# Laguna M.1 vs Ornith-1.5-9B

> Ornith-1.5-9B scores 61 (C) to Laguna M.1's 55.5 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b
- Markdown: https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.md (~5,950 tokens)
- Slim: https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.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: 2026-10-21

Ornith-1.5-9B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json
- Ornith-1.5-9B: grade C, 61/100, rank #44 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Laguna M.1 (C)

Good for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.

Ahead on:
- Risk, 55 against 50
- Documentation and upkeep, 36 against 31

Watch for: The smallest build needs about 167 GB, more than a 128 GB machine

### Ornith-1.5-9B (C)

Good for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required

Ahead on:
- Reach, 95 against 65

Watch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on


## Score by category

| Category | Weight | Laguna M.1 | Ornith-1.5-9B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 95 | Ornith-1.5-9B +30 |
| Risk | 20% (20 this run) | 55 | 50 | Laguna M.1 +5 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 36 | 31 | Laguna M.1 +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.5 · C** | **61 · C** | |

## Facts side by side

| Fact | Laguna M.1 | Ornith-1.5-9B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-14 | 2026-08-23 |
| Terms last updated |  | no document linked |
| Privacy policy last updated |  | 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 | 2.5/5 (4) | 2.3/5 (4) |

## Verdicts

**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.

**Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.

## Before you call either

### Laguna M.1

1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later
2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding
3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped
4. Sampling per the card is temperature 1.0 and top_k 20
5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM
6. It doesn't fit our 128 GB reference machine at any published precision
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

### Ornith-1.5-9B

1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file
2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed
3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention
4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
5. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card
6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp
7. Validate JSON output yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, Laguna M.1 or Ornith-1.5-9B?

Ornith-1.5-9B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-m-1", "b": "ornith-1-5-9b"}`. From a terminal: `anchor compare laguna-m-1 ornith-1-5-9b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json

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