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

Apertus v1.5 8B vs GLM-4.5-Air

Apertus v1.5 8B 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 reach. 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

Apertus v1.5 8BSwiss AI Initiative (EPFL and ETH Zurich) · Open-weight model C61/100#35 of 100 open-weight models GLM-4.5-AirZ.ai (Zhipu AI) · Open-weight model C59.1/100#62 of 100 open-weight models

Which one, for what

Apertus v1.5 8B C

Best for Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.

Ahead on

Risk, 75 against 65
Documentation and upkeep, 84 against 38

Watch for

No released runtime supports it, only Swiss AI's forks of vLLM and Transformers

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

Reach, 65 against 40

Watch for

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

Score by category

Category	Weight this run	Apertus v1.5 8B	GLM-4.5-Air	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	40	65	GLM-4.5-Air +25
Risk	20%20	75	65	Apertus v1.5 8B +10
Agent interface	10%10	85	85	even
Documentation and upkeep	10%10	84	38	Apertus v1.5 8B +46
Negative events	≤15	0	0
Total		C 61/100	C 59.1/100

Facts side by side

Fact	Apertus v1.5 8B	GLM-4.5-Air
Kind	Open-weight model	Open-weight model
Vendor	Swiss AI Initiative (EPFL and ETH Zurich)	Z.ai (Zhipu AI)
Hosted endpoint	no (local only)	no (local only)
Transports
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate	MIT
Read-only variant documented	no	no
llms.txt	no	no
Last release	2026-10-14	2025-08-11
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)	2.8/5 (4)

Verdicts

Apertus v1.5 8B

A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.

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.

Before you call either

Apertus v1.5 8B

Accept the terms on the card and set HF_TOKEN before downloading
Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
Serve with --chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus for tools, and leave thinking off when you need tools
For thinking, set --reasoning-parser apertus and --default-chat-template-kwargs.enable_thinking true, and budget max tokens generously
Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent

GLM-4.5-Air

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
Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
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
Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights

Questions

Which is better for AI agents, Apertus v1.5 8B or GLM-4.5-Air?

Apertus v1.5 8B 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 reach.

Other comparisons with Apertus v1.5 8B or GLM-4.5-Air

Machine-readable

This page as Markdown /compare/apertus-v1-5-8b-vs-glm-4-5-air.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/apertus-v1-5-8b.json · /api/v1/tools/glm-4-5-air.json
From a terminal anchor compare apertus-v1-5-8b glm-4-5-air (the CLI)
Over MCP compare_tools {"a": "apertus-v1-5-8b", "b": "glm-4-5-air"} at /mcp, no key

Which one, for what

Score by category

Facts side by side

Verdicts

Apertus v1.5 8B

GLM-4.5-Air

Before you call either

Apertus v1.5 8B

GLM-4.5-Air

Questions

Which is better for AI agents, Apertus v1.5 8B or GLM-4.5-Air?

Other comparisons with Apertus v1.5 8B or GLM-4.5-Air

Machine-readable

Do agents find, use and choose your tools?