown pages. No credential on the local API, and any caller that reaches it can pull, push, create and delete models.\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### Ollama\n\n1. Send `\"stream\": false` for one JSON body. The native routes stream NDJSON by default\n2. Set `OLLAMA_CONTEXT_LENGTH=64000` or `options.num_ctx` before agent work. The default is 4k below 24 GiB of VRAM\n3. Back off on a 503. It means the queue (512 by default) is full\n4. Put an authenticating proxy in front before binding past 127.0.0.1. The server checks no credential\n5. Expect model names with a `cloud` tag to run on Ollama's servers. They need `ollama signin` and fail with `OLLAMA_NO_CLOUD=1`\n\n## Questions\n\n### Which is better for AI agents, HyperQwen or Ollama?\n\nHyperQwen and Ollama score within a point of each other on agent readiness, 56.9 (C) and 56.6 (C). Ollama leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.\n\n### Do HyperQwen and Ollama need an API key?\n\nNeither needs a key.\n\n### Can an agent call HyperQwen and Ollama without installing anything?\n\nNo hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Ollama.\n\n### Are HyperQwen and Ollama open source?\n\nYes. HyperQwen is open source (Apache-2.0). Ollama is open source (MIT (server, CLI and desktop app). Ollama Cloud is a closed service under the ollama.com terms, and each model carries its own licence).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hyperqwen\", \"b\": \"ollama\"}`. From a terminal: `anchor compare hyperqwen ollama`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hyperqwen.json and https://www.anchorterminal.com/api/v1/tools/ollama.json\n\n## Other comparisons with HyperQwen or Ollama\n\n- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)\n- [AnythingLLM vs Ollama](https://www.anchorterminal.com/compare/anythingllm-vs-ollama.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [Berth vs Ollama](https://www.anchorterminal.com/compare/berth-vs-ollama.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [BitRun vs Ollama](https://www.anchorterminal.com/compare/bitrun-vs-ollama.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Docker Model Runner vs Ollama](https://www.anchorterminal.com/compare/docker-model-runner-vs-ollama.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Foundry Local vs Ollama](https://www.anchorterminal.com/compare/foundry-local-vs-ollama.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [Core vs Ollama](https://www.anchorterminal.com/compare/ghost-core-vs-ollama.md)\n- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)\n- [GPT4All vs Ollama](https://www.anchorterminal.com/compare/gpt4all-vs-ollama.md)\n- [HyperQwen vs Jan](https://www.anchorterminal.com/compare/hyperqwen-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 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 Ollama](https://www.anchorterminal.com/compare/jan-vs-ollama.md)\n- [Khoj vs Ollama](https://www.anchorterminal.com/compare/khoj-vs-ollama.md)\n- [KoboldCpp vs Ollama](https://www.anchorterminal.com/compare/koboldcpp-vs-ollama.md)\n- [Lemonade vs Ollama](https://www.anchorterminal.com/compare/lemonade-vs-ollama.md)\n- [llama.cpp vs Ollama](https://www.anchorterminal.com/compare/llama-cpp-vs-ollama.md)\n- [LM Studio vs Ollama](https://www.anchorterminal.com/compare/lm-studio-vs-ollama.md)\n- [LocalAI vs Ollama](https://www.anchorterminal.com/compare/localai-vs-ollama.md)\n- [MLX LM vs Ollama](https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.md)\n- [Ollama vs Open WebUI](https://www.anchorterminal.com/compare/ollama-vs-open-webui.md)\n- [Ollama vs screenpipe](https://www.anchorterminal.com/compare/ollama-vs-screenpipe.md)\n- [Ollama vs TextGen](https://www.anchorterminal.com/compare/ollama-vs-text-generation-webui.md)\n- [Ollama vs vLLM](https://www.anchorterminal.com/compare/ollama-vs-vllm.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n- [Ollama vs Underdog](https://www.anchorterminal.com/compare/ollama-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 Ollama", "url": "" } ], "description": "HyperQwen and Ollama score within a point of each other for local inference, 56.9 and 56.6 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "HyperQwen C 56.9", "Ollama C 56.6", "scores" ], "h1": "HyperQwen vs Ollama", "image": "https://www.anchorterminal.com/assets/og/compare-hyperqwen-vs-ollama.png", "path": "/compare/hyperqwen-vs-ollama", "published": "2026-10-01", "section": "tools", "title": "HyperQwen vs Ollama for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama" }, "tokens": { "markdown": 2700, "slim": 680 }, "version": 1 }