# elephant-1 vs mammoth-1 > mammoth-1 and elephant-1 score within a point of each other for decision models, 47.7 and 47.6 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1 - Markdown: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.md (~1,900 tokens) - Slim: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.json (this page as data, same URL with Accept: application/json) - 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: 2026-10-22 mammoth-1 and elephant-1 score within a point of each other on agent readiness, 47.7 (D) and 47.6 (D). elephant-1 leads on reach. Both do decision models. - elephant-1: grade D, 47.6/100, rank #98 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/elephant-1.md · JSON https://www.anchorterminal.com/api/v1/tools/elephant-1.json - mammoth-1: grade D, 47.7/100, rank #97 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/mammoth-1.md · JSON https://www.anchorterminal.com/api/v1/tools/mammoth-1.json - Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md - All 193 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. Ahead on: - Reach, 55 against 40 Watch for: Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support ### mammoth-1 (D) Good for: Agents that want self-hosted routing, triage, classification or judging with probabilities to threshold, many questions per state, on one 80 GB or 96 GB GPU. Ahead on: - Agent interface, 55 against 45 - Documentation and upkeep, 55 against 40 Watch for: Runs as trained only on OneJev's qev runtime at a pinned commit with the repository's patch ## Score by category | Category | Weight | elephant-1 | mammoth-1 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 40 | elephant-1 +15 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 45 | 55 | mammoth-1 +10 | | Documentation and upkeep | 10% (10 this run) | 40 | 55 | mammoth-1 +15 | | Negative events | ≤15 | -2 | 0 | | | **Total** | | **47.6 · D** | **47.7 · D** | | ## Facts side by side | Fact | elephant-1 | mammoth-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 only in the card's metadata (license: other, license_name: mit) with no LICENSE file. Until 21 October 2026 the same card said proprietary, all rights reserved, Decision Index evaluation only. The base, Qwen/Qwen3.5-27B, is Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-21 | 2026-10-21 | | 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) | 2.3/5 (4) | ## 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. **mammoth-1.** A cousin for typed decisions, several questions about one state answered with probabilities from one prefill. It needs about 66 GB in BF16 and a pinned, patched third-party runtime. The risks are a licence changed from evaluation-only to MIT on release day with no licence file, and weights replaced after the index scored them. ## 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 ### mammoth-1 1. Run `bash setup.sh ` in the pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel image on one CUDA GPU. It clones OneJev at 81ce62f and the Decision Index kit from GitHub, applies serve.patch and pip-installs them, so read it first 2. Load through engine.py's DecisionModel (qev DecisionEngine). Plain Transformers or vLLM run the full-attention layers causally and won't give the trained probabilities 3. Send one state and many questions per request, each choice, yes or no (noul) or score, and read a probability per option 4. Keep requests within the trained length, the report trained at up to 16,384 tokens and engine.py refuses requests over the trained limits 5. The index's scores are for revision b9f2f2cacef2ec178ed25c9ef43e987d3422a639. Pin that revision to get the scored weights, or main for the later ones 6. Text only, the vision encoder is the base's, frozen and untrained for decisions ## Questions ### Which is better for AI agents, elephant-1 or mammoth-1? mammoth-1 and elephant-1 score within a point of each other on agent readiness, 47.7 (D) and 47.6 (D). elephant-1 leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "elephant-1", "b": "mammoth-1"}`. From a terminal: `anchor compare elephant-1 mammoth-1` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/elephant-1.json and https://www.anchorterminal.com/api/v1/tools/mammoth-1.json ## Other comparisons with elephant-1 or mammoth-1 - [elephant-1 vs JEV-27B-VL](https://www.anchorterminal.com/compare/elephant-1-vs-jev-27b-vl.md) - [JEV-27B-VL vs mammoth-1](https://www.anchorterminal.com/compare/jev-27b-vl-vs-mammoth-1.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp