# HyperQwen vs PrivateGPT (slim)

> PrivateGPT scores 53.2 (D) to HyperQwen's 50.8 (D) for local inference. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt.md (~5,550 tokens) · this version ~3,330 tokens · JSON https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt.json · canonical https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-07
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

PrivateGPT scores 53.2 (D) on agent readiness against HyperQwen's 50.8 (D), and leads in 4 of 7 scored categories. HyperQwen leads on security & auth. Both do local inference.

- HyperQwen: D 50.8, rank #1051 of 1510 · https://www.anchorterminal.com/tools/hyperqwen.min.md
- PrivateGPT: D 53.2, rank #960 of 1510 · https://www.anchorterminal.com/tools/privategpt.min.md
- HyperQwen, good 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. Ahead on Security & auth, 41 against 32. Also Free to start without a card; No incidents deducted, where PrivateGPT loses 7 points for them.
- PrivateGPT, good for A local backend for apps and agents that want an Anthropic-style API with document retrieval, citations and tools over local models. Ahead on Reliability, 70 against 46; Schema & documentation, 83 against 56; Maintenance & community, 41 against 28; Transparency & trust, 64 against 55.

| Category | HyperQwen | PrivateGPT |
| --- | --- | --- |
| Reliability (16%) | 46 | 70 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 56 | 83 |
| Agent ergonomics (13%) | 65 | 64 |
| Security & auth (14%) | 41 | 32 |
| Payments & pricing (10%) | 60 | 60 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 28 | 41 |
| Transparency & trust (7%) | 55 | 64 |

| Fact (where they differ) | HyperQwen | PrivateGPT |
| --- | --- | --- |
| Vendor | syv.ai (syv-ai on GitHub) | Zylon (PriBAI Technology Spain S.L.) |
| llms.txt | no | yes |
| Last release | none | 2026-06-18 |
| Terms last updated | no document linked | no date given |
| Privacy policy last updated | no date given | no document linked |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Popularity | none | 58k stars |

