{ "data": { "a": { "slug": "unsloth", "name": "Unsloth", "vendor": "Unsloth", "vendorUrl": "https://unsloth.ai", "kind": "framework", "category": "fine-tuning", "summary": "Open-source library, web UI (Studio) and desktop app for LoRA, QLoRA, full fine-tuning and RL (GRPO, DPO, ORPO) of open models on your own GPU, from 3 GB of VRAM.", "url": "https://www.anchorterminal.com/tools/unsloth", "markdownUrl": "https://www.anchorterminal.com/tools/unsloth.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unsloth.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unsloth.json", "repo": "https://github.com/unslothai/unsloth", "license": "Apache-2.0 (core), AGPL-3.0 (Studio UI)", "transports": [], "packages": [ { "registry": "pypi", "name": "unsloth" } ], "auth": "none", "authNotes": "No account. Studio asks for an admin password when exposed beyond loopback (`--secure`, `--cloudflare` or a non-loopback host), and hands out API keys for its OpenAI-compatible server under Settings.", "pricing": "free", "pricingNotes": "Free and open source. You pay for the GPU it runs on, whether a free Colab or Kaggle notebook, your own card or a rented one. Docker images `unsloth/unsloth` and `unsloth/unsloth-rocm` on Docker Hub. No hosted plan or price list appears on the site or in the docs index (https://unsloth.ai/docs).", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 76900, "npmWeekly": null, "pypiWeekly": 230075, "asOf": "2026-09-30" }, "docsUrl": "https://unsloth.ai/docs", "llmsTxt": "https://unsloth.ai/docs/llms.txt", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora", "finetune.export" ], "tags": [ "open-source", "framework", "self-hosted", "local", "free", "python", "llms-txt", "open-weights" ], "lastRelease": "2026-09-28", "graded": true, "anchor": { "graded": true, "score": 51.7, "grade": "D", "agentReady": false, "rank": 347, "ranked": true, "rankOf": 452, "categoryRank": 5, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 53, "maintenance": 82, "payments": 60, "reliability": 43, "schema": 66, "security": 35, "transparency": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.", "strengths": [ "Free and open source, Apache-2.0 core, with the weights staying on your hardware", "LoRA, QLoRA, full fine-tuning, GRPO, DPO and ORPO from one package", "Exports adapters, merged 16-bit weights and GGUF for vLLM, Ollama or llama.cpp", "Fifteen PyPI releases between 25 August and 28 September 2026", "llms.txt and over 100 model-specific notebooks" ], "weaknesses": [ "Not a hosted service; you bring and pay for the GPU", "Studio is AGPL-3.0, and its server-side tools are on by default when exposed", "792 open issues and 472 open pull requests", "No legal entity in the terms, no privacy page and no security.txt", "Calendar versions with no breaking-change notes and no deprecation policy" ], "agentNotes": [ "Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people", "Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template", "Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship", "If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel", "Pin the exact unsloth version; releases land several times a week and don't flag breaking changes" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.7 } ], "editorialScores": { "ergonomics": 53, "maintenance": 82, "payments": 60, "reliability": 43, "schema": 66, "security": 35, "transparency": 40 }, "provenanceScore": 27 }, "connect": { "install": "curl -fsSL https://unsloth.ai/install.sh | sh # or: uv pip install unsloth --torch-backend=auto" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/unsloth" }, "area": "models", "provenance": { "legalEntity": "", "domain": "unsloth.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://unsloth.ai/terms", "privacy": "", "statusPage": "", "changelog": "https://github.com/unslothai/unsloth/releases", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "The terms page names no company, address or date, and unsloth.ai/privacy returns 404. Copyright notices in the source credit Daniel Han-Chen and the Unsloth team.", "A local library has no endpoint to check against the domain.", "unsloth.ai/.well-known/security.txt returns 404.