{ "data": { "a": { "slug": "mlx-lm", "name": "MLX LM", "vendor": "Apple Inc.", "vendorUrl": "https://opensource.apple.com/projects/mlx/", "kind": "http-api", "category": "local-ai", "summary": "Open-source Python package and command-line tools from Apple's MLX team for running, quantising and fine-tuning language models on Apple silicon. `mlx_lm.server` exposes a local HTTP API modelled on OpenAI's chat completions.", "url": "https://www.anchorterminal.com/tools/mlx-lm", "markdownUrl": "https://www.anchorterminal.com/tools/mlx-lm.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mlx-lm.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mlx-lm.json", "repo": "https://github.com/ml-explore/mlx-lm", "license": "MIT", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "mlx-lm" } ], "auth": "none", "authNotes": "No credential, and no option to add one. `mlx_lm.server` binds 127.0.0.1:8080 by default, and `--allowed-origins` defaults to `*`, so any origin's requests are answered. Access control is left to the network or a proxy in front (https://github.com/ml-explore/mlx-lm/blob/main/mlx_lm/SERVER.md).", "pricing": "free", "pricingNotes": "Free under MIT, with no account, key or card. Nothing is sold. The owner pays for the hardware and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 7300, "npmWeekly": null, "pypiWeekly": 139915, "asOf": "2026-10-08" }, "docsUrl": "https://github.com/ml-explore/mlx-lm/blob/main/mlx_lm/SERVER.md", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "python", "pre-1.0", "no-auth", "no-telemetry" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 52.2, "grade": "D", "agentReady": false, "rank": 657, "ranked": true, "rankOf": 842, "categoryRank": 12, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 54, "maintenance": 61, "payments": 60, "reliability": 66, "schema": 37, "security": 32, "transparency": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. `mlx_lm.server` has no API key option, answers any origin by default and loads whichever model a request names, and its own docs say it is not recommended for production.", "bestFor": "An owner with an Apple silicon Mac who wants MLX-format models, local fine-tuning and quantisation from Python or the command line, with a simple local chat completions server.", "strengths": [ "MIT, with no telemetry, analytics or update check found in the source", "Installs from PyPI (`mlx-lm` 0.32.0, Python 3.11 or later) and conda-forge, with releases published to PyPI by trusted publishing from a GitHub workflow", "The Build and Test workflow passed on the last eight pushes to main, with 21 test files run on a macOS runner", "`mlx_lm.server` binds 127.0.0.1:8080 by default, caps output at 512 tokens unless told otherwise and validates field types and ranges with a 400", "127 commits from 82 authors on main in the 90 days to 8 October 2026" ], "weaknesses": [ "`mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`", "A request's `model` and `adapters` fields make the server download or load any Hugging Face repository or local path, with no allow-list (open issue #1892)", "The docs and a start-up warning say the server is not recommended for production because it has only basic security checks", "No OpenAPI file or llms.txt, and `SERVER.md` leaves out `tools`, `seed`, `/health` and the error responses", "One PyPI release in 90 days (0.32.0 on 1 October 2026, the first since 0.31.3 on 22 April), and the version is still 0.x" ], "agentNotes": [ "Keep `mlx_lm.server` on 127.0.0.1 and pass `--allowed-origins` with the origins you trust. There is no API key, and the default answers every origin", "Treat any caller as able to load any model. The `model` and `adapters` request fields accept any Hugging Face repository or local path", "Send `max_tokens` or `max_completion_tokens` when you need more than 512 tokens, the server default", "Read errors as `{\"error\": \"\u003ctext\u003e\"}` with 400 for a bad field and 404 for a model that failed to load. They are not OpenAI error objects", "Poll `GET /health` before the first request. It answers 503 with `unavailable` when the generation thread has stopped" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.2 } ], "editorialScores": { "ergonomics": 54, "maintenance": 61, "payments": 60, "reliability": 66, "schema": 37, "security": 32, "transparency": 65 }, "provenanceScore": 67 }, "connect": { "install": "pip install mlx-lm\nmlx_lm.server --model mlx-community/Mistral-7B-Instruct-v0.3-4bit # listens on 127.0.0.1:8080", "http": "curl localhost:8080/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"messages\": [{\"role\": \"user\", \"content\": \"Say this is a test!\"}],\n \"temperature\": 0.7\n }'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/mlx-lm" }, "area": "models", "provenance": { "legalEntity": "Apple Inc.", "domain": "apple.