{ "data": { "a": { "slug": "bitrun", "name": "BitRun", "vendor": "Abhishek Khanra (IshekKhal on GitHub)", "vendorUrl": "https://github.com/IshekKhal", "kind": "http-api", "category": "local-ai", "summary": "BitRun is an open-source command-line runner for 1-bit and 1.58-bit ternary language models such as BitNet b1.58, Falcon3 1.58-bit and Bonsai on CPUs, with a model downloader and a local OpenAI-compatible server.", "url": "https://www.anchorterminal.com/tools/bitrun", "markdownUrl": "https://www.anchorterminal.com/tools/bitrun.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/bitrun.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/bitrun.json", "repo": "https://github.com/IshekKhal/bitrun", "license": "MIT", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "No credential. `bitrun serve` starts a patched llama-server on 127.0.0.1 (default port 11435, changed with `--port`) and passes no `--api-key`, so any process on the machine can call it. The bind address is fixed in the code, with no flag to expose it on the network. The README's SDK example sets `api_key` to any string.", "pricing": "free", "pricingNotes": "Free under MIT with nothing to buy. Models are downloaded from Hugging Face under each model's own licence.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the source, the README or the release notes (checked 2026-10-15). Nothing is sold.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 5, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-15" }, "docsUrl": "https://github.com/IshekKhal/bitrun#readme", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "mit", "openai-compatible", "go", "pre-1.0", "macos", "windows", "cli" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 37.9, "grade": "F", "agentReady": false, "rank": 1018, "ranked": true, "rankOf": 1094, "categoryRank": 18, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 40, "maintenance": 59, "payments": 60, "reliability": 38, "schema": 30, "security": 28, "transparency": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-15" }, "negative": -3, "negativeNotes": [ "Misleading claim, 15 October 2026. The README's \"Verified Hardware Benchmarks\", described as physical hardware runs, list package power of 11.70 W, 22.60 W and 8.50 W for every model on each machine, and joules per token from them. These equal the fixed estimates in power.go (2.5 + 1.15 per thread on macOS without battery data, 5.0 + 2.2 per thread on Windows, 4.5 + 2.0 per thread on Linux without RAPL) at the default thread counts of 8, 8 and 2. The README also says Windows power is measured through PPM and that `pull` resumes downloads, and the source does neither (https://github.com/IshekKhal/bitrun/blob/0fec65a/pkg/telemetry/power.go#L137-L163, https://github.com/IshekKhal/bitrun/blob/0fec65a/pkg/models/downloader.go#L57-L60, https://github.com/IshekKhal/bitrun/blob/0fec65a/README.md). Judgement call, -3." ], "verdict": "BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.", "bestFor": "A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.", "strengths": [ "One archive per platform (macOS arm64, Linux amd64, Windows amd64) with llama-server, so no compiler, Python or Docker is needed", "The server binds 127.0.0.1 with no flag to expose it, and works with OpenAI SDKs by changing the base URL", "A catalogue of six ternary and compact models, 79 MB to 2.08 GB, pulled from their Hugging Face repositories by tag", "MIT licence, and no network telemetry found in the Go source. Outbound calls are model downloads on `pull`" ], "weaknesses": [ "First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow", "The README's package power and joules-per-token figures match a fixed per-thread formula in the code, not a counter reading", "No API key option, so any local process can call the server, and model downloads and the installer verify no checksum", "The server documentation covers one route, `/v1/chat/completions`, with no error responses, OpenAPI file or llms.txt", "Context windows of the catalogue models run from 768 to 8,192 tokens, and one server process serves one model" ], "agentNotes": [ "Ask the user to install BitRun and run `bitrun pull \u003ctag\u003e` and `bitrun serve \u003ctag\u003e` first. Then call http://127.0.0.1:11435/v1 with any API key string", "Poll `/health` before the first request. The server loads the model in the background and answers only when ready", "Use a model tag from the catalogue (bitnet-70m, bonsai-1.7b, falcon3-1b, bitnet-2b, falcon3-3b, bonsai-8b) and keep prompts within its context window", "Treat the README's energy figures as estimates, and don't report them as measured power", "Expect small research checkpoints. Check outputs before relying on them for code or facts" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "F", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 37.9 } ], "editorialScores": { "ergonomics": 40, "maintenance": 59, "payments": 60, "reliability": 38, "schema": 30, "security": 28, "transparency": 46 }, "provenanceScore": 53 }, "connect": { "install": "curl -fsSL https://raw.githubusercontent.com/IshekKhal/bitrun/main/install.sh | sh\nbitrun pull falcon3-1b\nbitrun serve falcon3-1b --port 11435", "http": "curl -s http://127.0.0.1:11435/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"falcon3-1b\", \"messages\": [{\"role\": \"user\", \"content\": \"How do 1-bit models conserve memory bandwidth?\"}], \"temperature\": 0.2}'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/bitrun" }, "area": "models", "provenance": { "legalEntity": "Abhishek Khanra (IshekKhal on GitHub)", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/IshekKhal/bitrun/releases", "securityTxt": "none", "checked": "2026-10-15", "notes": [ "The project has no website. The GitHub account IshekKhal (created 17 December 2024) gives the name Abhishek Khanra, and the licence reads Copyright (c) 2026 Abhishek. The Show HN post was made by the HN account ishekofficial.", "No terms, privacy policy, SECURITY.md or security.txt. The repository has no .github folder.", "The software runs on the owner's machine, so there is no vendor endpoint or status page to check." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/bitrun.json" }, "answer": "Jan scores 51.3 (D) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. BitRun leads on maintenance \u0026 community.", "b": { "slug": "jan", "name": "Jan", "vendor": "Menlo Research", "vendorUrl": "https://jan.ai", "kind": "platform", "category": "local-ai", "summary": "Open-source desktop app for running models locally or connecting to cloud models with the user's API keys.", "url": "https://www.anchorterminal.com/tools/jan", "markdownUrl": "https://www.anchorterminal.com/tools/jan.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jan.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jan.json", "repo": "https://github.com/janhq/jan", "license": "Apache-2.0", "transports": [ "http" ], "packages": [], "auth": "api-key", "authNotes": "The Local API Server takes one optional key set in Settings, empty by default, sent as `Authorization: Bearer` or `X-Api-Key`. There are no scopes and no keys per client. It binds to 127.0.0.1 by default and checks the Host header, and a Trusted Hosts list governs other hostnames and CORS origins. In 0.8.4 a 0.0.0.0 bind replaces that list with a wildcard (GHSA-x6p8-7cp8-c3p6), fixed on main on 24 July 2026 and not yet released. `jan serve` takes `--api-key`, empty by default. Cloud provider keys sit in the OS keyring since 0.8.4.", "pricing": "free", "pricingNotes": "Free and Apache-2.0, with no account and nothing to buy. Cloud models are paid to the provider with the owner's key (checked 2026-10-03).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-03).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 44800, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-03" }, "docsUrl": "https://www.jan.ai/docs/desktop/api-server", "openapi": "https://raw.githubusercontent.com/janhq/jan/main/src-tauri/static/openapi.json", "capabilities": [ "inference.local", "inference.open-weights", "agent.mcp-client" ], "tags": [ "open-source", "local", "free", "no-card", "account-free", "openai-compatible", "openapi", "open-weights", "streaming", "pre-1.0" ], "lastRelease": "2026-07-23", "graded": true, "anchor": { "graded": true, "score": 51.3, "grade": "D", "agentReady": false, "rank": 812, "ranked": true, "rankOf": 