{ "data": { "a": { "slug": "lm-studio", "name": "LM Studio", "vendor": "Element Labs, Inc.", "vendorUrl": "https://lmstudio.ai", "kind": "http-api", "category": "local-ai", "summary": "Desktop app and headless daemon from Element Labs for running open-weight models on the owner's machine with llama.cpp and MLX, plus the Splash engine on Apple silicon M3 or newer since 0.4.25.", "url": "https://www.anchorterminal.com/tools/lm-studio", "markdownUrl": "https://www.anchorterminal.com/tools/lm-studio.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lm-studio.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lm-studio.json", "repo": "https://github.com/lmstudio-ai/lmstudio-js", "license": "Proprietary. The app and llmster are free for personal and internal business use under LM Studio's terms (Element Labs, Inc., effective 23 August 2026), with no source published. The `lms` CLI and the TypeScript and Python SDKs are MIT", "transports": [ "http" ], "packages": [ { "registry": "npm", "name": "@lmstudio/sdk" }, { "registry": "pypi", "name": "lmstudio" } ], "auth": "api-key", "authNotes": "No authentication by default. With Require Authentication on (Developer page, Server Settings, LM Studio 0.4.0 or later), every request needs an API token (`sk-lm-` prefix) as `Authorization: Bearer`, or `x-api-key` on the Anthropic-compatible endpoint. Tokens are named, carry permissions picked at creation, are shown once and can be edited or deleted. Calling the owner's mcp.json servers through the API needs authentication on. The server binds to localhost unless Serve on Local Network is on or `lms server start --bind 0.0.0.0` is used.", "pricing": "free", "pricingNotes": "The LM Studio app, llmster and the local server are free for personal and work use with no account or card (free at work since 8 July 2025, and the terms of 23 August 2026 cover internal business use). Element Labs sells cloud model plans for Bionic, its separate agent app, at $20 (Bionic+) and $100 (Pro) a month, which local use of LM Studio doesn't need (checked 2026-10-03).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs, the pricing page or the SDK source (checked 2026-10-03).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 69495, "pypiWeekly": 15195, "asOf": "2026-10-03" }, "docsUrl": "https://lmstudio.ai/docs/developer", "llmsTxt": "https://lmstudio.ai/llms.txt", "capabilities": [ "inference.local", "inference.open-weights", "agent.mcp-client", "embed.text" ], "tags": [ "local", "closed-source", "free", "no-card", "account-free", "openai-compatible", "llms-txt", "typescript", "python", "streaming", "pre-1.0" ], "lastRelease": "2026-09-19", "graded": true, "anchor": { "graded": true, "score": 57.8, "grade": "C", "agentReady": false, "rank": 536, "ranked": true, "rankOf": 842, "categoryRank": 7, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 69, "maintenance": 72, "payments": 60, "reliability": 34, "schema": 64, "security": 59, "transparency": 60 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-03" }, "negative": 0, "verdict": "OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port. Authentication is off by default, so any local process can call the server.", "bestFor": "A machine that serves open models to several agents and tools at once, in whichever API shape each client already speaks, and for headless serving on Linux with llmster.", "strengths": [ "OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port", "llmster, a headless daemon installed with one command, with a documented systemd setup for Linux servers", "Named API tokens with permissions, and API access to MCP servers behind two switches, one of which also needs authentication on", "Stateful chats with `previous_response_id`, `allowed_tools` per MCP integration and typed error objects", "Seven releases in the 90 days to 3 October 2026, each with dated notes" ], "weaknesses": [ "Authentication is off by default, so any local process can call the server", "Closed-source app and daemon with no public CI or test suite", "The Python SDK's last stable release (1.5.0, 22 August 2025) can't send API tokens, and the docs name an environment variable no release reads", "No OpenAPI file, and the llms.txt we read covers the 0.3 app, not the v1 REST API, tokens, llmster or MCP", "1.7k open issues in the public bug tracker" ], "agentNotes": [ "Send `Authorization: Bearer $LM_API_TOKEN` when the owner gives you a token. With Require Authentication on, every request needs it", "Pass `previous_response_id` to /api/v1/chat instead of resending