# Ornith-1.5-9B vs Qwen3.8-27B

> Qwen3.8-27B scores 68.6 (B) to Ornith-1.5-9B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.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

Qwen3.8-27B scores 68.6 (B) on agent readiness against Ornith-1.5-9B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-9B leads on reach and agent interface. Both do open-weight models you run yourself.

- 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
- Qwen3.8-27B: grade B, 68.6/100, rank #10 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.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

### 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 90
- Agent interface, 85 against 70

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

### Qwen3.8-27B (B)

Good for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.

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

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | Ornith-1.5-9B | Qwen3.8-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 95 | 90 | Ornith-1.5-9B +5 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3.8-27B +5 |
| Agent interface | 10% (10 this run) | 85 | 70 | Ornith-1.5-9B +15 |
| Documentation and upkeep | 10% (10 this run) | 31 | 36 | Qwen3.8-27B +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **68.6 · B** | |

## Facts side by side

| Fact | Ornith-1.5-9B | Qwen3.8-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Ornith | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-23 | 2026-08-14 |
| 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) | 3/5 (4) |

## Verdicts

**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.

**Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.

## Before you call either

### 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

### Qwen3.8-27B

1. Quickest start is `ollama run qwen3.8`, about 18 GB of files
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented
3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`
5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions
8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts
9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, Ornith-1.5-9B or Qwen3.8-27B?

Qwen3.8-27B scores 68.6 (B) on agent readiness against Ornith-1.5-9B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-9B leads on reach and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ornith-1-5-9b", "b": "qwen3-8-27b"}`. From a terminal: `anchor compare ornith-1-5-9b qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json

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- [Ornith-1.5-9B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.md)
- [Ornith-1.5-9B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.md)
- [Ornith-1.5-9B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b.md)
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- [Ornith-1.5-9B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.md)
- [Ornith-1.5-9B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct.md)
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- [Ornith-1.5-9B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct.md)
- [Ornith-1.5-9B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct.md)
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- [Ornith-1.5-9B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.md)
- [Ornith-1.5-9B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct.md)
- [Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2.md)
- [Ornith-1.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.md)
- [Ornith-1.5-9B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.md)
- [Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.md)
- [Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-8-27b.md)
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- [Qwen3.5-0.8B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-27b.md)
- [Qwen3.5-122B-A10B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b.md)
- [Qwen3.5-2B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b.md)
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- [Qwen3.5-4B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-27b.md)
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- [Qwen3.8-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next.md)
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- [Qwen3.8-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next.md)
- [Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct.md)
- [Qwen3.8-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.md)
- [Qwen3.8-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.8-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct.md)
- [Qwen3.8-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-8b-instruct.md)
- [Qwen3.8-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.8-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.md)
- [Qwen3.8-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b.md)
- [Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.md)

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