1094, "categoryRank": 15, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 46, "maintenance": 47, "payments": 60, "reliability": 68, "schema": 56, "security": 43, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-03" }, "negative": -4, "negativeNotes": [ "2026-07-24. GHSA-x6p8-7cp8-c3p6. In 0.8.4, the current release, binding the Local API Server to 0.0.0.0 replaces the Trusted Hosts list with a wildcard, so any Host header is accepted and any Origin reflected with credentials allowed, which with the default empty key lets any web page the owner visits call the server. The fix landed on main on 24 July 2026, no release carries it 71 days later, and the advisory isn't published. It needs a setting the docs flag as risky, -4. https://github.com/janhq/jan/commit/3e1c1e724f696620d89bb4a9cc18a380e0753757" ], "verdict": "Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store. No release since 0.8.4 on 23 July 2026, while a security fix waits on main.", "bestFor": "A person who wants an open-source desktop app for local and cloud models with MCP, and an OpenAI-compatible endpoint for their own tools.", "strengths": [ "Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store", "Product analytics off until the user agrees at first launch, with a toggle in Settings", "MCP tool calls ask for approval by default, and server-side tool execution through the API is off by default", "The local server serves its own OpenAPI 3.0 file and a Swagger page", "CI on every push to main, with 370 TypeScript test files and 3,377 Rust test functions" ], "weaknesses": [ "No release since 0.8.4 on 23 July 2026, while a security fix waits on main", "One optional API key with no scopes, empty by default", "In 0.8.4 a 0.0.0.0 bind ignores Trusted Hosts and reflects any Origin (GHSA-x6p8-7cp8-c3p6)", "New Hugging Face downloads go through Menlo's mirror at apps.jan.ai, which neither privacy page mentions", "No llms.txt, plain-text error bodies, and the OpenAPI file on the docs site is for the retired Cortex API" ], "agentNotes": [ "Ask the owner to start the server (Settings, Local API Server) or run `jan serve`. Nothing listens until then", "Call http://127.0.0.1:1337/v1 for the app and localhost:6767/v1 for `jan serve`. The ports differ", "Read /openapi.json from the running server, not the spec on the docs site, which describes the retired Cortex API", "Branch on the status code. Error bodies are plain text", "Keep the bind at 127.0.0.1 on 0.8.4. Trusted Hosts is ignored on 0.0.0.0 until the next release" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.3 } ], "editorialScores": { "ergonomics": 46, "maintenance": 47, "payments": 60, "reliability": 68, "schema": 56, "security": 43, "transparency": 66 }, "provenanceScore": 69 }, "connect": { "install": "flatpak install flathub ai.jan.Jan # or the macOS, Windows and Linux installers at https://jan.ai", "http": "curl http://127.0.0.1:1337/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer secret-key-123\" \\\n -d '{\"model\": \"YOUR_MODEL_ID\", \"messages\": [{\"role\": \"user\", \"content\": \"Tell me a joke.\"}]}'", "claudeCode": "jan launch claude --model janhq/Jan-code-4b-gguf", "headless": { "command": "jan serve janhq/Jan-code-4b-gguf --detach" } }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/jan" }, "area": "models", "provenance": { "legalEntity": "Menlo Research Pte Ltd", "domain": "jan.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://www.jan.ai/docs/desktop/privacy-policy", "statusPage": "", "changelog": "https://www.jan.ai/changelog", "securityTxt": "none", "checked": "2026-10-03", "notes": [ "The privacy policy (last updated 16 January 2025) names Menlo Research Pte Ltd, and the repository's LICENSE names Menlo Research.", "We found no terms of use on jan.ai or in the docs source.", "jan.ai/.well-known/security.txt returns 404. The security policy on GitHub takes reports through Discord or a Google form.", "RDAP for jan.ai gives a registration date of 2017-12-16.", "There's no hosted endpoint. The server answers on the owner's machine, at 127.0.0.1:1337 by default." ], "score": 69 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jan.json", "live": { "slug": "jan", "versions": [ { "registry": "github", "name": "janhq/jan", "version": "v0.8.6", "released": "2026-10-09", "seenAt": "2026-10-14T23:59:14.127383905Z" } ], "githubStars": 44949, "securityTxt": { "url": "https://jan.