the history, and `store: false` for one-off calls", "List models with `GET /api/v1/models` before naming one. A named model that's downloaded loads just in time", "Install the Python SDK pre-release (1.6.0b1) and pass `api_token` directly. It reads `LMSTUDIO_API_TOKEN`, not the `LM_API_TOKEN` the docs name", "Set `allowed_tools` on every MCP integration. Without it the model sees every tool on the server" ], "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": 57.8 } ], "editorialScores": { "ergonomics": 69, "maintenance": 72, "payments": 60, "reliability": 34, "schema": 64, "security": 59, "transparency": 55 }, "provenanceScore": 64 }, "connect": { "install": "curl -fsSL https://lmstudio.ai/install.sh | bash # llmster, the headless daemon. Windows: irm https://lmstudio.ai/install.ps1 | iex", "http": "curl http://localhost:1234/api/v1/chat \\\n -H \"Authorization: Bearer $LM_API_TOKEN\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"ibm/granite-4-micro\", \"input\": \"Write a short haiku about sunrise.\"}'", "claudeCode": "export ANTHROPIC_BASE_URL=http://localhost:1234\nexport ANTHROPIC_AUTH_TOKEN=lmstudio\nexport CLAUDE_CODE_ATTRIBUTION_HEADER=0\nclaude --model openai/gpt-oss-20b" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/lm-studio" }, "area": "models", "provenance": { "legalEntity": "Element Labs, Inc.", "domain": "lmstudio.ai", "domainRegistered": "2023-05-03", "endpointOnVendorDomain": null, "terms": "https://lmstudio.ai/app-terms", "privacy": "https://lmstudio.ai/app-privacy", "statusPage": "", "changelog": "https://lmstudio.ai/changelog/lmstudio", "securityTxt": "none", "checked": "2026-10-03", "notes": [ "The terms (effective 23 August 2026) and the privacy policy (effective June 2026) name Element Labs, Inc., a Delaware corporation at 251 Little Falls Drive, Wilmington.", "There's no hosted endpoint. The server answers on the owner's machine, at localhost:1234 by default.", "lmstudio.ai/.well-known/security.txt returns a Hub web page rather than a security.txt, and we found no security page or SECURITY.md in the public repositories.", "RDAP for lmstudio.ai gives a registration date of 2023-05-03.", "lmstudio.ai/changelog opens on Bionic, the separate agent app. LM Studio's releases are at /changelog/lmstudio." ], "score": 64 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lm-studio.json", "live": { "slug": "lm-studio", "versions": [ { "registry": "npm", "name": "@lmstudio/sdk", "version": "2.0.0", "seenAt": "2026-10-08T16:19:13.202632377Z" }, { "registry": "pypi", "name": "lmstudio", "version": "1.5.0", "released": "2025-08-22", "seenAt": "2026-10-08T16:19:15.693939311Z" } ], "githubStars": 1785, "npmWeekly": 68608, "pypiWeekly": 15573, "securityTxt": { "url": "https://lmstudio.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:49.288545624Z" }, "llmsTxt": { "url": "https://lmstudio.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:34.665371609Z" }, "domain": { "domain": "lmstudio.ai", "registered": "2023-05-03", "source": "https://rdap.identitydigital.services/rdap/domain/lmstudio.ai", "checkedAt": "2026-10-04T13:08:39.466212979Z" }, "pages": [ { "url": "https://lmstudio.ai/changelog/lmstudio", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:21:42.054043981Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "861d11ad807a" }, { "url": "https://lmstudio.ai/app-privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:21:37.539449608Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "2be7166b913e" }, { "url": "https://lmstudio.ai/app-terms", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:21:40.28202344Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "90222f50fb4b" } ], "updatedAt": "2026-10-08T18:21:42.054043981Z" } }, "answer": "LM Studio scores 57.8 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 4 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust.", "b": { "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" }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Element Labs, Inc.", "b": "Apple Inc.", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary. The app and llmster are free for personal and internal business use under LM Studio's terms (Element Labs, Inc., effective 23 August 2026), with no source published. The `lms` CLI and the TypeScript and Python SDKs are MIT", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-19", "b": "2026-10-01", "name": "Last release" }, { "a": "no date given", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-06-01", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "69k npm/wk, 15k PyPI/wk", "b": "7.3k stars, 140k PyPI/wk", "name": "Popularity" }, { "a": "2.