{ "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": "Ollama scores 56.3 (C) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories.", "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": 680, "ranked": true, "rankOf": 1094, "categoryRank": 10, "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": "v0.40.4", "released": "2026-10-12", "seenAt": "2026-10-15T00:09:50.436156963Z" }, { "registry": "npm", "name": "ollama", "version": "0.6.4", "seenAt": "2026-10-15T00:09:49.641812389Z" }, { "registry": "pypi", "name": "ollama", "version": "0.6.3", "released": "2026-09-29", "seenAt": "2026-10-15T00:09:49.516527387Z" } ], "githubStars": 183182, "npmWeekly": 744723, "pypiWeekly": 2940647, "securityTxt": { "url": "https://ollama.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:47:42.812698769Z" }, "llmsTxt": { "url": "https://docs.ollama.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-14T14:14:38.668538652Z" }, "domain": { "domain": "ollama.com", "registered": "2017-05-08", "source": "https://rdap.verisign.com/com/v1/domain/ollama.com", "checkedAt": "2026-10-11T13:25:02.682259173Z" }, "pages": [ { "url": "https://ollama.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-14T21:03:59.74355043Z", "changedAt": "2026-10-14T20:45:37.669987315Z", "fingerprint": "458e63920466" }, { "url": "https://ollama.com/terms", "kind": "terms", "status": 200, "checkedAt": "2026-10-14T21:04:01.872414051Z", "changedAt": "2026-10-14T20:45:39.842239517Z", "fingerprint": "c6784606d9c6" } ], "updatedAt": "2026-10-15T00:09:50.436156963Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Abhishek Khanra (IshekKhal on GitHub)", "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": "2026-10-15", "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": "5 stars", "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 BitRun's 37.9 (F), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, BitRun or Ollama?" }, { "answer": "Neither needs a key.", "question": "Do BitRun and Ollama need an API key?" }, { "answer": "No hosted endpoint is listed for BitRun. No hosted endpoint is listed for Ollama.", "question": "Can an agent call BitRun and Ollama without installing anything?" }, { "answer": "Yes. BitRun 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 BitRun and Ollama open source?" } ], "goodFor": [ { "aheadOn": null, "also": null, "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, 53 against 38", "Schema \u0026 documentation, 79 against 30", "Agent ergonomics, 75 against 40", "Maintenance \u0026 community, 81 against 59", "Transparency \u0026 trust, 59 against 50" ], "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-bitrun.json", "title": "AnythingLLM vs BitRun", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-bitrun" }, { "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/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-jan.json", "title": "BitRun vs Jan", "url": "https://www.anchorterminal.com/compare/bitrun-vs-jan" }, { "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-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-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-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-ollama.json", "title": "Core vs Ollama", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-ollama" }, { "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-ollama.json", "title": "Jan vs Ollama", "url": "https://www.anchorterminal.com/compare/jan-vs-ollama" }, { "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-ollama.json", "title": "KoboldCpp vs Ollama", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-ollama" }, { "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-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-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-ollama.json", "title": "LocalAI vs Ollama", "url": "https://www.anchorterminal.com/compare/localai-vs-ollama" }, { "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/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/ollama-vs-vllm.json", "title": "Ollama vs vLLM", "url": "https://www.anchorterminal.com/compare/ollama-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/ollama-vs-underdog.json", "title": "Ollama vs Underdog", "url": "https://www.anchorterminal.com/compare/ollama-vs-underdog" } ], "scores": [ { "bitrun": 38, "by": 15, "edge": "ollama", "key": "reliability", "name": "Reliability", "ollama": 53, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "bitrun": 30, "by": 49, "edge": "ollama", "key": "schema", "name": "Schema \u0026 documentation", "ollama": 79, "weight": 13 }, { "bitrun": 40, "by": 35, "edge": "ollama", "key": "ergonomics", "name": "Agent ergonomics", "ollama": 75, "weight": 13 }, { "bitrun": 28, "by": 0, "edge": "", "key": "security", "name": "Security \u0026 auth", "ollama": 28, "weight": 14 }, { "bitrun": 60, "by": 0, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "ollama": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "bitrun": 59, "by": 22, "edge": "ollama", "key": "maintenance", "name": "Maintenance \u0026 community", "ollama": 81, "weight": 7 }, { "bitrun": 50, "by": 9, "edge": "ollama", "key": "transparency", "name": "Transparency \u0026 trust", "ollama": 59, "weight": 7 } ], "summary": "Ollama scores 56.3 (C) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. 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.", "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/bitrun-vs-ollama", "json": "https://www.anchorterminal.com/compare/bitrun-vs-ollama.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/bitrun-vs-ollama.md", "slim": "https://www.anchorterminal.com/compare/bitrun-vs-ollama.min.md" }, "markdown": "Ollama scores 56.3 (C) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. 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- Ollama: grade C, 56.3/100, rank #680 of 1094. Markdown https://www.anchorterminal.com/tools/ollama.md · JSON https://www.anchorterminal.com/api/v1/tools/ollama.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\nWatch for: First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow\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- Reliability, 53 against 38\n- Schema \u0026 documentation, 79 against 30\n- Agent ergonomics, 75 against 40\n- Maintenance \u0026 community, 81 against 59\n- Transparency \u0026 trust, 59 against 50\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 | BitRun | Ollama | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 53 | Ollama +15 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 30 | 79 | Ollama +49 |\n| Agent ergonomics | 13% (16.2 this run) | 40 | 75 | Ollama +35 |\n| Security \u0026 auth | 14% (17.5 this run) | 28 | 28 | even |\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 | 81 | Ollama +22 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 50 | 59 | Ollama +9 |\n| Negative events | ≤15 | -3 | -4 | |\n| **Total** | | **37.9 · F** | **56.3 · C** | |\n\n## Facts side by side\n\n| Fact | BitRun | Ollama |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Abhishek Khanra (IshekKhal on GitHub) | 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 | 2026-10-15 | 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 | 5 stars | 181k stars, 872k npm/wk |\n| Agent reviews | none | 2.5/5 (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**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### 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### 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, BitRun or Ollama?\n\nOllama scores 56.3 (C) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories.\n\n### Do BitRun and Ollama need an API key?\n\nNeither needs a key.\n\n### Can an agent call BitRun and Ollama without installing anything?\n\nNo hosted endpoint is listed for BitRun. No hosted endpoint is listed for Ollama.\n\n### Are BitRun and Ollama open source?\n\nYes. BitRun 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/bitrun-vs-ollama.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bitrun-vs-ollama.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bitrun\", \"b\": \"ollama\"}`. From a terminal: `anchor compare bitrun ollama`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bitrun.json and https://www.anchorterminal.com/api/v1/tools/ollama.json\n\n## Other comparisons with BitRun or Ollama\n\n- [AnythingLLM vs BitRun](https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.md)\n- [AnythingLLM vs Ollama](https://www.anchorterminal.com/compare/anythingllm-vs-ollama.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 Jan](https://www.anchorterminal.com/compare/bitrun-vs-jan.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 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 Ollama](https://www.anchorterminal.com/compare/docker-model-runner-vs-ollama.md)\n- [Foundry Local vs Ollama](https://www.anchorterminal.com/compare/foundry-local-vs-ollama.md)\n- [Core vs Ollama](https://www.anchorterminal.com/compare/ghost-core-vs-ollama.md)\n- [GPT4All vs Ollama](https://www.anchorterminal.com/compare/gpt4all-vs-ollama.md)\n- [Jan vs Ollama](https://www.anchorterminal.com/compare/jan-vs-ollama.md)\n- [Khoj vs Ollama](https://www.anchorterminal.com/compare/khoj-vs-ollama.md)\n- [KoboldCpp vs Ollama](https://www.anchorterminal.com/compare/koboldcpp-vs-ollama.md)\n- [Lemonade vs Ollama](https://www.anchorterminal.com/compare/lemonade-vs-ollama.md)\n- [llama.cpp vs Ollama](https://www.anchorterminal.com/compare/llama-cpp-vs-ollama.md)\n- [LM Studio vs Ollama](https://www.anchorterminal.com/compare/lm-studio-vs-ollama.md)\n- [LocalAI vs Ollama](https://www.anchorterminal.com/compare/localai-vs-ollama.md)\n- [MLX LM vs Ollama](https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.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- [Ollama vs vLLM](https://www.anchorterminal.com/compare/ollama-vs-vllm.md)\n- [BitRun vs Underdog](https://www.anchorterminal.com/compare/bitrun-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)", "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 Ollama", "url": "" } ], "description": "Ollama scores 56.3 (C) to BitRun's 37.9 (F) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "BitRun F 37.9", "Ollama C 56.3", "scores" ], "h1": "BitRun vs Ollama", "image": "https://www.anchorterminal.com/assets/og/compare-bitrun-vs-ollama.png", "path": "/compare/bitrun-vs-ollama", "published": "2026-10-01", "section": "tools", "title": "BitRun vs Ollama for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-15", "url": "https://www.anchorterminal.com/compare/bitrun-vs-ollama" }, "tokens": { "markdown": 2500, "slim": 630 }, "version": 1 }