# Nanbeige4.3-33B vs Ornith-1.5-9B

> Nanbeige4.3-33B and Ornith-1.5-9B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b
- Markdown: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.md (~6,600 tokens)
- Slim: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-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: 2026-10-25

Nanbeige4.3-33B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach. Both do open-weight models you run yourself.

- Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json
- Ornith-1.5-9B: grade C, 61/100, rank #47 of 111 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 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Nanbeige4.3-33B (C)

Good for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.

Ahead on:
- Risk, 60 against 50
- Documentation and upkeep, 56 against 31

Watch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run

### 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 55

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 | Nanbeige4.3-33B | Ornith-1.5-9B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 95 | Ornith-1.5-9B +40 |
| Risk | 20% (20 this run) | 60 | 50 | Nanbeige4.3-33B +10 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 56 | 31 | Nanbeige4.3-33B +25 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | Nanbeige4.3-33B | Ornith-1.5-9B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-24 | 2026-08-23 |
| 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 | none | 2.3/5 (4) |

## Verdicts

**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.

**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

### Nanbeige4.3-33B

1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers
2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes
3. Pass `chat_template_kwargs` `{"preserve_thinking": true}` for multi-turn tool use and coding, and `{"enable_thinking": false}` to skip reasoning, as the card recommends
4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card
5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed
6. Send one system message at the start only, as the template raises an error for a later one
7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed

### 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, Nanbeige4.3-33B or Ornith-1.5-9B?

Nanbeige4.3-33B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nanbeige-4-3-33b", "b": "ornith-1-5-9b"}`. From a terminal: `anchor compare nanbeige-4-3-33b ornith-1-5-9b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json

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- [Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct.md)
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- [Nanbeige4.3-33B vs Recursive Flint](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint.md)
- [Nanbeige4.3-33B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b.md)
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- [Ornith-1.5-9B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507.md)
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- [Ornith-1.5-9B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.md)
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- [Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.md)

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