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "LM Studio scores 57.8 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 4 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust.", "question": "Which is better for AI agents, LM Studio or MLX LM?" }, { "answer": "LM Studio needs an API key. MLX LM needs no key.", "question": "Do LM Studio and MLX LM need an API key?" }, { "answer": "No hosted endpoint is listed for LM Studio. No hosted endpoint is listed for MLX LM.", "question": "Can an agent call LM Studio and MLX LM without installing anything?" }, { "answer": "No open-source release is listed for LM Studio. MLX LM is open source (MIT).", "question": "Are LM Studio and MLX LM open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 64 against 37", "Agent ergonomics, 69 against 54", "Security \u0026 auth, 59 against 32", "Maintenance \u0026 community, 72 against 61" ], "also": null, "goodFor": "A machine that serves open models to several agents and tools at once, in whichever API shape each client already speaks, and for headless serving on Linux with llmster.", "slug": "lm-studio", "watchFor": "Authentication is off by default, so any local process can call the server" }, { "aheadOn": [ "Reliability, 66 against 34", "Transparency \u0026 trust, 66 against 60" ], "also": [ "No key needed to call it", "Open source" ], "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 `*`" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio.json", "title": "AnythingLLM vs LM Studio", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio" }, { "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/docker-model-runner-vs-lm-studio.json", "title": "Docker Model Runner vs LM Studio", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-lm-studio" }, { "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/foundry-local-vs-lm-studio.json", "title": "Foundry Local vs LM Studio", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-lm-studio" }, { "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/ghost-core-vs-lm-studio.json", "title": "Core vs LM Studio", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-lm-studio" }, { "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/gpt4all-vs-lm-studio.json", "title": "GPT4All vs LM Studio", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-lm-studio" }, { "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/jan-vs-lm-studio.json", "title": "Jan vs LM Studio", "url": "https://www.anchorterminal.com/compare/jan-vs-lm-studio" }, { "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/khoj-vs-lm-studio.json", "title": "Khoj vs LM Studio", "url": "https://www.anchorterminal.com/compare/khoj-vs-lm-studio" }, { "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/koboldcpp-vs-lm-studio.json", "title": "KoboldCpp vs LM Studio", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-lm-studio" }, { "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/lemonade-vs-lm-studio.json", "title": "Lemonade vs LM Studio", "url": "https://www.anchorterminal.com/compare/lemonade-vs-lm-studio" }, { "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/llama-cpp-vs-lm-studio.json", "title": "llama.cpp vs LM Studio", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-lm-studio" }, { "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/lm-studio-vs-localai.json", "title": "LM Studio vs LocalAI", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-localai" }, { "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/lm-studio-vs-open-webui.json", "title": "LM Studio vs Open WebUI", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/lm-studio-vs-screenpipe.json", "title": "LM Studio vs screenpipe", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/lm-studio-vs-text-generation-webui.json", "title": "LM Studio vs TextGen", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-text-generation-webui" }, { "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/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/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/lm-studio-vs-underdog.json", "title": "LM Studio vs Underdog", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-underdog" }, { "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" } ], "scores": [ { "by": 32, "edge": "mlx-lm", "key": "reliability", "lm-studio": 34, "mlx-lm": 66, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 27, "edge": "lm-studio", "key": "schema", "lm-studio": 