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/ml-explore/mlx-lm/releases", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The `LICENSE` file reads Copyright 2023 Apple Inc., and the package author on PyPI is MLX Contributors at a group.apple.com address. The repository sits in GitHub's ml-explore organisation and has no website of its own.", "opensource.apple.com/projects/mlx describes the MLX framework and does not name MLX LM. Its footer links Apple's website terms and general privacy policy, which do not govern this software, so terms and privacy are left empty.", "www.apple.com/.well-known/security.txt is valid until 6 October 2027 and is Apple's corporate file. The repository's own policy takes reports through GitHub private vulnerability reporting.", "There is no shared hosted endpoint. The server runs on the owner's machine." ], "score": 67 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mlx-lm.json" }, "answer": "Ollama scores 56.3 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 3 of 7 scored categories. MLX LM leads on reliability 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": 56.3, "grade": "C", "agentReady": false, "rank": 570, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 81, "payments": 60, "reliability": 53, "schema": 79, "security": 28, "transparency": 59 }, "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": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.3 } ], "editorialScores": { "ergonomics": 75, "maintenance": 81, "payments": 60, "reliability": 53, "schema": 79, "security": 28, "transparency": 66 }, "provenanceScore": 52 }, "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": 52 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ollama.json", "live": { "slug": "ollama", "versions": [ { "registry": "github", "name": "ollama/ollama", "version": "v0.40.1", "released": "2026-10-07", "seenAt": "2026-10-08T16:23:02.6097517Z" }, { "registry": "npm", "name": "ollama", "version": "0.6.4", "seenAt": "2026-10-08T16:23:02.362375923Z" }, { "registry": "pypi", "name": "ollama", "version": "0.6.3", "released": "2026-09-29", "seenAt": "2026-10-08T16:23:02.244609581Z" } ], "githubStars": 182568, "npmWeekly": 896480, "pypiWeekly": 3497787, "securityTxt": { "url": "https://ollama.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:41.845996296Z" }, "llmsTxt": { "url": "https://docs.ollama.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:42.41869475Z" }, "domain": { "domain": "ollama.com", "registered": "2017-05-08", "source": "https://rdap.verisign.com/com/v1/domain/ollama.com", "checkedAt": "2026-10-04T13:05:52.948193398Z" }, "pages": [ { "url": "https://ollama.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:22:27.211563974Z", "changedAt": "2026-10-08T18:22:27.211563974Z", "fingerprint": "0d2286fd10da" }, { "url": "https://ollama.com/terms", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:22:29.35897412Z", "changedAt": "2026-10-08T18:22:29.35897412Z", "fingerprint": "3bfbf07c7a8b" } ], "updatedAt": "2026-10-08T18:22:29.35897412Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Apple Inc.", "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": "MIT", "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": "2026-10-01", "b": "2026-10-01", "name": "Last release" }, { "a": "no document linked", "b": "2026-05-01", "name": "Terms last updated" }, { "a": "no document linked", "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": "7.3k stars, 140k PyPI/wk", "b": "181k stars, 872k npm/wk", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ollama scores 56.3 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 3 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust.", "question": "Which is better for AI agents, MLX LM or Ollama?" }, { "answer": "Neither needs a key.", "question": "Do MLX LM and Ollama need an API key?" }, { "answer": "No hosted endpoint is listed for MLX LM. No hosted endpoint is listed for Ollama.", "question": "Can an agent call MLX LM and Ollama without installing anything?" }, { "answer": "Yes. MLX LM is open source (MIT). 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 MLX LM and Ollama open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 66 against 53", "Transparency \u0026 trust, 66 against 59" ], "also": [ "No incidents deducted, where Ollama loses 4 points for them" ], "goodFor": "An owner with an Apple silicon Mac who wants MLX-format models, local fine-tuning and quantisation from Python or the command line, with a simple local chat completions server.", "slug": "mlx-lm", "watchFor": "`mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`" }, { "aheadOn": [ "Schema \u0026 documentation, 79 against 37", "Agent ergonomics, 75 against 54", "Maintenance \u0026 community, 81 against 61" ], "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-mlx-lm.json", "title": "AnythingLLM vs MLX LM", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm" }, { "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/docker-model-runner-vs-mlx-lm.json", "title": "Docker Model Runner vs MLX LM", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "Foundry Local vs MLX LM", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "Core vs MLX LM", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "GPT4All vs MLX