{ "data": { "a": { "slug": "tinker", "name": "Tinker", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai/tinker/", "kind": "sdk", "category": "fine-tuning", "summary": "Thinking Machines Lab's API for model training.", "url": "https://www.anchorterminal.com/tools/tinker", "markdownUrl": "https://www.anchorterminal.com/tools/tinker.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/tinker.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/tinker.json", "repo": "https://github.com/thinking-machines-lab/tinker-cookbook", "license": "Apache-2.0 (cookbook)", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "tinker" }, { "registry": "pypi", "name": "tinker-cookbook" } ], "auth": "api-key", "authNotes": "API key from the Tinker console, exported as `TINKER_API_KEY`, or `tinker auth login`. Sign-up is at auth.thinkingmachines.ai and the quickstart says to add payment details in Billing before training.", "pricing": "usage", "pricingNotes": "Per 1M tokens, split into prefill, cached prefill (20 per cent of prefill), sample and train. Qwen3.8-27B $1.86 prefill, $5.595 sample, $4.103 train; Qwen3.5-9B $0.66, $1.995, $1.463; GPT-OSS-20B $0.18, $0.45, $0.396; DeepSeek-V3.1 $1.695, $4.215, $3.718; Inkling $1.87, $4.68, $5.61; Inkling-Small $0.58, $1.44, $1.73. MoE models are priced by active parameters. Checkpoint storage $0.10 per GB-month. Prices rose on 2026-07-17 for standard-context models. No free credits are mentioned (https://tinker-docs.thinkingmachines.ai/tinker/models/).", "priceSummary": "Pay per use", "where": "local", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 4000, "npmWeekly": null, "pypiWeekly": 330895, "asOf": "2026-09-30" }, "docsUrl": "https://tinker-docs.thinkingmachines.ai", "llmsTxt": "https://tinker-docs.thinkingmachines.ai/llms.txt", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "python", "open-source" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 51.2, "grade": "D", "agentReady": false, "rank": 354, "ranked": true, "rankOf": 452, "categoryRank": 6, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 53, "maintenance": 87, "payments": 20, "reliability": 35, "schema": 70, "security": 55, "transparency": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.", "strengths": [ "Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation", "Checkpoints download and merge into Hugging Face safetensors, so the weights can leave", "Per-token billing with machine-readable prices in models.json", "Ten SDK releases in September 2026 and a dated changelog that names removals", "Audit log through the SDK for admins, and SDK retries with stable request IDs" ], "weaknesses": [ "LoRA only; no full-parameter training", "Python SDK only, with no REST reference or OpenAPI", "No terms of service, status page or SLA found", "The privacy notice (August 2025) doesn't cover training data or weights", "Standard-context prices rose on 2026-07-17, and there's no free tier" ], "agentNotes": [ "Set `TINKER_API_KEY` and start from the cookbook recipes rather than the raw primitives", "Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs", "Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire", "Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models", "Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12" ], "metrics": { "kind": "remote", "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": 51.2 } ], "editorialScores": { "ergonomics": 53, "maintenance": 87, "payments": 20, "reliability": 35, "schema": 70, "security": 55, "transparency": 30 }, "provenanceScore": 71 }, "connect": { "install": "uv pip install tinker tinker-cookbook # then export TINKER_API_KEY=..." }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/tinker" }, "area": "models", "unitPrices": [ { "item": "Qwen3.8-27B, training", "unit": "1m-tokens", "usd": 4.103 }, { "item": "Qwen3.8-27B, sampling", "unit": "1m-tokens", "usd": 5.595 }, { "item": "Qwen3.8-27B, prefill", "unit": "1m-tokens", "usd": 1.86, "note": "Cached prefill $0.372" }, { "item": "Qwen3.5-9B, training", "unit": "1m-tokens", "usd": 1.463 }, { "item": "GPT-OSS-20B, training", "unit": "1m-tokens", "usd": 0.396 }, { "item": "DeepSeek-V3.1, training", "unit": "1m-tokens", "usd": 3.718 }, { "item": "Inkling, training", "unit": "1m-tokens", "usd": 5.61 }, { "item": "Inkling-Small, training", "unit": "1m-tokens", "usd": 1.73 }, { "item": "Checkpoint storage", "unit": "gb-month", "usd": 0.1 } ], "provenance": { "legalEntity": "Thinking Machines Labs, Inc.", "domain": "thinkingmachines.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://thinkingmachines.ai/privacy/", "statusPage": "", "changelog": "https://tinker-docs.thinkingmachines.ai/changelog/", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "The privacy notice (2025-08-18) names Thinking Machines Labs, Inc. as data controller and gives no address. We found no terms of service page on thinkingmachines.ai or the docs; the support page links only to email, Discord and GitHub.", "The service is reached through the SDK's ServiceClient with an undocumented default base URL, so there's no endpoint to check against the domain.", "security.txt at thinkingmachines.ai lists security-reports@thinkingmachines.ai and expires 2029-07-13.", "No status page was found.", "The .ai registry's RDAP server refused our requests, so the registration date is blank." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/tinker.json", "live": { "slug": "tinker", "versions": [ { "registry": "github", "name": "thinking-machines-lab/tinker-cookbook", "version": "v0.5.7", "released": "2026-09-03", "seenAt": "2026-10-04T16:41:59.205166565Z" }, { "registry": "pypi", "name": "tinker", "version": "0.32.