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:47:38.947150623Z" }, "domain": { "domain": "jan.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/jan.ai", "checkedAt": "2026-10-11T13:18:39.878065088Z" }, "pages": [ { "url": "https://www.jan.ai/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-14T21:13:59.541809791Z", "changedAt": "2026-10-09T18:51:19.90176075Z", "fingerprint": "0b5f30cb0868" }, { "url": "https://www.jan.ai/docs/desktop/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-14T21:14:01.660519786Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "b042b8460c35" } ], "updatedAt": "2026-10-14T23:59:14.127383905Z" } }, "facts": [ { "a": "HTTP API", "b": "Platform", "name": "Kind" }, { "a": "Abhishek Khanra (IshekKhal on GitHub)", "b": "Menlo Research", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-15", "b": "2026-07-23", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "2025-01-16", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "5 stars", "b": "45k stars", "name": "Popularity" }, { "a": "none", "b": "2/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Jan scores 51.3 (D) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. BitRun leads on maintenance \u0026 community.", "question": "Which is better for AI agents, BitRun or Jan?" }, { "answer": "No hosted endpoint is listed for BitRun. No hosted endpoint is listed for Jan.", "question": "Can an agent call BitRun and Jan without installing anything?" }, { "answer": "Yes. BitRun is open source (MIT). Jan is open source (Apache-2.0).", "question": "Are BitRun and Jan open source?" } ], "goodFor": [ { "aheadOn": [ "Maintenance \u0026 community, 59 against 47" ], "also": [ "No key needed to call it" ], "goodFor": "A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.", "slug": "bitrun", "watchFor": "First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow" }, { "aheadOn": [ "Reliability, 68 against 38", "Schema \u0026 documentation, 56 against 30", "Agent ergonomics, 46 against 40", "Security \u0026 auth, 43 against 28", "Transparency \u0026 trust, 68 against 50" ], "also": null, "goodFor": "A person who wants an open-source desktop app for local and cloud models with MCP, and an OpenAI-compatible endpoint for their own tools.", "slug": "jan", "watchFor": "No release since 0.8.4 on 23 July 2026, while a security fix waits on main" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.json", "title": "AnythingLLM vs BitRun", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-bitrun" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-jan.json", "title": "AnythingLLM vs Jan", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner.json", "title": "BitRun vs Docker Model Runner", "url": "https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-foundry-local.json", "title": "BitRun vs Foundry Local", "url": "https://www.anchorterminal.com/compare/bitrun-vs-foundry-local" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-ghost-core.json", "title": "BitRun vs Core", "url": "https://www.anchorterminal.com/compare/bitrun-vs-ghost-core" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-gpt4all.json", "title": "BitRun vs GPT4All", "url": "https://www.anchorterminal.com/compare/bitrun-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-khoj.json", "title": "BitRun vs Khoj", "url": "https://www.anchorterminal.com/compare/bitrun-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp.json", "title": "BitRun vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-lemonade.json", "title": "BitRun vs Lemonade", "url": "https://www.anchorterminal.com/compare/bitrun-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp.json", "title": "BitRun vs llama.cpp", "url": "https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-lm-studio.json", "title": "BitRun vs LM Studio", "url": "https://www.anchorterminal.com/compare/bitrun-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-localai.json", "title": "BitRun vs