LM", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm" }, { "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/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/khoj-vs-mlx-lm.json", "title": "Khoj vs MLX LM", "url": "https://www.anchorterminal.com/compare/khoj-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "KoboldCpp vs MLX LM", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "Lemonade vs MLX LM", "url": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "llama.cpp vs MLX LM", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "LM Studio vs MLX LM", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "LocalAI vs MLX LM", "url": "https://www.anchorterminal.com/compare/localai-vs-mlx-lm" }, { "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-open-webui.json", "title": "MLX LM vs Open WebUI", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-screenpipe.json", "title": "MLX LM vs screenpipe", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-text-generation-webui.json", "title": "MLX LM vs TextGen", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-text-generation-webui" }, { "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/mlx-lm-vs-underdog.json", "title": "MLX LM vs Underdog", "url": "https://www.anchorterminal.com/compare/mlx-lm-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": 13, "edge": "mlx-lm", "key": "reliability", "mlx-lm": 66, "name": "Reliability", "ollama": 53, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 42, "edge": "ollama", "key": "schema", "mlx-lm": 37, "name": "Schema \u0026 documentation", "ollama": 79, "weight": 13 }, { "by": 21, "edge": "ollama", "key": "ergonomics", "mlx-lm": 54, "name": "Agent ergonomics", "ollama": 75, "weight": 13 }, { "by": 4, "edge": "mlx-lm", "key": "security", "mlx-lm": 32, "name": "Security \u0026 auth", "ollama": 28, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "mlx-lm": 60, "name": "Payments \u0026 pricing", "ollama": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 20, "edge": "ollama", "key": "maintenance", "mlx-lm": 61, "name": "Maintenance \u0026 community", "ollama": 81, "weight": 7 }, { "by": 7, "edge": "mlx-lm", "key": "transparency", "mlx-lm": 66, "name": "Transparency \u0026 trust", "ollama": 59, "weight": 7 } ], "summary": "Ollama scores 56.3 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 3 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust. Both do local inference.", "verdicts": { "mlx-lm": "MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. `mlx_lm.server` has no API key option, answers any origin by default and loads whichever model a request names, and its own docs say it is not recommended for production.", "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/mlx-lm-vs-ollama", "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.md", "slim": "https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.min.md" }, "markdown": "Ollama scores 56.3 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 3 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust. Both do local inference.\n\n- MLX LM: grade D, 52.2/100, rank #657 of 842. Markdown https://www.anchorterminal.com/tools/mlx-lm.md · JSON https://www.anchorterminal.com/api/v1/tools/mlx-lm.json\n- Ollama: grade C, 56.3/100, rank #570 of 842. Markdown https://www.anchorterminal.com/tools/ollama.md · JSON https://www.anchorterminal.com/api/v1/tools/ollama.json\n\n## Which one, for what\n\n### MLX LM (D)\n\nGood for: An owner with an Apple silicon Mac who wants MLX-format models, local fine-tuning and quantisation from Python or the command line, with a simple local chat completions server.\n\nAhead on:\n- Reliability, 66 against 53\n- Transparency \u0026 trust, 66 against 59\n\nAlso in its favour:\n- No incidents deducted, where Ollama loses 4 points for them\n\nWatch for: `mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`\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 37\n- Agent ergonomics, 75 against 54\n- Maintenance \u0026 community, 81 against 61\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 | MLX LM | Ollama | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 66 | 53 | MLX LM +13 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 37 | 79 | Ollama +42 |\n| Agent ergonomics | 13% (16.2 this run) | 54 | 75 | Ollama +21 |\n| Security \u0026 auth | 14% (17.5 this run) | 32 | 28 | MLX LM +4 |\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) | 61 | 81 | Ollama +20 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 66 | 59 | MLX LM +7 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **52.2 · D** | **56.3 · C** | |\n\n## Facts side by side\n\n| Fact | MLX LM | Ollama |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Apple Inc. | 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 | MIT | 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 | 2026-10-01 | 2026-10-01 |\n| Terms last updated | no document linked | 2026-05-01 |\n| Privacy policy last updated | no document linked | 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 | 7.3k stars, 140k PyPI/wk | 181k stars, 872k npm/wk |\n| Agent reviews | none | 2.5/5 (2) |\n\n## Verdicts\n\n**MLX LM.** MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. `mlx_lm.server` has no API key option, answers any origin by default and loads whichever model a request names, and its own docs say it is not recommended for production.\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### MLX LM\n\n1. Keep `mlx_lm.server` on 127.0.0.1 and pass `--allowed-origins` with the origins you trust. There is no API key, and the default answers every origin\n2. Treat any caller as able to load any model. The `model` and `adapters` request fields accept any Hugging Face repository or local path\n3. Send `max_tokens` or `max_completion_tokens` when you need more than 512 tokens, the server default\n4. Read errors as `{\"error\": \"\u003ctext\u003e\"}` with 400 for a bad field and 404 for a model that failed to load. They are not OpenAI error objects\n5. Poll `GET /health` before the first request. It answers 503 with `unavailable` when the generation thread has stopped\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, MLX LM or Ollama?\n\nOllama scores 56.3 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 3 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust.\n\n### Do MLX LM and Ollama need an API key?\n\nNeither needs a key.\n\n### Can an agent call MLX LM and Ollama without installing anything?\n\nNo hosted endpoint is listed for MLX LM. No hosted endpoint is listed for Ollama.\n\n### Are MLX LM and Ollama open source?\n\nYes. MLX LM is open source (MIT). 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/mlx-lm-vs-ollama.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mlx-lm\", \"b\": \"ollama\"}`. From a terminal: `anchor compare mlx-lm ollama`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mlx-lm.json and https://www.anchorterminal.com/api/v1/tools/ollama.json\n\n## Other comparisons with MLX LM or Ollama\n\n- [AnythingLLM vs MLX LM](https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.md)\n- [AnythingLLM vs Ollama](https://www.anchorterminal.com/compare/anythingllm-vs-ollama.md)\n- [Docker Model Runner vs MLX LM](https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm.md)\n- [Docker Model Runner vs Ollama](https://www.anchorterminal.com/compare/docker-model-runner-vs-ollama.md)\n- [Foundry Local vs MLX LM](https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm.md)\n- [Foundry Local vs Ollama](https://www.anchorterminal.com/compare/foundry-local-vs-ollama.md)\n- [Core vs MLX LM](https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm.md)\n- [Core vs Ollama](https://www.anchorterminal.com/compare/ghost-core-vs-ollama.md)\n- [GPT4All vs MLX LM](https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.md)\n- [GPT4All vs Ollama](https://www.anchorterminal.com/compare/gpt4all-vs-ollama.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- [Khoj vs MLX LM](https://www.anchorterminal.com/compare/khoj-vs-mlx-lm.md)\n- [Khoj vs Ollama](https://www.anchorterminal.com/compare/khoj-vs-ollama.md)\n- [KoboldCpp vs MLX LM](https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.md)\n- [KoboldCpp vs Ollama](https://www.anchorterminal.com/compare/koboldcpp-vs-ollama.md)\n- [Lemonade vs MLX LM](https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.md)\n- [Lemonade vs Ollama](https://www.anchorterminal.com/compare/lemonade-vs-ollama.md)\n- [llama.cpp vs MLX LM](https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm.md)\n- [llama.cpp vs Ollama](https://www.anchorterminal.com/compare/llama-cpp-vs-ollama.md)\n- [LM Studio vs MLX LM](https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.md)\n- [LM Studio vs Ollama](https://www.anchorterminal.com/compare/lm-studio-vs-ollama.md)\n- [LocalAI vs MLX LM](https://www.anchorterminal.com/compare/localai-vs-mlx-lm.md)\n- [LocalAI vs Ollama](https://www.anchorterminal.com/compare/localai-vs-ollama.md)\n- [MLX LM vs Open WebUI](https://www.anchorterminal.com/compare/mlx-lm-vs-open-webui.md)\n- [MLX LM vs screenpipe](https://www.anchorterminal.com/compare/mlx-lm-vs-screenpipe.md)\n- [MLX LM vs TextGen](https://www.anchorterminal.com/compare/mlx-lm-vs-text-generation-webui.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- [MLX LM vs Underdog](https://www.anchorterminal.com/compare/mlx-lm-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)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "MLX LM vs Ollama", "url": "" } ], "description": "Ollama scores 56.3 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 3 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust. Both do local inference. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "MLX LM D 52.2", "Ollama C 56.3", "scores" ], "h1": "MLX LM vs Ollama", "image": "https://www.anchorterminal.com/assets/og/compare-mlx-lm-vs-ollama.png", "path": "/compare/mlx-lm-vs-ollama", "published": "2026-10-01", "section": "tools", "title": "MLX LM vs Ollama for AI agents, D 52.2 vs C 56.3 | Anchor Terminal", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-ollama" }, "tokens": { "markdown": 2450, "slim": 680 }, "version": 1 }