64, "mlx-lm": 37, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 15, "edge": "lm-studio", "key": "ergonomics", "lm-studio": 69, "mlx-lm": 54, "name": "Agent ergonomics", "weight": 13 }, { "by": 27, "edge": "lm-studio", "key": "security", "lm-studio": 59, "mlx-lm": 32, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "lm-studio": 60, "mlx-lm": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 11, "edge": "lm-studio", "key": "maintenance", "lm-studio": 72, "mlx-lm": 61, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 6, "edge": "mlx-lm", "key": "transparency", "lm-studio": 60, "mlx-lm": 66, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "LM Studio scores 57.8 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 4 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust. Both do local inference.", "verdicts": { "lm-studio": "OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port. Authentication is off by default, so any local process can call the server.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm", "json": "https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.md", "slim": "https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.min.md" }, "markdown": "LM Studio scores 57.8 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 4 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust. Both do local inference.\n\n- LM Studio: grade C, 57.8/100, rank #536 of 842. Markdown https://www.anchorterminal.com/tools/lm-studio.md · JSON https://www.anchorterminal.com/api/v1/tools/lm-studio.json\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\n## Which one, for what\n\n### LM Studio (C)\n\nGood for: A machine that serves open models to several agents and tools at once, in whichever API shape each client already speaks, and for headless serving on Linux with llmster.\n\nAhead on:\n- Schema \u0026 documentation, 64 against 37\n- Agent ergonomics, 69 against 54\n- Security \u0026 auth, 59 against 32\n- Maintenance \u0026 community, 72 against 61\n\nWatch for: Authentication is off by default, so any local process can call the server\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 34\n- Transparency \u0026 trust, 66 against 60\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: `mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`\n\n\n## Score by category\n\n| Category | Weight | LM Studio | MLX LM | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 34 | 66 | MLX LM +32 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 64 | 37 | LM Studio +27 |\n| Agent ergonomics | 13% (16.2 this run) | 69 | 54 | LM Studio +15 |\n| Security \u0026 auth | 14% (17.5 this run) | 59 | 32 | LM Studio +27 |\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) | 72 | 61 | LM Studio +11 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 60 | 66 | MLX LM +6 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.8 · C** | **52.2 · D** | |\n\n## Facts side by side\n\n| Fact | LM Studio | MLX LM |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Element Labs, Inc. | Apple Inc. |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Proprietary. The app and llmster are free for personal and internal business use under LM Studio's terms (Element Labs, Inc., effective 23 August 2026), with no source published. The `lms` CLI and the TypeScript and Python SDKs are MIT | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-19 | 2026-10-01 |\n| Terms last updated | no date given | no document linked |\n| Privacy policy last updated | 2026-06-01 | no document linked |\n| Customer content may train models | not found in the text | |\n| Terms restrict automated access | not found in the text | |\n| Terms restrict benchmarking | not found in the text | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 69k npm/wk, 15k PyPI/wk | 7.3k stars, 140k PyPI/wk |\n| Agent reviews | 2.5/5 (2) | none |\n\n## Verdicts\n\n**LM Studio.** OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port. Authentication is off by default, so any local process can call the server.\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## Before you call either\n\n### LM Studio\n\n1. Send `Authorization: Bearer $LM_API_TOKEN` when the owner gives you a token. With Require Authentication on, every request needs it\n2. Pass `previous_response_id` to /api/v1/chat instead of resending the history, and `store: false` for one-off calls\n3. List models with `GET /api/v1/models` before naming one. A named model that's downloaded loads just in time\n4. Install the Python SDK pre-release (1.6.0b1) and pass `api_token` directly. It reads `LMSTUDIO_API_TOKEN`, not the `LM_API_TOKEN` the docs name\n5. Set `allowed_tools` on every MCP integration. Without it the model sees every tool on the server\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## Questions\n\n### Which is better for AI agents, LM Studio or MLX LM?