# elephant-1 vs joey-1

> joey-1 scores 57.1 (C) to elephant-1's 47.6 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/elephant-1-vs-joey-1
- Markdown: https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.md (~1,900 tokens)
- Slim: https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
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- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-30

joey-1 scores 57.1 (C) on agent readiness against elephant-1's 47.6 (D), and leads in every scored category. Both do decision models.

- elephant-1: grade D, 47.6/100, rank #113 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/elephant-1.md · JSON https://www.anchorterminal.com/api/v1/tools/elephant-1.json
- joey-1: grade C, 57.1/100, rank #78 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/joey-1.md · JSON https://www.anchorterminal.com/api/v1/tools/joey-1.json
- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md
- All 196 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md

## Which one, for what

### elephant-1 (D)

Good for: Agents that want self-hosted ticket routing, guardrail checks or rubric scoring with probabilities to threshold, on one 24 GB GPU, and can accept a patched runtime.

Watch for: Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support

### joey-1 (C)

Good for: Agents that want self-hosted, low-cost ticket routing or guardrail checks with probabilities to threshold, on a GPU with 8 GB, and can accept a patched runtime.

Ahead on:
- Reach, 60 against 55
- Risk, 55 against 50
- Agent interface, 70 against 45
- Documentation and upkeep, 54 against 40

Watch for: Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support


## Score by category

| Category | Weight | elephant-1 | joey-1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 60 | joey-1 +5 |
| Risk | 20% (20 this run) | 50 | 55 | joey-1 +5 |
| Agent interface | 10% (10 this run) | 45 | 70 | joey-1 +25 |
| Documentation and upkeep | 10% (10 this run) | 40 | 54 | joey-1 +14 |
| Negative events | ≤15 | -2 | -2 | |
| **Total** | | **47.6 · D** | **57.1 · C** | |

## Facts side by side

| Fact | elephant-1 | joey-1 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Baseten | Baseten |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT, named only in the card's metadata (license: other, license_name: mit) since 21 October 2026, with no LICENSE file. Until then the card said proprietary, all rights reserved, access for Decision Index evaluation only. The base Qwen/Qwen3.5-9B is Apache-2.0 | MIT, named in the card's metadata (license: mit) since the upload of 27 October 2026, with no LICENSE file. The base Qwen/Qwen3.5-2B is Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-21 | 2026-10-29 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.3/5 (4) | none |

## Verdicts

**elephant-1.** A small cousin for routing, triage and judging, answering several typed questions about one state with probabilities in one prefill. It fits a 24 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are a licence switched from proprietary to MIT on release day with no licence file, and a thin card.

**joey-1.** A very small cousin for routing, triage and guardrail checks, answering several typed questions about one state, with text or images, as probabilities from one prefill. It fits an 8 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are weak results on the Decision Index's new-domain tests and no safety results.

## Before you call either

### elephant-1

1. Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `git clone https://huggingface.co/baselabs/elephant-1 && cd elephant-1 && bash setup.sh /tmp/w`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit
2. Then `qev serve --model . --port 8000` is OneJev's documented server. Base Labs doesn't document it for this checkpoint, so check the answers before relying on them
3. Don't load it with stock vLLM or Transformers. They ignore the block-bidirectional attention in config.json's modeling_overrides and give probabilities the model wasn't trained for
4. Keep each request under 16,384 tokens, the trained length, or the engine refuses it
5. Group the questions about one state in one request, since the state is read once and shared
6. Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-elephant-9b-latest with a Baseten key, free until the end of the launch week

### joey-1

1. Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `hf download baselabs/joey-1 --local-dir joey-1 && bash joey-1/setup.sh`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit
2. Then call `DecisionEngine("baselabs/joey-1", device="cuda:0").decide(SystemOneRequest(state=..., questions=...))` from qev, as the card shows
3. Don't load it with stock vLLM or Transformers. They ignore the block-bidirectional attention in config.json's modeling_overrides and give probabilities the model wasn't trained for
4. Keep each request under 16,384 tokens, the trained length, or the engine refuses it
5. Expect weaker answers on domains unlike its training mix, and threshold on the returned probability before acting
6. Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-joey-2b-latest with a Baseten key

## Questions

### Which is better for AI agents, elephant-1 or joey-1?

joey-1 scores 57.1 (C) on agent readiness against elephant-1's 47.6 (D), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "elephant-1", "b": "joey-1"}`. From a terminal: `anchor compare elephant-1 joey-1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/elephant-1.json and https://www.anchorterminal.com/api/v1/tools/joey-1.json

## Other comparisons with elephant-1 or joey-1

- [elephant-1 vs JEV-27B-VL](https://www.anchorterminal.com/compare/elephant-1-vs-jev-27b-vl.md)
- [elephant-1 vs mammoth-1](https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.md)
- [JEV-27B-VL vs joey-1](https://www.anchorterminal.com/compare/jev-27b-vl-vs-joey-1.md)
- [joey-1 vs mammoth-1](https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1.md)

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