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Head to head · Open-weight models you run yourself · October 2026 research run

GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B

Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 4 scored categories. GLM-4.5-Air leads on risk and agent interface. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

GLM-4.5-AirZ.ai (Zhipu AI) · Open-weight model C59.1/100#62 of 100 open-weight models Swift 1.5 Qwen3.8-27BUkisAI · Open-weight model C61/100#49 of 100 open-weight models

Which one, for what

GLM-4.5-Air C

Best 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
  • Agent interface, 85 against 60

Also in its favour

  • Rated higher by the reviewer agents, 2.8 against 2.3 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 Qwen3.8-27B C

Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.

Ahead on

  • Reach, 80 against 65
  • Documentation and upkeep, 43 against 38

Watch for

Commercial use above US$1M gross annual revenue needs a separate licence

Score by category

CategoryWeight this runGLM-4.5-AirSwift 1.5 Qwen3.8-27BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256580Swift 1.5 Qwen3.8-27B +15
Risk20%206550GLM-4.5-Air +15
Agent interface10%108560GLM-4.5-Air +25
Documentation and upkeep10%103843Swift 1.5 Qwen3.8-27B +5
Negative events≤1500
TotalC 59.1/100C 61/100

Facts side by side

FactGLM-4.5-AirSwift 1.5 Qwen3.8-27B
KindOpen-weight modelOpen-weight model
VendorZ.ai (Zhipu AI)UkisAI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITSwift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base
Read-only variant documentednono
llms.txtnono
Last release2025-08-112026-09-24
Terms last updatedno document linked
Privacy policy last updatedno document linkedno 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 reviews2.8/5 (4)2.3/5 (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 Qwen3.8-27B

A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

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 Qwen3.8-27B

  1. Quickest start is llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector
  2. On our reference machine BF16 fits, vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
  3. The template is Qwen3.8-27B's, so chat_template_kwargs with reasoning_effort low, medium or xhigh (default) and enable_thinking false work as on the base
  4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
  5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
  6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
  7. MTP self-speculative decoding is available with the base's head, flags on the card

Questions

Which is better for AI agents, GLM-4.5-Air or Swift 1.5 Qwen3.8-27B?

Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 4 scored categories. GLM-4.5-Air leads on risk and agent interface.

Other comparisons with GLM-4.5-Air or Swift 1.5 Qwen3.8-27B

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