{ "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": "together-fine-tuning", "name": "Together AI Fine-tuning", "vendor": "Together AI", "vendorUrl": "https://www.together.ai", "kind": "http-api", "category": "fine-tuning", "summary": "Managed LoRA and full fine-tuning, supervised or DPO, on about 30 open models from Qwen3.5 0.8B to Kimi K2.7, billed per training token with a $4 minimum.", "url": "https://www.anchorterminal.com/tools/together-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json", "repo": "https://github.com/togethercomputer/together-py", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.together.ai/v1", "packages": [ { "registry": "pypi", "name": "together" }, { "registry": "npm", "name": "together-ai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with the key from the console, read from `TOGETHER_API_KEY` by the SDKs and the `tg` CLI. One key covers files, fine-tuning jobs, downloads and endpoints.", "pricing": "usage", "pricingNotes": "Per training token, where tokens = epochs x training tokens + evaluations x validation tokens. LoRA SFT from $0.34 per 1M (Llama 3.1 8B, Qwen3.5 9B) through $1.05 (Qwen3.8 27B), $2.03 (Llama 3.3 70B), $2.50 (gpt-oss-120b), $7 (DeepSeek V3.1) and $15 (Kimi K2.6) to $40 (GLM-5.2). DPO is 2.5x the SFT rate ($0.84 for Llama 3.1 8B, $37.50 for Kimi K2.6). Full fine-tuning $0.38 (8B and 9B models) to $2.24 (Llama 3.3 70B). Minimum $4 a job, rising to $6 for gpt-oss-120b, $20 for DeepSeek V3.1 and $60 for Kimi K2.6. Hosting the result needs a dedicated endpoint; the pricing page lists dedicated endpoint GPUs at $5.49 an hour for an H100 and $8.99 for a B200, with H200 and B300 by quote. No free trial; access needs a $5 prepaid credit purchase (https://www.together.ai/pricing, https://docs.together.ai/docs/billing-credits).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 10, "npmWeekly": 117852, "pypiWeekly": 369054, "asOf": "2026-09-30" }, "docsUrl": "https://docs.together.ai/docs/fine-tuning/overview", "llmsTxt": "https://docs.together.ai/llms.txt", "openapi": "https://docs.together.ai/openapi.yaml", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "python", "typescript", "async-jobs" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 54.9, "grade": "C", "agentReady": false, "rank": 319, "ranked": true, "rankOf": 452, "categoryRank": 4, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 42, "maintenance": 80, "payments": 20, "reliability": 55, "schema": 78, "security": 50, "transparency": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.", "strengths": [ "31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA", "GET /v1/finetune/download returns merged weights or the adapter, at any saved checkpoint", "POST /v1/fine-tunes/estimate-price quotes a job before it runs", "Project-scoped API keys with expiry dates from 1 hour", "Python and TypeScript SDKs, an OpenAPI file and llms.txt" ], "weaknesses": [ "Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour", "No free trial, a $5 prepaid purchase before the first call, and job minimums up to $60", "The status page covers serverless models only, and no fine-tuning rate limits are published", "No pagination on the job list and no documented error responses for fine-tuning calls", "No read-only project role and no audit log found" ], "agentNotes": [ "Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge", "Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29", "Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first", "Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream", "Tear down the dedicated endpoint once evaluation ends, since it bills while idle" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.9 } ], "editorialScores": { "ergonomics": 42, "maintenance": 80, "payments": 20, "reliability": 55, "schema": 78, "security": 50, "transparency": 55 }, "provenanceScore": 85 }, "connect": { "install": "pip install together # or: npm i together-ai", "http": "curl https://api.together.ai/v1/fine-tunes \\\n -H \"Authorization: Bearer $TOGETHER_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"Qwen/Qwen3.5-9B\",\"training_file\":\"file-abc123\",\"n_epochs\":3,\"training_type\":{\"type\":\"Lora\",\"lora_r\":16,\"lora_alpha\":32},\"training_method\":{\"method\":\"sft\"},\"suffix\":\"my-run\"}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/together-fine-tuning" }, "area": "models", "unitPrices": [ { "item": "LoRA SFT, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.34, "note": "Same rate for Qwen3.5 9B. $4 minimum" }, { "item": "LoRA DPO, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.84 }, { "item": "Full SFT, Llama 3.1 8B", "unit": "1m-tokens", "usd": 0.38 }, { "item": "LoRA SFT, Qwen3.8 27B", "unit": "1m-tokens", "usd": 1.05 }, { "item": "LoRA SFT, Llama 3.3 70B", "unit": "1m-tokens", "usd": 2.03, "note": "Full SFT $2.24" }, { "item": "LoRA SFT, gpt-oss-120b", "unit": "1m-tokens", "usd": 2.5, "note": "$6 minimum" }, { "item": "LoRA SFT, DeepSeek V3.1", "unit": "1m-tokens", "usd": 7, "note": "$20 minimum" }, { "item": "LoRA SFT, Kimi K2.6", "unit": "1m-tokens", "usd": 15, "note": "$60 minimum" }, { "item": "H100 on demand", "unit": "gpu-hour", "usd": 3.99 }, { "item": "H200 on demand", "unit": "gpu-hour", "usd": 5.99 }, { "item": "B200 on demand", "unit": "gpu-hour", "usd": 8.19 } ], "provenance": { "legalEntity": "Together Computer, Inc.", "domain": "together.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://www.together.ai/terms-of-service", "privacy": "https://www.together.ai/privacy", "statusPage": "https://status.together.ai", "changelog": "https://docs.together.ai/docs/changelog", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "The terms (2026-05-19) name Together Computer, Inc., a Delaware corporation. The privacy policy (2025-12-17) says data isn't used to train models without opt-in.", "security.txt points Contact and Policy at hackerone.com/together_ai and has no Expires field.", "The status page monitors serverless inference models only; there's no fine-tuning component.