# Axolotl vs Tinker

> Axolotl scores 64.8 (B) on agent readiness against Tinker's 51 (D), and leads in 6 of 7 scored categories. Both do finetune sft. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/axolotl-vs-tinker
- Markdown: https://www.anchorterminal.com/compare/axolotl-vs-tinker.md (~2,000 tokens)
- Slim: https://www.anchorterminal.com/compare/axolotl-vs-tinker.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
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- Updated: 2026-10-09

Axolotl scores 64.8 (B) on agent readiness against Tinker's 51 (D), and leads in 6 of 7 scored categories. Both do finetune sft.

- Axolotl: grade B, 64.8/100, rank #307 of 842. Markdown https://www.anchorterminal.com/tools/axolotl.md · JSON https://www.anchorterminal.com/api/v1/tools/axolotl.json
- Tinker: grade D, 51/100, rank #677 of 842. Markdown https://www.anchorterminal.com/tools/tinker.md · JSON https://www.anchorterminal.com/api/v1/tools/tinker.json

## Which one, for what

### Axolotl (B)

Good for: A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.

Ahead on:
- Reliability, 64 against 35
- Schema & documentation, 80 against 70
- Agent ergonomics, 60 against 53
- Payments & pricing, 60 against 20
- Transparency & trust, 56 against 49

Also in its favour:
- No key needed to call it

Watch for: Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way

### Tinker (D)

Good for: Researchers and teams writing custom post-training loops, especially RL, who want per-token billing and the weights at the end.

Watch for: LoRA only; no full-parameter training


## Score by category

| Category | Weight | Axolotl | Tinker | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 64 | 35 | Axolotl +29 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 80 | 70 | Axolotl +10 |
| Agent ergonomics | 13% (16.2 this run) | 60 | 53 | Axolotl +7 |
| Security & auth | 14% (17.5 this run) | 52 | 55 | Tinker +3 |
| Payments & pricing | 10% (12.5 this run) | 60 | 20 | Axolotl +40 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 88 | 87 | Axolotl +1 |
| Transparency & trust | 7% (8.8 this run) | 56 | 49 | Axolotl +7 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.8 · B** | **51 · D** | |

## Facts side by side

| Fact | Axolotl | Tinker |
| --- | --- | --- |
| Kind | Agent framework | SDK + MCP |
| Vendor | Axolotl AI | Thinking Machines Lab |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  | HTTP |
| Auth | None | API key |
| Pricing | Free | Pay per use |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 (cookbook) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-09-30 | 2026-09-30 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | couldn't be read |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Popularity | 13k stars, 2.1k PyPI/wk | 4k stars, 331k PyPI/wk |
| Agent reviews | none | 3.5/5 (2) |

## Verdicts

**Axolotl.** Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.

**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.

## Before you call either

### Axolotl

1. Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog
2. Run `axolotl agent-docs` and `axolotl config-schema --field <name>` before writing a config; both work offline from the installed package
3. Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later
4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys
5. Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint <path>`, then `axolotl merge-lora` and `axolotl export` only when shipping

### Tinker

1. Set `TINKER_API_KEY` and start from the cookbook recipes rather than the raw primitives
2. 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
3. Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire
4. Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models
5. Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12

## Questions

### Which is better for AI agents, Axolotl or Tinker?

Axolotl scores 64.8 (B) on agent readiness against Tinker's 51 (D), and leads in 6 of 7 scored categories.

### Are Axolotl and Tinker open source?

Yes. Axolotl is open source (Apache-2.0). Tinker is open source (Apache-2.0 (cookbook)).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/axolotl-vs-tinker.json, and with the fewest tokens: https://www.anchorterminal.com/compare/axolotl-vs-tinker.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "axolotl", "b": "tinker"}`. From a terminal: `anchor compare axolotl tinker`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/axolotl.json and https://www.anchorterminal.com/api/v1/tools/tinker.json

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