Head to head · Local inference · October 2026 research run
HyperQwen vs PrivateGPT
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.
Best local AI models and assistants · All 280 local ai comparisons
Which one, for what
Best 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 in its favour
- Free to start without a card
- No incidents deducted, where PrivateGPT loses 7 points for them
Watch for
Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap
Best 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
Watch for
Authentication is off by default, the server binds 0.0.0.0 and CORS allows every origin
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
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.
Score by category
| Category | Weight this run | HyperQwen | PrivateGPT | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 46 | 70 | PrivateGPT +24 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 56 | 83 | PrivateGPT +27 |
| Agent ergonomics | 13%16.2 | 65 | 64 | HyperQwen +1 |
| Security & auth | 14%17.5 | 41 | 32 | HyperQwen +9 |
| Payments & pricing | 10%12.5 | 60 | 60 | even |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 28 | 41 | PrivateGPT +13 |
| Transparency & trust | 7%8.8 | 55 | 64 | PrivateGPT +9 |
| Negative events | ≤15 | 0 | -7 | |
| Total | D 50.8/100 | D 53.2/100 |
Facts side by side
| Fact | HyperQwen | PrivateGPT |
|---|---|---|
| Kind | HTTP API | HTTP API |
| Vendor | syv.ai (syv-ai on GitHub) | Zylon (PriBAI Technology Spain S.L.) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| 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 |
How the scores have moved
PrivateGPT
No score change since it was listed.
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.
PrivateGPT
PrivateGPT 1.0 is an Apache-2.0 server that gives local models an Anthropic-style messages API with retrieval, citations, tools and MCP, documented by an OpenAPI file and llms.txt. A default start has authentication off, listens on every interface and reads local paths given as URIs, and a public report of that was closed without a reply.
Before you call either
HyperQwen
- Call http://127.0.0.1:18020/v1 with model
qwen3.8-27band theVLLM_API_KEYvalue, or any string if no key was set - Poll
/healthbefore 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 forCTX=huge) so the client doesn't truncate - Ask the owner to set
VLLM_API_KEYbefore settingBIND=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
PrivateGPT
- Turn on
server.authand bind to 127.0.0.1 before exposing an instance, since a default start answers any caller - Point Anthropic SDKs or Claude Code at the server with
ANTHROPIC_BASE_URL=http://localhost:8080 - Start an OpenAI-compatible model server and an embedding server first, and set
OPENAI_API_BASEandOPENAI_EMBEDDING_API_BASE - Pass ingested documents through
tool_contextto get answers with citations - Treat web fetch, search and MCP results as untrusted, since PrivateGPT documents no prompt-injection handling
Questions
Which is better for AI agents, HyperQwen or PrivateGPT?
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.
Do HyperQwen and PrivateGPT need an API key?
Neither needs a key.
Can an agent call HyperQwen and PrivateGPT without installing anything?
No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for PrivateGPT.
Are HyperQwen and PrivateGPT open source?
Yes. HyperQwen is open source (Apache-2.0). PrivateGPT is open source (Apache-2.0).
Other comparisons with HyperQwen or PrivateGPT
- AnythingLLM vs HyperQwen
- AnythingLLM vs PrivateGPT
- Berth vs HyperQwen
- Berth vs PrivateGPT
- BitRun vs HyperQwen
- BitRun vs PrivateGPT
- Docker Model Runner vs HyperQwen
- Docker Model Runner vs PrivateGPT
- Foundry Local vs HyperQwen
- Foundry Local vs PrivateGPT
- Core vs HyperQwen
- Core vs PrivateGPT
- GPT4All vs HyperQwen
- GPT4All vs PrivateGPT
- HyperQwen vs Jan
- HyperQwen vs Khoj
- HyperQwen vs KoboldCpp
- HyperQwen vs Lemonade
- HyperQwen vs llama.cpp
- HyperQwen vs LM Studio
- HyperQwen vs LocalAI
- HyperQwen vs MLX LM
- HyperQwen vs Ollama
- HyperQwen vs Open WebUI
- HyperQwen vs screenpipe
- HyperQwen vs TextGen
- HyperQwen vs vLLM
- Jan vs PrivateGPT
- KoboldCpp vs PrivateGPT
- Lemonade vs PrivateGPT
- llama.cpp vs PrivateGPT
- LM Studio vs PrivateGPT
- LocalAI vs PrivateGPT
- MLX LM vs PrivateGPT
- Ollama vs PrivateGPT
- Open WebUI vs PrivateGPT
- PrivateGPT vs screenpipe
- PrivateGPT vs TextGen
- PrivateGPT vs vLLM
- Khoj vs PrivateGPT
- LocalGhost vs PrivateGPT
- HyperQwen vs Underdog
Machine-readable
- This page as Markdown
/compare/hyperqwen-vs-privategpt.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/hyperqwen.json·/api/v1/tools/privategpt.json - From a terminal
anchor compare hyperqwen privategpt(the CLI) - Over MCP
compare_tools {"a": "hyperqwen", "b": "privategpt"}at/mcp, no key