Why the edge:
- Schema & documentation, PrivateGPT +27: HyperQwen 56 (Read with the API lines, for the local server an agent calls, which is vLLM 0.32.0's OpenAI-compatible server. vLLM's FastAPI app serves its own OpenAPI document at runtime. HyperQwen publishes none and documents only the base URL and the served model name (12 of 25). No llms.txt. The docs are Markdown files in the repository, readable raw (5 of 10). The README's decision tree and the knob tables say which setup suits which workload and when not to use one (for example `CTX=huge` only when the request wouldn't otherwise fit, `SPEC_ATTN=0` past 64k). The routes themselves are left to vLLM's docs (14 of 20). vLLM's typed request models, tool argument schemas enforced by default (`TOOL_STRICT=parameter`), and launchers that refuse invalid knob values before boot (11 of 15). An OpenCode client config, benchmark commands and a 64-item gotchas file with failure signatures such as the 400 for `tool_choice: auto` without a parser. No curl example and no error reference (9 of 15). No versions or changelog, a fixed `/v1` prefix and model name, and PATCHES.md with an upstream reference for every patch (5 of 15).); PrivateGPT 83 (An OpenAPI file of 38 paths in the repository (fern/openapi/openapi.json, version 1.0.1), also served by each instance at /openapi.json, with the messages API modelled on Anthropic's (25). docs.privategpt.dev serves llms.txt, a Markdown copy of every page and a docs MCP server (10). The docs explain each endpoint group, startup and model discovery, and a compatibility table says which parts of the Claude API are supported, partial or missing, such as prompt caching and OAuth (15 of 20). Pydantic models with literal type discriminators and required fields, while settings and tool context are wide objects (12 of 15). curl examples for each guide and a `/v1/messages/validate` route, with error shapes not documented as a list (9 of 15). `/v1` paths and a dated changelog, though releases trail main by more than four months (12 of 15). 25 + 10 + 15 + 12 + 9 + 12 = 83.)
- Reliability, PrivateGPT +24: HyperQwen 46 (Read with the local-software lines, since HyperQwen runs on the owner's GPU with no hosted service. A prebuilt image on ghcr.io with an immutable tag per commit, and a venv route, with the GPU, driver, Python and disk needs stated and Volta ruled out. The Compose route still needs a clone of the repository, and first start downloads an unpinned third-party checkpoint (18 of 20). Two GitHub workflows, `patch integrity` (about 40 GPU-less checks, among them the no-key loopback bind) and `docker image` (applies every patch, runs the build-time tests, pushes `latest` and `sha-<7>`, rebuilds weekly). Their pass state wasn't readable on 4 November 2026, and the 30 October reading of passing badges came from shields.io, a third-party copy of GitHub data, so it is struck. Nothing in CI runs on a GPU (15 of 25). The 30 October issue and pull request counts also came from shields.io and are struck. The clone shows 210 commits from 17 authors between 4 October and 3 November 2026, fix commits that cite issue numbers, and 19 merged pull requests from five outside accounts in that time. The README still describes an open speculation fault on sm80 cards (#98, #72), and open regressions couldn't be counted (10 of 25). No tags, releases or changelog. The per-pin notes (docs/vllm-0.29.md to vllm-0.32.md) and PATCHES.md record what changed with each vLLM upgrade (3 of 15). No version number and nothing declared stable (0). 18 + 15 + 10 + 3 + 0 = 46.); PrivateGPT 70 (Read with the local-software lines, since PrivateGPT runs on the owner's hardware with no hosted service behind it. Homebrew tap, `uv tool install` from Zylon's own wheel index (not PyPI) and Docker Hub images `zylonai/private-gpt` 1.0.1 of 18 June 2026. Python 3.11 only, stated in pyproject.toml, and it needs a separate OpenAI-compatible model server (17 of 20). tests.yml runs format, lint, typecheck and the tests affected by each push on main, with the full suite of 217 test files on release pull requests. We couldn't see run results because the GitHub API refused us, and issue #2441 of 1 November reports the Claude spec update workflow failing (15 of 25). GitHub shows 3 open issues of about 1,250, because a stale bot closes quiet issues, two security reports among them, with no maintainer reply (12 of 25). release-please keeps a changelog with breaking changes marked, but no release has been cut since 1.0.1 while main carries 51 commits since 8 August, among them a breaking observability change of 23 October (11 of 15). Version 1.0.1 (15). 17 + 15 + 12 + 11 + 15 = 70.)
