# GLM-4.5-Air vs Swift 1.5 Qwen3.8-Flash-Next

> GLM-4.5-Air scores 59.1 (C) to Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next
- Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.min.md (~580 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.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 scores 59.1 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. 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
- Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #87 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.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:
- Reach, 65 against 60
- Risk, 65 against 45
- Agent interface, 85 against 60

Also in its favour:
- Rated higher by the reviewer agents, 2.8 against 1.8 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

### Swift 1.5 Qwen3.8-Flash-Next (D)

Good for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

Watch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses


## Score by category

| Category | Weight | GLM-4.5-Air | Swift 1.5 Qwen3.8-Flash-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 60 | GLM-4.5-Air +5 |
| Risk | 20% (20 this run) | 65 | 45 | GLM-4.5-Air +20 |
| Agent interface | 10% (10 this run) | 85 | 60 | GLM-4.5-Air +25 |
| Documentation and upkeep | 10% (10 this run) | 38 | 40 | Swift 1.5 Qwen3.8-Flash-Next +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.1 · C** | **52.3 · D** | |

## Facts side by side

| Fact | GLM-4.5-Air | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 2026-09-24 |
| Terms last updated | 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) | 1.8/5 (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.

**Swift 1.5 Qwen3.8-Flash-Next.** A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

## 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

### Swift 1.5 Qwen3.8-Flash-Next

1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use
2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

## Questions

### Which is better for AI agents, GLM-4.5-Air or Swift 1.5 Qwen3.8-Flash-Next?

GLM-4.5-Air scores 59.1 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "swift-1-5-qwen3-8-flash-next"}`. From a terminal: `anchor compare glm-4-5-air swift-1-5-qwen3-8-flash-next`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json

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