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "The MCP registry has a third-party io.usefulapi/together-ai server that wraps fine-tunes; Together doesn't publish one." ], "score": 85 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/together-fine-tuning.json", "live": { "slug": "together-fine-tuning", "probe": { "target": "https://api.together.ai/v1", "method": "get", "lastAt": "2026-10-04T23:32:55.481697836Z", "lastOk": true, "lastStatus": 404, "lastMs": 198, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 223, "p95ms24h": 550, "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 } ] }, "vendorStatus": { "page": "https://status.together.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-04T21:40:31.633872227Z" }, "versions": [ { "registry": "github", "name": "togethercomputer/together-py", "version": "v2.39.0", "released": "2026-10-01", "seenAt": "2026-10-04T16:42:06.500144656Z" }, { "registry": "npm", "name": "together-ai", "version": "0.57.0", "seenAt": "2026-10-04T16:42:05.689430855Z" }, { "registry": "pypi", "name": "together", "version": "2.39.0", "released": "2026-10-01", "seenAt": "2026-10-04T16:42:05.492937716Z" } ], "githubStars": 10, "npmWeekly": 120148, "pypiWeekly": 377345, "securityTxt": { "url": "https://together.ai/.well-known/security.txt", "state": "valid", "checkedAt": "2026-10-04T15:16:01.636231106Z" }, "llmsTxt": { "url": "https://docs.together.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:18.165758207Z" }, "domain": { "domain": "together.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/together.ai", "checkedAt": "2026-10-04T13:04:15.476837291Z" }, "pages": [ { "url": "https://docs.together.ai/docs/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-04T15:44:08.24409663Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "3820028b3e12" }, { "url": "https://www.together.ai/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-10-04T15:52:28.018092824Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "6569e636eb45" }, { "url": "https://www.together.ai/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-04T15:52:30.046079919Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "1ae1e2a8ef61" }, { "url": "https://www.together.ai/terms-of-service", "kind": "terms", "status": 304, "checkedAt": "2026-10-04T15:52:32.055910677Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "c30ddbd90e92" } ], "updatedAt": "2026-10-04T23:32:55.481697836Z" } }, "summary": "Together AI Fine-tuning has a score of 54.9 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is reliability, 20 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning", "json": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.min.md" }, "markdown": "Together AI Fine-tuning has a score of 54.9 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is reliability, 20 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- Together AI Fine-tuning: grade C, 54.9/100, rank #319 of 452. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json\n\n## Which one, for what\n\nPick Tinker for agent ergonomics (+11), security \u0026 auth (+5), maintenance \u0026 community (+7).\n\nPick Together AI Fine-tuning for reliability (+20), schema \u0026 documentation (+8), transparency \u0026 trust (+19).\n\n## Score by category\n\n| Category | Weight | Tinker | Together AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 35 | 55 | Together AI Fine-tuning +20 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 70 | 78 | Together AI Fine-tuning +8 |\n| Agent ergonomics | 13% (16.2 this run) | 53 | 42 | Tinker +11 |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 50 | Tinker +5 |\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 | 70 | Together AI Fine-tuning +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.2 · D** | **54.9 · C** | |\n\n## Facts side by side\n\n| Fact | Tinker | Together AI Fine-tuning |\n| --- | --- | --- |\n| Kind | SDK + MCP | HTTP API |\n| Vendor | Thinking Machines Lab | Together AI |\n| Hosted endpoint | no (local only) | `https://api.together.ai/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (cookbook) | Apache-2.0 (SDKs) |\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 | yes |\n| MCP registry | not listed | not listed |\n| Last release | 2026-09-30 | 2026-09-30 |\n| Popularity | 4k stars, 331k PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | 3/5 (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**Together AI Fine-tuning.** 31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.\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### Together AI Fine-tuning\n\n1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge\n2. Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29\n3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first\n4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream\n5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle\n\n## Other comparisons with Tinker or Together AI Fine-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 Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-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 Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-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": "Tinker vs Together AI Fine-tuning", "url": "" } ], "description": "Together AI Fine-tuning has a score of 54.9 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is reliability, 20 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Tinker D 51.2", "Together AI Fine-tuning C 54.9", "scores" ], "h1": "Tinker vs Together AI Fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-tinker-vs-together-fine-tuning.png", "path": "/compare/tinker-vs-together-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Tinker vs Together AI Fine-tuning for AI agents, D 51.2 vs C 54.9", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning" }, "tokens": { "markdown": 1450, "slim": 330 }, "version": 1 }