LocalAI", "url": "https://www.anchorterminal.com/compare/bitrun-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.json", "title": "BitRun vs MLX LM", "url": "https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-ollama.json", "title": "BitRun vs Ollama", "url": "https://www.anchorterminal.com/compare/bitrun-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-open-webui.json", "title": "BitRun vs Open WebUI", "url": "https://www.anchorterminal.com/compare/bitrun-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-screenpipe.json", "title": "BitRun vs screenpipe", "url": "https://www.anchorterminal.com/compare/bitrun-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-text-generation-webui.json", "title": "BitRun vs TextGen", "url": "https://www.anchorterminal.com/compare/bitrun-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-vllm.json", "title": "BitRun vs vLLM", "url": "https://www.anchorterminal.com/compare/bitrun-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-jan.json", "title": "Docker Model Runner vs Jan", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-jan.json", "title": "Foundry Local vs Jan", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-jan.json", "title": "Core vs Jan", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-jan.json", "title": "GPT4All vs Jan", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-khoj.json", "title": "Jan vs Khoj", "url": "https://www.anchorterminal.com/compare/jan-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-koboldcpp.json", "title": "Jan vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/jan-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-lemonade.json", "title": "Jan vs Lemonade", "url": "https://www.anchorterminal.com/compare/jan-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-llama-cpp.json", "title": "Jan vs llama.cpp", "url": "https://www.anchorterminal.com/compare/jan-vs-llama-cpp" }, { "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-localai.json", "title": "Jan vs LocalAI", "url": "https://www.anchorterminal.com/compare/jan-vs-localai" }, { "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/jan-vs-open-webui.json", "title": "Jan vs Open WebUI", "url": "https://www.anchorterminal.com/compare/jan-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-screenpipe.json", "title": "Jan vs screenpipe", "url": "https://www.anchorterminal.com/compare/jan-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-text-generation-webui.json", "title": "Jan vs TextGen", "url": "https://www.anchorterminal.com/compare/jan-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-vllm.json", "title": "Jan vs vLLM", "url": "https://www.anchorterminal.com/compare/jan-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-underdog.json", "title": "BitRun vs Underdog", "url": "https://www.anchorterminal.com/compare/bitrun-vs-underdog" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-underdog.json", "title": "Jan vs Underdog", "url": "https://www.anchorterminal.com/compare/jan-vs-underdog" } ], "scores": [ { "bitrun": 38, "by": 30, "edge": "jan", "jan": 68, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "bitrun": 30, "by": 26, "edge": "jan", "jan": 56, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "bitrun": 40, "by": 6, "edge": "jan", "jan": 46, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "bitrun": 28, "by": 15, "edge": "jan", "jan": 43, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "bitrun": 60, "by": 0, "edge": "", "jan": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "bitrun": 59, "by": 12, "edge": "bitrun", "jan": 47, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "bitrun": 50, "by": 18, "edge": "jan", "jan": 68, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Jan scores 51.3 (D) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. BitRun leads on maintenance \u0026 community. Both do local inference.", "verdicts": { "bitrun": "BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.", "jan": "Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store. No release since 0.8.4 on 23 July 2026, while a security fix waits on main." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/bitrun-vs-jan", "json": "https://www.anchorterminal.com/compare/bitrun-vs-jan.