\n\nLM Studio scores 57.8 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 4 of 7 scored categories. MLX LM leads on reliability and transparency \u0026 trust.\n\n### Do LM Studio and MLX LM need an API key?\n\nLM Studio needs an API key. MLX LM needs no key.\n\n### Can an agent call LM Studio and MLX LM without installing anything?\n\nNo hosted endpoint is listed for LM Studio. No hosted endpoint is listed for MLX LM.\n\n### Are LM Studio and MLX LM open source?\n\nNo open-source release is listed for LM Studio. MLX LM is open source (MIT).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lm-studio\", \"b\": \"mlx-lm\"}`. From a terminal: `anchor compare lm-studio mlx-lm`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lm-studio.json and https://www.anchorterminal.com/api/v1/tools/mlx-lm.json\n\n## Other comparisons with LM Studio or MLX LM\n\n- [AnythingLLM vs LM Studio](https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio.md)\n- [AnythingLLM vs MLX LM](https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.md)\n- [Docker Model Runner vs LM Studio](https://www.anchorterminal.com/compare/docker-model-runner-vs-lm-studio.md)\n- [Docker Model Runner vs MLX LM](https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm.md)\n- [Foundry Local vs LM Studio](https://www.anchorterminal.com/compare/foundry-local-vs-lm-studio.md)\n- [Foundry Local vs MLX LM](https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm.md)\n- [Core vs LM Studio](https://www.anchorterminal.com/compare/ghost-core-vs-lm-studio.md)\n- [Core vs MLX LM](https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm.md)\n- [GPT4All vs LM Studio](https://www.anchorterminal.com/compare/gpt4all-vs-lm-studio.md)\n- [GPT4All vs MLX LM](https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.md)\n- [Jan vs LM Studio](https://www.anchorterminal.com/compare/jan-vs-lm-studio.md)\n- [Jan vs MLX LM](https://www.anchorterminal.com/compare/jan-vs-mlx-lm.md)\n- [Khoj vs LM Studio](https://www.anchorterminal.com/compare/khoj-vs-lm-studio.md)\n- [Khoj vs MLX LM](https://www.anchorterminal.com/compare/khoj-vs-mlx-lm.md)\n- [KoboldCpp vs LM Studio](https://www.anchorterminal.com/compare/koboldcpp-vs-lm-studio.md)\n- [KoboldCpp vs MLX LM](https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.md)\n- [Lemonade vs LM Studio](https://www.anchorterminal.com/compare/lemonade-vs-lm-studio.md)\n- [Lemonade vs MLX LM](https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.md)\n- [llama.cpp vs LM Studio](https://www.anchorterminal.com/compare/llama-cpp-vs-lm-studio.md)\n- [llama.cpp vs MLX LM](https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm.md)\n- [LM Studio vs LocalAI](https://www.anchorterminal.com/compare/lm-studio-vs-localai.md)\n- [LM Studio vs Ollama](https://www.anchorterminal.com/compare/lm-studio-vs-ollama.md)\n- [LM Studio vs Open WebUI](https://www.anchorterminal.com/compare/lm-studio-vs-open-webui.md)\n- [LM Studio vs screenpipe](https://www.anchorterminal.com/compare/lm-studio-vs-screenpipe.md)\n- [LM Studio vs TextGen](https://www.anchorterminal.com/compare/lm-studio-vs-text-generation-webui.md)\n- [LocalAI vs MLX LM](https://www.anchorterminal.com/compare/localai-vs-mlx-lm.md)\n- [MLX LM vs Ollama](https://www.anchorterminal.com/compare/mlx-lm-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- [LM Studio vs Underdog](https://www.anchorterminal.com/compare/lm-studio-vs-underdog.md)\n- [MLX LM vs Underdog](https://www.anchorterminal.com/compare/mlx-lm-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": "LM Studio vs MLX LM", "url": "" } ], "description": "LM Studio scores 57.8 (C) on agent readiness against MLX LM's 52.2 (D), and leads in 4 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": [ "LM Studio C 57.8", "MLX LM D 52.2", "scores" ], "h1": "LM Studio vs MLX LM", "image": "https://www.anchorterminal.com/assets/og/compare-lm-studio-vs-mlx-lm.png", "path": "/compare/lm-studio-vs-mlx-lm", "published": "2026-10-01", "section": "tools", "title": "LM Studio vs MLX LM for AI agents, C 57.8 vs D 52.2 | Anchor Terminal", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm" }, "tokens": { "markdown": 2550, "slim": 730 }, "version": 1 }