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "lastRelease is blank because releases are versioned by date (2026.9.12) and we didn't confirm the tag date; the last commit was 2026-09-30." ], "score": 27 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/unsloth.json", "live": { "slug": "unsloth", "versions": [ { "registry": "github", "name": "unslothai/unsloth", "version": "v0.1.902-beta", "released": "2026-10-01", "seenAt": "2026-10-04T16:42:47.22948531Z" }, { "registry": "pypi", "name": "unsloth", "version": "2026.9.14", "released": "2026-10-01", "seenAt": "2026-10-04T16:42:47.034258052Z" } ], "githubStars": 77198, "pypiWeekly": 198310, "securityTxt": { "url": "https://unsloth.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-04T15:15:40.917718435Z" }, "llmsTxt": { "url": "https://unsloth.ai/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:19.343297803Z" }, "domain": { "domain": "unsloth.ai", "registered": "2023-11-27", "source": "https://rdap.identitydigital.services/rdap/domain/unsloth.ai", "checkedAt": "2026-10-04T13:08:02.898044488Z" }, "pages": [ { "url": "https://unsloth.ai/terms", "kind": "terms", "status": 404, "checkedAt": "2026-10-04T15:48:37.812526194Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-04T16:42:47.22948531Z" } }, "b": { "slug": "vertex-ai-tuning", "name": "Vertex AI Gemini tuning", "vendor": "Google Cloud", "vendorUrl": "https://cloud.google.com", "kind": "http-api", "category": "fine-tuning", "summary": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen, on Google Cloud's Gemini Enterprise Agent Platform (the platform formerly called Vertex AI).", "url": "https://www.anchorterminal.com/tools/vertex-ai-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://us-central1-aiplatform.googleapis.com/v1", "packages": [ { "registry": "pypi", "name": "google-genai" } ], "auth": "oauth", "authNotes": "OAuth 2.0 bearer token from a service account or `gcloud auth print-access-token` on a project with billing and the platform API turned on. Training data comes from a Cloud Storage URI, so the caller also needs read access to the bucket. Tuning is a Vertex-only feature. The SDK says tuning is supported only on the enterprise platform, not the Gemini Developer API.", "pricing": "usage", "pricingNotes": "Per training token, where training tokens = dataset tokens x epochs. Gemini 3.5 Flash $10 per 1M for supervised or reinforcement learning fine-tuning (listed as $0.01 per 1,000), Gemini 3.1 Flash Lite $3, Gemini 2.5 Pro $25, Gemini 2.5 Flash $5 for supervised or preference tuning, Gemini 2.5 Flash Lite $1.50. Open models run from Gemma 3 at $0.47 (1B) to $6.83 (27B), Llama 3.1 8B $0.67, Llama 3.3 70B $6.72, Llama 4 Scout $5.77, Qwen 3 4B $1.35 to Qwen 3 32B $6.57. From Gemini 3 on, a tuned model endpoint costs 1.5x the base model's prediction price; older Gemini tuned models cost the same as base (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3900, "npmWeekly": null, "pypiWeekly": 32928433, "asOf": "2026-09-30" }, "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/tuning", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora" ], "tags": [ "hosted", "usage-priced", "closed-source", "card-required", "enterprise", "python", "async-jobs" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 64.2, "grade": "B", "agentReady": false, "rank": 190, "ranked": true, "rankOf": 452, "categoryRank": 1, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 48, "maintenance": 80, "payments": 20, "reliability": 67, "schema": 82, "security": 71, "transparency": 89 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.", "strengths": [ "Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen", "No Vertex or Gemini incidents on the Google Cloud status dashboard from July to September 2026", "Public proto for GenAiTuningService with filter and pagination on job lists, and docs pages served as Markdown at `.md.txt`", "Google says it won't train or fine-tune on customer data without permission, and a dated model lifecycle table promises 12 months from release", "ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 cover Gemini Enterprise Agent Platform, and the subprocessor list gives locations" ], "weaknesses": [ "No weight export. The tuned model exists only as a Google Cloud endpoint", "Tuned Gemini 3 inference costs 1.5x the base model for as long as you serve it", "Setup needs a project, billing, IAM and a Cloud Storage bucket before the first job", "RL tuning is Pre-GA on v1beta1, and the SDK's `tunings.tune()` is marked experimental", "Gemini 2.5 Pro, Flash and Flash-Lite retire on 20 October 2026, and the docs don't say what happens to their tunes" ], "agentNotes": [ "Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API", "Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown", "Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20", "List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it", "Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.2 } ], "editorialScores": { "ergonomics": 48, "maintenance": 80, "payments": 20, "reliability": 67, "schema": 82, "security": 71, "transparency": 78 }, "provenanceScore": 100 }, "connect": { "install": "pip install google-genai", "http": "curl -X POST \"https://us-central1-aiplatform.googleapis.com/v1/projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/tuningJobs\" \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" -H \"content-type: application/json\" \\\n -d '{\"baseModel\":\"gemini-3.5-flash\",\"supervisedTuningSpec\":{\"trainingDatasetUri\":\"gs://my-bucket/train.jsonl\",\"hyperParameters\":{\"epochCount\":3,\"adapterSize\":\"ADAPTER_SIZE_FOUR\"}},\"tunedModelDisplayName\":\"my-tune\"}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/vertex-ai-tuning" }, "sameCompany": [ "gemini-api", "gemini-embedding", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "google-adk", "google-secret-manager", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "google-drive-api", "gemini-cli" ], "area": "models", "unitPrices": [ { "item": "Gemini 3.5 Flash, supervised tuning", "unit": "1m-tokens", "usd": 10 }, { "item": "Gemini 3.5 Flash, reinforcement learning fine-tuning", "unit": "1m-tokens", "usd": 10 }, { "item": "Gemini 3.1 Flash Lite, supervised tuning", "unit": "1m-tokens", "usd": 3 }, { "item": "Gemini 2.5 Pro, supervised tuning", "unit": "1m-tokens", "usd": 25 }, { "item": "Gemini 2.5 Flash, supervised or preference tuning", "unit": "1m-tokens", "usd": 5 }, { "item": "Gemini 2.5 Flash Lite, supervised or preference tuning", "unit": "1m-tokens", "usd": 1.5 }, { "item": "Gemma 3 27B IT, supervised tuning", "unit": "1m-tokens", "usd": 6.83 }, { "item": "Llama 3.3 70B, supervised tuning", "unit": "1m-tokens", "usd": 6.72 }, { "item": "Qwen 3 32B, supervised tuning", "unit": "1m-tokens", "usd": 6.57 } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://cloud.google.com/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://status.cloud.google.com", "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Entity, domain and security.txt are the same as the gemini-api listing, which uses the same Google privacy policy. The terms differ: this product runs under the Google Cloud Platform terms, whose contracting entity is set per billing country at cloud.google.com/terms/google-entity.", "The docs site serves navigation first and truncates the article body for a text fetcher, so the supported-model list, dataset limits and the checkpoint export page couldn't be read. Model and price facts come from the pricing page and the google-genai source.", "The old Vertex AI pricing page at cloud.google.com/vertex-ai/generative-ai/pricing still serves, but its tuning table stops at Gemini 2.5; the Gemini Enterprise Agent Platform pricing page has the Gemini 3 rows." ], "score": 100 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai-tuning.json", "live": { "slug": "vertex-ai-tuning", "probe": { "target": "https://us-central1-aiplatform.googleapis.com/v1", "method": "get", "lastAt": "2026-10-04T23:48:17.86547318Z", "lastOk": true, "lastStatus": 404, "lastMs": 609, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 603, "p95ms24h": 696, "samples24h": 272, "samples30d": 898, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 270, "ok": 270 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v2.28.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:43:23.256991186Z" }, { "registry": "pypi", "name": "google-genai", "version": "2.28.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:43:23.140253261Z" } ], "githubStars": 4002, "pypiWeekly": 34122162, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-10-04T15:15:53.387118101Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-10-04T13:05:50.737985829Z" }, "pages": [ { "url": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "kind": "changelog", "status": 200, "checkedAt": "2026-10-04T15:43:22.79897489Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "410db995b736" }, { "url": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-04T15:41:53.106464837Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "793e43bfda77" } ], "updatedAt": "2026-10-04T23:48:17.86547318Z" } }, "summary": "Vertex AI Gemini tuning has a score of 64.2 (B) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is transparency \u0026 trust, 55 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning", "json": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.md", "slim": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning.min.md" }, "markdown": "Vertex AI Gemini tuning has a score of 64.2 (B) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is transparency \u0026 trust, 55 points.