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:41:57.121115125Z" }, { "registry": "pypi", "name": "tinker-cookbook", "version": "0.5.7", "released": "2026-09-03", "seenAt": "2026-10-04T16:41:57.304569133Z" } ], "githubStars": 4172, "pypiWeekly": 384718, "securityTxt": { "url": "https://thinkingmachines.ai/.well-known/security.txt", "state": "valid", "expires": "2029-07-13T07:00:00.000Z", "checkedAt": "2026-10-04T15:15:51.616624235Z" }, "llmsTxt": { "url": "https://tinker-docs.thinkingmachines.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:17.9955218Z" }, "domain": { "domain": "thinkingmachines.ai", "registered": "2024-07-09", "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai", "checkedAt": "2026-10-04T13:07:26.919974206Z" }, "pages": [ { "url": "https://tinker-docs.thinkingmachines.ai/changelog/", "kind": "changelog", "status": 200, "checkedAt": "2026-10-04T15:48:28.96257516Z", "changedAt": "2026-10-03T15:36:25.857913905Z", "fingerprint": "18caaae94eb7" }, { "url": "https://thinkingmachines.ai/privacy/", "kind": "privacy", "status": 404, "checkedAt": "2026-10-04T15:48:27.643400729Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-04T16:41:59.205166565Z" } }, "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": "2026-10-04T23:32:56.151710289Z", "lastOk": true, "lastStatus": 404, "lastMs": 591, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 603, "p95ms24h": 696, "samples24h": 272, "samples30d": 895, "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": 267, "ok": 267 } ] }, "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": "2026-10-04T23:32:56.151710289Z" } }, "summary": "Vertex AI Gemini tuning has a score of 64.2 (B) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is transparency \u0026 trust, 38 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning", "json": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.md", "slim": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning.min.md" }, "markdown": "Vertex AI Gemini tuning has a score of 64.2 (B) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is transparency \u0026 trust, 38 points.\n\n- Tinker: grade D, 51.2/100, rank #354 of 452. Markdown https://www.anchorterminal.com/tools/tinker.md · JSON https://www.anchorterminal.com/api/v1/tools/tinker.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 Tinker for agent ergonomics (+5), maintenance \u0026 community (+7).\n\nPick Vertex AI Gemini tuning for reliability (+32), schema \u0026 documentation (+12), security \u0026 auth (+16), transparency \u0026 trust (+38).\n\n## Score by category\n\n| Category | Weight | Tinker | Vertex AI Gemini tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 35 | 67 | Vertex AI Gemini tuning +32 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 70 | 82 | Vertex AI Gemini tuning +12 |\n| Agent ergonomics | 13% (16.2 this run) | 53 | 48 | Tinker +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 71 | Vertex AI Gemini tuning +16 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 87 | 80 | Tinker +7 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 51 | 89 | Vertex AI Gemini tuning +38 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.2 · D** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | Tinker | Vertex AI Gemini tuning |\n| --- | --- | --- |\n| Kind | SDK + MCP | HTTP API |\n| Vendor | Thinking Machines Lab | Google Cloud |\n| Hosted endpoint | no (local only) | `https://us-central1-aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (cookbook) | 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 | 2026-09-30 | 2026-10-01 |\n| Popularity | 4k stars, 331k 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**Tinker.** Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.\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### Tinker\n\n1. Set `TINKER_API_KEY` and start from the cookbook recipes rather than the raw primitives\n2. Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs\n3. Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire\n4. Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models\n5. Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12\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 Tinker or Vertex AI Gemini tuning\n\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Tinker](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-tinker.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 Tinker](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.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 Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)\n- [Tinker vs Unsloth](https://www.anchorterminal.com/compare/tinker-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- [Unsloth vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/unsloth-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": "Tinker vs Vertex AI Gemini tuning", "url": "" } ], "description": "Vertex AI Gemini tuning has a score of 64.2 (B) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is transparency \u0026 trust, 38 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Tinker D 51.2", "Vertex AI Gemini tuning B 64.2", "scores" ], "h1": "Tinker vs Vertex AI Gemini tuning", "image": "https://www.anchorterminal.com/assets/og/compare-tinker-vs-vertex-ai-tuning.png", "path": "/compare/tinker-vs-vertex-ai-tuning", "published": "2026-10-01", "section": "tools", "title": "Tinker vs Vertex AI Gemini tuning for AI agents, D 51.2 vs B 64.2", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/tinker-vs-vertex-ai-tuning" }, "tokens": { "markdown": 1500, "slim": 330 }, "version": 1 }