- Maintenance & community, PrivateGPT +13: HyperQwen 28 (No versioned release. Every push to main that touches code publishes `latest` and an immutable `sha-<7>` image from CI, and the clone's newest commit is dated 3 November 2026 (0 of 30). The `sha-<7>` tags are per-commit builds with no version or changelog entry, which this run doesn't count as releases, as it reads untagged commits and rolling snapshot builds on other listings, and no release has ever been published. No releases, tags or dated changelog entries in the 90 days from 6 August. The four vLLM pin moves (0.29.0 on 23 September, 0.30.0 on 28 September, 0.31.0 on 9 October, 0.32.0 on 20 October, each with upgrade notes) are commits to main with no version, tag or changelog entry of HyperQwen's own, so they aren't counted as releases, and the line is a threshold (0). Responsiveness rests on the clone, since the 30 October issue counts came from shields.io and are struck. 42 authors have commits since 6 August, 19 pull requests from five outside accounts were merged between 4 October and 3 November, and community field reports are written into docs/reproductions with credit. Reply times weren't read, as github.com pages answer 403 (15 of 25). Not an MCP server and no SDK of its own. OpenAI clients work against it (5 of 15). Pinned to vLLM 0.32.0 with the CUDA compiler set pinned to match, a weekly image rebuild and CI that fails when PATCHES.md is out of date (8 of 10). 0 + 0 + 15 + 5 + 8 = 28.); PrivateGPT 41 (The last release is 1.0.1, dated 16 June 2026 in the changelog and tagged 18 June, 141 days before the check (10 of 30). No release or dated changelog entry in the 90 days since 8 August (0 of 20). Maintainers commit most days and outside pull requests merged in October, but 23 pull requests are open and the stale bot closes issues, including two security reports, with no maintainer reply (13 of 25). Homebrew tap, wheel index and Docker images ship with releases, and Anthropic SDKs work against it. No product MCP server of its own, though it connects to remote MCP servers (10 of 15). Dependabot, uv.lock, actions pinned by commit since 19 October and dependency CVE upgrades in June, July and August (8 of 10). 10 + 0 + 13 + 10 + 8 = 41.)
- Security & auth, HyperQwen +9: HyperQwen 41 (Read with the tool checklist. vLLM's optional static key, set by `VLLM_API_KEY` or `api_key.txt`, with no scopes and checked only under `/v1`, `/v2`, `/inference` and `/cohere` (per the vLLM listing's check of 9 October 2026). HyperQwen's launcher binds 127.0.0.1 when no key is set, Compose publishes on 127.0.0.1 by default, a CI check tests the no-key bind, and `.env.example` says the key is required before exposing the port (15 of 30). Inference only, but vLLM's control routes such as `/pause` answer without the key, and the container runs as root (6 of 20). The server returns model output and fetches no outside content with the default `VISION=0`. No prompt-injection guidance (9 of 15). vLLM's access logs and Prometheus `/metrics`, with optional per-request timing, and no per-key record (8 of 15). No SECURITY.md, disclosure address or advisories, and no security.txt on syv.ai. The image is rebuilt weekly for base-image fixes, but the base weights come from a third-party repository (dbirks/Qwen3.8-27B-W4A16-AutoRound) downloaded with no pinned revision or checksum of the project's own (3 of 20).); PrivateGPT 32 (Read with the tool checklist. Authentication is off by default, and when on it is one static secret compared with the whole `Authorization` header, with a sample `secret:key` value in settings.yaml. There are no per-user keys or scopes, `private-gpt serve` binds 0.0.0.0 by default and CORS allows every origin, so a default start answers anyone on the network. The docs disagree on the header (Bearer, Basic or an Anthropic key) (6 of 30). Tools are chosen per request and code execution has internet off by default, but bash runs as a host subprocess with resource limits, not in a container, and nothing asks for approval (6 of 20). Web fetch, web search, documents and MCP servers bring untrusted text to the model. A public prompt-injection report (#2270) was closed by the stale bot, and no guidance was found beyond a 19 October change that bounds tool output (3 of 15). OpenTelemetry traces cover every request, model call, tool and sandbox step, off by default, with local JSON lines exporters (10 of 15). SECURITY.md since 23 July 2026 asks for private reports through GitHub or Discord. No bounty, no certification, and the June and July security reports in issues were closed by a bot with no reply (7 of 20). 6 + 6 + 3 + 10 + 7 = 32.)
- Transparency & trust, PrivateGPT +9: HyperQwen 55 (The editorial half. Apache-2.0 for the repository, the vendored pth-flash-attn source and the fa2 plugin (with FlashAttention's BSD-3-Clause notices kept), and Apache-2.0 on the base model and every checkpoint it downloads (30). No privacy or data statement for the software. The Docker image turns off vLLM's usage statistics, but the venv launchers don't, and nothing in the README or docs says so. Outbound calls are downloads from Hugging Face and ghcr.io (12 of 30). PATCHES.md says what retires each patch, and upgrade notes say what changed per vLLM pin. Knobs and setups change without a deprecation notice or dates (8 of 20). The telemetry switch is set in the Dockerfile and not documented for venv installs, which inherit vLLM's default (10 of 20).); PrivateGPT 64 (Apache-2.0 (30). The OpenAPI description says no data leaves the execution environment, but the docs list cloud gateways as model providers and web search goes to outside services when enabled, and no privacy statement covers the software. Zylon's privacy policy covers its website (15 of 30). The 1.0 rewrite was marked as a breaking change in the changelog, and there is no deprecation policy (6 of 20). No telemetry to Zylon found in the source. OpenTelemetry tracing is off by default and its exporters write local files or to the owner's collector, documented on the observability page (16 of 20). 30 + 15 + 6 + 16 = 67.)