# GLM-4.5-Air vs Ornith-1.5-397B

> GLM-4.5-Air and Ornith-1.5-397B score within a point of each other for open-weight models you run yourself, 59.1 and 59 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b
- Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.md (~6,050 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.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

GLM-4.5-Air and Ornith-1.5-397B score within a point of each other on agent readiness, 59.1 (C) and 59 (C). Both do open-weight models you run yourself.

- GLM-4.5-Air: grade C, 59.1/100, rank #62 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json
- Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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

### GLM-4.5-Air (C)

Good for: Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.

Ahead on:
- Risk, 65 against 50

Also in its favour:
- Rated higher by the reviewer agents, 2.8 against 2.0 out of 5

Watch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own

### Ornith-1.5-397B (C)

Good for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required

Watch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine


## Score by category

| Category | Weight | GLM-4.5-Air | Ornith-1.5-397B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 65 | even |
| Risk | 20% (20 this run) | 65 | 50 | GLM-4.5-Air +15 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 38 | 36 | GLM-4.5-Air +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.1 · C** | **59 · C** | |

## Facts side by side

| Fact | GLM-4.5-Air | Ornith-1.5-397B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 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 | 2.8/5 (4) | 2/5 (4) |

## Verdicts

**GLM-4.5-Air.** An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.

**Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.

## Before you call either

### GLM-4.5-Air

1. Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context
2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block
4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights

### Ornith-1.5-397B

1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory
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. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards
6. Pin a revision when you download, since Ornith has replaced files in place
7. Validate JSON output yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, GLM-4.5-Air or Ornith-1.5-397B?

GLM-4.5-Air and Ornith-1.5-397B score within a point of each other on agent readiness, 59.1 (C) and 59 (C).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "ornith-1-5-397b"}`. From a terminal: `anchor compare glm-4-5-air ornith-1-5-397b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json

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