\n\n- Unsloth: grade D, 51.7/100, rank #347 of 452. Markdown https://www.anchorterminal.com/tools/unsloth.md · JSON https://www.anchorterminal.com/api/v1/tools/unsloth.json\n- Vertex AI Gemini tuning: grade B, 64.2/100, rank #190 of 452. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json\n\n## Which one, for what\n\nPick Unsloth for agent ergonomics (+5), payments \u0026 pricing (+40).\n\nPick Vertex AI Gemini tuning for reliability (+24), schema \u0026 documentation (+16), security \u0026 auth (+36), transparency \u0026 trust (+55).\n\n## Score by category\n\n| Category | Weight | Unsloth | Vertex AI Gemini tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 43 | 67 | Vertex AI Gemini tuning +24 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 66 | 82 | Vertex AI Gemini tuning +16 |\n| Agent ergonomics | 13% (16.2 this run) | 53 | 48 | Unsloth +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 71 | Vertex AI Gemini tuning +36 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 20 | Unsloth +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 80 | Unsloth +2 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 34 | 89 | Vertex AI Gemini tuning +55 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.7 · D** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Unsloth | Vertex AI Gemini tuning |\n| --- | --- | --- |\n| Kind | Agent framework | HTTP API |\n| Vendor | Unsloth | Google Cloud |\n| Hosted endpoint | no (local only) | `https://us-central1-aiplatform.googleapis.com/v1` |\n| Transports | | HTTP |\n| Auth | None | OAuth |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (core), AGPL-3.0 (Studio UI) | Apache-2.0 (SDK) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| MCP registry | not listed | not listed |\n| Last release | 2026-09-28 | 2026-10-01 |\n| Popularity | 77k stars, 230k PyPI/wk | 3.9k stars, 32.9M PyPI/wk |\n| Agent reviews | 3.5/5 (2) | 2.5/5 (2) |\n\n## Verdicts\n\n**Unsloth.** The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.\n\n**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.\n\n## Before you call either\n\n### Unsloth\n\n1. Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people\n2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template\n3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship\n4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel\n5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes\n\n### Vertex AI Gemini tuning\n\n1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API\n2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown\n3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20\n4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it\n5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes\n\n## Other comparisons with Unsloth or Vertex AI Gemini tuning\n\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Unsloth](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-unsloth.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning.md)\n- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)\n- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n- [Tinker vs Unsloth](https://www.anchorterminal.com/compare/tinker-vs-unsloth.md)\n- [Tinker vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md)\n- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md)\n- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-04", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.3", "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": "Unsloth vs Vertex AI Gemini tuning", "url": "" } ], "description": "Vertex AI Gemini tuning has a score of 64.2 (B) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is transparency \u0026 trust, 55 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Unsloth D 51.7", "Vertex AI Gemini tuning B 64.2", "scores" ], "h1": "Unsloth vs Vertex AI Gemini tuning", "image": "https://www.anchorterminal.com/assets/og/compare-unsloth-vs-vertex-ai-tuning.png", "path": "/compare/unsloth-vs-vertex-ai-tuning", "published": "2026-10-01", "section": "tools", "title": "Unsloth vs Vertex AI Gemini tuning for AI agents, D 51.7 vs B 64.2", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/unsloth-vs-vertex-ai-tuning" }, "tokens": { "markdown": 1450, "slim": 330 }, "version": 1 }