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/bitrun-vs-jan.md", "slim": "https://www.anchorterminal.com/compare/bitrun-vs-jan.min.md" }, "markdown": "Jan scores 51.3 (D) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. BitRun leads on maintenance \u0026 community. Both do local inference.\n\n- BitRun: grade F, 37.9/100, rank #1018 of 1094. Markdown https://www.anchorterminal.com/tools/bitrun.md · JSON https://www.anchorterminal.com/api/v1/tools/bitrun.json\n- Jan: grade D, 51.3/100, rank #812 of 1094. Markdown https://www.anchorterminal.com/tools/jan.md · JSON https://www.anchorterminal.com/api/v1/tools/jan.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 204 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md\n\n## Which one, for what\n\n### BitRun (F)\n\nGood for: A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.\n\nAhead on:\n- Maintenance \u0026 community, 59 against 47\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow\n\n### Jan (D)\n\nGood for: A person who wants an open-source desktop app for local and cloud models with MCP, and an OpenAI-compatible endpoint for their own tools.\n\nAhead on:\n- Reliability, 68 against 38\n- Schema \u0026 documentation, 56 against 30\n- Agent ergonomics, 46 against 40\n- Security \u0026 auth, 43 against 28\n- Transparency \u0026 trust, 68 against 50\n\nWatch for: No release since 0.8.4 on 23 July 2026, while a security fix waits on main\n\n\n## Score by category\n\n| Category | Weight | BitRun | Jan | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 68 | Jan +30 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 30 | 56 | Jan +26 |\n| Agent ergonomics | 13% (16.2 this run) | 40 | 46 | Jan +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 28 | 43 | Jan +15 |\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) | 59 | 47 | BitRun +12 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 50 | 68 | Jan +18 |\n| Negative events | ≤15 | -3 | -4 | |\n| **Total** | | **37.9 · F** | **51.3 · D** | |\n\n## Facts side by side\n\n| Fact | BitRun | Jan |\n| --- | --- | --- |\n| Kind | HTTP API | Platform |\n| Vendor | Abhishek Khanra (IshekKhal on GitHub) | Menlo Research |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-15 | 2026-07-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | 2025-01-16 |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 5 stars | 45k stars |\n| Agent reviews | none | 2/5 (2) |\n\n## Verdicts\n\n**BitRun.** BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.\n\n**Jan.** Apache-2.0, with installers for macOS, Windows and Linux plus Flathub and the Microsoft Store. No release since 0.8.4 on 23 July 2026, while a security fix waits on main.\n\n## Before you call either\n\n### BitRun\n\n1. Ask the user to install BitRun and run `bitrun pull \u003ctag\u003e` and `bitrun serve \u003ctag\u003e` first. Then call http://127.0.0.1:11435/v1 with any API key string\n2. Poll `/health` before the first request. The server loads the model in the background and answers only when ready\n3. Use a model tag from the catalogue (bitnet-70m, bonsai-1.7b, falcon3-1b, bitnet-2b, falcon3-3b, bonsai-8b) and keep prompts within its context window\n4. Treat the README's energy figures as estimates, and don't report them as measured power\n5. Expect small research checkpoints. Check outputs before relying on them for code or facts\n\n### Jan\n\n1. Ask the owner to start the server (Settings, Local API Server) or run `jan serve`. Nothing listens until then\n2. Call http://127.0.0.1:1337/v1 for the app and localhost:6767/v1 for `jan serve`. The ports differ\n3. Read /openapi.json from the running server, not the spec on the docs site, which describes the retired Cortex API\n4. Branch on the status code. Error bodies are plain text\n5. Keep the bind at 127.0.0.1 on 0.8.4. Trusted Hosts is ignored on 0.0.0.0 until the next release\n\n## Questions\n\n### Which is better for AI agents, BitRun or Jan?\n\nJan scores 51.3 (D) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. BitRun leads on maintenance \u0026 community.\n\n### Can an agent call BitRun and Jan without installing anything?\n\nNo hosted endpoint is listed for BitRun. No hosted endpoint is listed for Jan.