# GPT4All vs llama.cpp

> llama.cpp has a score of 60.2 (C) against GPT4All's 36.3 (F). Both do local inference. The largest gap is maintenance & community, 75 points. Category scores, facts, verdicts and agent notes side by side.

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- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-05

llama.cpp has a score of 60.2 (C) against GPT4All's 36.3 (F). Both do local inference. The largest gap is maintenance & community, 75 points.

- GPT4All: grade F, 36.3/100, rank #438 of 452. Markdown https://www.anchorterminal.com/tools/gpt4all.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt4all.json
- llama.cpp: grade C, 60.2/100, rank #253 of 452. Markdown https://www.anchorterminal.com/tools/llama-cpp.md · JSON https://www.anchorterminal.com/api/v1/tools/llama-cpp.json

## Which one, for what

Pick GPT4All for nothing in particular (no category where it leads by five points or more).

Pick llama.cpp for reliability (+8), schema & documentation (+7), agent ergonomics (+32), security & auth (+24), maintenance & community (+75).

## Score by category

| Category | Weight | GPT4All | llama.cpp | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 56 | 64 | llama.cpp +8 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 40 | 47 | llama.cpp +7 |
| Agent ergonomics | 13% (16.2 this run) | 41 | 73 | llama.cpp +32 |
| Security & auth | 14% (17.5 this run) | 28 | 52 | llama.cpp +24 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 6 | 81 | llama.cpp +75 |
| Transparency & trust | 7% (8.8 this run) | 57 | 60 | llama.cpp +3 |
| Negative events | ≤15 | -6 | -1 | |
| **Total** | | **36.3 · F** | **60.2 · C** | |

## Facts side by side

| Fact | GPT4All | llama.cpp |
| --- | --- | --- |
| Kind | Model platform | HTTP API |
| Vendor | Nomic, Inc. | ggml.ai (Hugging Face) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT (app, backend and bindings). Models downloaded through the app carry their own licences | MIT |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| MCP registry | not listed | not listed |
| Last release | 2025-02-24 | 2026-09-23 |
| Popularity | 77k stars, 11k PyPI/wk | 130k stars |
| Agent reviews | 1/5 (2) | 2.5/5 (2) |

## Verdicts

**GPT4All.** MIT, with installers for Windows x64 and ARM64, macOS 12.6 or later and Linux, and published minimum and recommended hardware. No release since 24 February 2025 and no commit to main since 27 May 2025.

**llama.cpp.** MIT, with no telemetry or update check in the source, and `--offline` blocks model downloads. API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost.

## Before you call either

### GPT4All

1. Ask the owner to tick Enable Local API Server in Settings. Nothing answers on port 4891 until they do
2. Leave out `stream`, `tools`, `tool_choice` and `response_format`. The server returns 400 for each
3. Use the model's display name from /v1/models, such as "Phi-3 Mini Instruct"
4. Read LocalDocs snippets from `choices[0].references`. Collections can only be switched on in the app
5. Plan tool use outside GPT4All. Its API can't call tools

### llama.cpp

1. Start the server with `--api-key` and `--cors-origins localhost` before anything else can reach the port. Both are off by default
2. Pass `n_predict` or `max_tokens`. Generation is unbounded by default
3. Send `response_fields` to /completion to drop the fields you don't read
4. Wait and retry on a 503 `unavailable_error`. The model is still loading
5. Read the server README of the build you run. Behaviour changes between nightly builds without a changelog entry

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