\n\n### Are BitRun and Jan open source?\n\nYes. BitRun is open source (MIT). Jan is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bitrun-vs-jan.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bitrun-vs-jan.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bitrun\", \"b\": \"jan\"}`. From a terminal: `anchor compare bitrun jan`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bitrun.json and https://www.anchorterminal.com/api/v1/tools/jan.json\n\n## Other comparisons with BitRun or Jan\n\n- [AnythingLLM vs BitRun](https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.md)\n- [AnythingLLM vs Jan](https://www.anchorterminal.com/compare/anythingllm-vs-jan.md)\n- [BitRun vs Docker Model Runner](https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner.md)\n- [BitRun vs Foundry Local](https://www.anchorterminal.com/compare/bitrun-vs-foundry-local.md)\n- [BitRun vs Core](https://www.anchorterminal.com/compare/bitrun-vs-ghost-core.md)\n- [BitRun vs GPT4All](https://www.anchorterminal.com/compare/bitrun-vs-gpt4all.md)\n- [BitRun vs Khoj](https://www.anchorterminal.com/compare/bitrun-vs-khoj.md)\n- [BitRun vs KoboldCpp](https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp.md)\n- [BitRun vs Lemonade](https://www.anchorterminal.com/compare/bitrun-vs-lemonade.md)\n- [BitRun vs llama.cpp](https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp.md)\n- [BitRun vs LM Studio](https://www.anchorterminal.com/compare/bitrun-vs-lm-studio.md)\n- [BitRun vs LocalAI](https://www.anchorterminal.com/compare/bitrun-vs-localai.md)\n- [BitRun vs MLX LM](https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.md)\n- [BitRun vs Ollama](https://www.anchorterminal.com/compare/bitrun-vs-ollama.md)\n- [BitRun vs Open WebUI](https://www.anchorterminal.com/compare/bitrun-vs-open-webui.md)\n- [BitRun vs screenpipe](https://www.anchorterminal.com/compare/bitrun-vs-screenpipe.md)\n- [BitRun vs TextGen](https://www.anchorterminal.com/compare/bitrun-vs-text-generation-webui.md)\n- [BitRun vs vLLM](https://www.anchorterminal.com/compare/bitrun-vs-vllm.md)\n- [Docker Model Runner vs Jan](https://www.anchorterminal.com/compare/docker-model-runner-vs-jan.md)\n- [Foundry Local vs Jan](https://www.anchorterminal.com/compare/foundry-local-vs-jan.md)\n- [Core vs Jan](https://www.anchorterminal.com/compare/ghost-core-vs-jan.md)\n- [GPT4All vs Jan](https://www.anchorterminal.com/compare/gpt4all-vs-jan.md)\n- [Jan vs Khoj](https://www.anchorterminal.com/compare/jan-vs-khoj.md)\n- [Jan vs KoboldCpp](https://www.anchorterminal.com/compare/jan-vs-koboldcpp.md)\n- [Jan vs Lemonade](https://www.anchorterminal.com/compare/jan-vs-lemonade.md)\n- [Jan vs llama.cpp](https://www.anchorterminal.com/compare/jan-vs-llama-cpp.md)\n- [Jan vs LM Studio](https://www.anchorterminal.com/compare/jan-vs-lm-studio.md)\n- [Jan vs LocalAI](https://www.anchorterminal.com/compare/jan-vs-localai.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- [Jan vs Open WebUI](https://www.anchorterminal.com/compare/jan-vs-open-webui.md)\n- [Jan vs screenpipe](https://www.anchorterminal.com/compare/jan-vs-screenpipe.md)\n- [Jan vs TextGen](https://www.anchorterminal.com/compare/jan-vs-text-generation-webui.md)\n- [Jan vs vLLM](https://www.anchorterminal.com/compare/jan-vs-vllm.md)\n- [BitRun vs Underdog](https://www.anchorterminal.com/compare/bitrun-vs-underdog.md)\n- [Jan vs Underdog](https://www.anchorterminal.com/compare/jan-vs-underdog.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-15", "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", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "BitRun vs Jan", "url": "" } ], "description": "Jan scores 51.3 (D) to BitRun's 37.9 (F) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "BitRun F 37.9", "Jan D 51.3", "scores" ], "h1": "BitRun vs Jan", "image": "https://www.anchorterminal.com/assets/og/compare-bitrun-vs-jan.png", "path": "/compare/bitrun-vs-jan", "published": "2026-10-01", "section": "tools", "title": "BitRun vs Jan for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-15", "url": "https://www.anchorterminal.com/compare/bitrun-vs-jan" }, "tokens": { "markdown": 2400, "slim": 530 }, "version": 1 }