Head to head · Open-weight models you run yourself · October 2026 research run
GLM-4.5-Air vs ZAYA1-8B
ZAYA1-8B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). 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 6359 open weights comparisons
Which one, for what
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 55
- Agent interface, 85 against 60
Watch for
Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own
ZAYA1-8B C
Best for People who want a small, fast reasoning model for maths and code and can live without tool calling
Ahead on
- Reach, 80 against 70
- Documentation and upkeep, 50 against 38
Watch for
Tool calling needs a parser that only Zyphra's vLLM fork has
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Agent interface GLM-4.5-Air +25
GLM-4.5-Air 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Z.ai names the glm45 parser for vLLM and SGLang, says to declare tools in OpenAI style and shows calls coming back. Trained tool calling 20, one or more calls per turn. Structured output 0, documented only for Z.ai's hosted API. Reasoning control 10, enable_thinking false turns thinking off per request and the glm45 reasoning parser returns it separately. Context 10, 128K native.
ZAYA1-8B 60
Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.
Documentation and upkeep ZAYA1-8B +12
GLM-4.5-Air 38
Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no reply.
ZAYA1-8B 50
Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.
Reach ZAYA1-8B +10
GLM-4.5-Air 70
Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face.
ZAYA1-8B 80
Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.
Risk GLM-4.5-Air +10
GLM-4.5-Air 65
File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories couldn't be checked.
ZAYA1-8B 55
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the Zyphra organisation, which the card ties to zyphra.com's blog post for the model (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and www.zyphra.com has no security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Score by category
| Category | Weight this run | GLM-4.5-Air | ZAYA1-8B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 70 | 80 | ZAYA1-8B +10 |
| Risk | 20%20 | 65 | 55 | GLM-4.5-Air +10 |
| Agent interface | 10%10 | 85 | 60 | GLM-4.5-Air +25 |
| Documentation and upkeep | 10%10 | 38 | 50 | ZAYA1-8B +12 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 60.3/100 | C 61/100 |
Facts side by side
| Fact | GLM-4.5-Air | ZAYA1-8B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Zyphra |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 2026-07-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
ZAYA1-8B
No score change since it was listed.
Verdicts
GLM-4.5-Air
An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. 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. Its files date from August 2025, with no Z.ai reply since July 2025.
ZAYA1-8B
A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.
Before you call either
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_kwargswithenable_thinkingfalse 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
ZAYA1-8B
- Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
- Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
- Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
- Download
Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes - The quantised repositories have no licence tag. The main one is Apache 2.0
Questions
Which is better for AI agents, GLM-4.5-Air or ZAYA1-8B?
ZAYA1-8B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface.
Other comparisons with GLM-4.5-Air or ZAYA1-8B
- Agents-A1-4B vs GLM-4.5-Air
- Agents-A1-4B vs ZAYA1-8B
- Apertus v1.5 8B vs GLM-4.5-Air
- Apertus v1.5 8B vs ZAYA1-8B
- Apodex-1.1-mini vs GLM-4.5-Air
- Apodex-1.1-mini vs ZAYA1-8B
- Arete-27B vs GLM-4.5-Air
- Arete-27B vs ZAYA1-8B
- BigBang-v1 vs GLM-4.5-Air
- BigBang-v1 vs ZAYA1-8B
- Carbon-X vs GLM-4.5-Air
- Carbon-X vs ZAYA1-8B
- Cola2 vs GLM-4.5-Air
- Cola2 vs ZAYA1-8B
- Cosmos-Reason2-2B vs GLM-4.5-Air
- Cosmos-Reason2-2B vs ZAYA1-8B
- Darwin-36B-Opus vs GLM-4.5-Air
- Darwin-36B-Opus vs ZAYA1-8B
- DeepSeek-V3.2 vs GLM-4.5-Air
- DeepSeek-V3.2 vs ZAYA1-8B
- DeepSeek V4.1 Flash vs GLM-4.5-Air
- DeepSeek V4.1 Flash vs ZAYA1-8B
- DeepSeek-V4-Flash-0731 vs GLM-4.5-Air
- DeepSeek-V4-Flash-0731 vs ZAYA1-8B
- Devstral Small 2 vs GLM-4.5-Air
- Devstral Small 2 vs ZAYA1-8B
- DiffusionGemma 26B A4B IT vs GLM-4.5-Air
- DiffusionGemma 26B A4B IT vs ZAYA1-8B
- EXAONE 4.5 33B vs GLM-4.5-Air
- EXAONE 4.5 33B vs ZAYA1-8B
- Gemma 4 12B IT vs GLM-4.5-Air
- Gemma 4 12B IT vs ZAYA1-8B
- Gemma 4 26B A4B IT vs GLM-4.5-Air
- Gemma 4 26B A4B IT vs ZAYA1-8B
- Gemma 4 31B IT vs GLM-4.5-Air
- Gemma 4 31B IT vs ZAYA1-8B
- Gemma 4 E2B IT vs GLM-4.5-Air
- Gemma 4 E2B IT vs ZAYA1-8B
- Gemma 4 E4B IT vs GLM-4.5-Air
- Gemma 4 E4B IT vs ZAYA1-8B
- GLM-4.5-Air vs GLM-4.7-Flash
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs GLM-5.3-Flash
- GLM-4.5-Air vs gpt-oss-120b
- GLM-4.5-Air vs gpt-oss-20b
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Granite 4.2 3B
- GLM-4.5-Air vs Granite 4.2 8B
- GLM-4.5-Air vs Hy-Memory-30B-A3B
- GLM-4.5-Air vs Hy3
- GLM-4.5-Air vs Inkling
- GLM-4.5-Air vs Inkling-Small
- GLM-4.5-Air vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-4.5-Air vs Kimi K3
- GLM-4.5-Air vs Kimi K3.1 Pro
- GLM-4.5-Air vs Kolibri-1
- GLM-4.5-Air vs Kronos
- GLM-4.5-Air vs Laguna M.1
- GLM-4.5-Air vs Laguna S 2.1
- GLM-4.5-Air vs Laguna XS 2.1
- GLM-4.5-Air vs LBS-27B
- GLM-4.5-Air vs LFM2.5-2.6B
- GLM-4.5-Air vs LFM3-1.7B
- GLM-4.5-Air vs LFM3-Audio-2B
- GLM-4.5-Air vs LFM3-Omni-2B
- GLM-4.5-Air vs LFM3-VL-2B
- GLM-4.5-Air vs Ling-3.0-tiny
- GLM-4.5-Air vs Ling-3.1-flash-1019
- GLM-4.5-Air vs LongCat-2.5
- GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.5-Air vs MiMo-V2.6-Flash-RL
- GLM-4.5-Air vs MiniCPM-o 4.5
- GLM-4.5-Air vs MiniCPM-V 4.7 1B
- GLM-4.5-Air vs MiniCPM-V 4.7 35B-A3B
- GLM-4.5-Air vs MiniCPM5-2B
- GLM-4.5-Air vs MiniMax-M2.5
- GLM-4.5-Air vs MiniMax-M2.7
- GLM-4.5-Air vs MiniMax-M3.1-Flash
- GLM-4.5-Air vs Mistral Large 4
- GLM-4.5-Air vs Muse Glimmer 30B
- GLM-4.5-Air vs Nanbeige4.2-3B
- GLM-4.5-Air vs Nanbeige4.3-33B
- GLM-4.5-Air vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- GLM-4.5-Air vs Nemotron 3.5 Lightning 30B A3B
- GLM-4.5-Air vs Nemotron 3 Nano 30B A3B
- GLM-4.5-Air vs Nemotron 3 Nano 4B
- GLM-4.5-Air vs Nemotron 3 Nano Omni 30B A3B Reasoning
- GLM-4.5-Air vs Nemotron 3 Super 120B A12B
- GLM-4.5-Air vs Nemotron 3 Ultra 550B A55B
- GLM-4.5-Air vs North Mini Code 1.0
- GLM-4.5-Air vs occamy-1.0
- GLM-4.5-Air vs Olmo 3 7B Think
- GLM-4.5-Air vs Ornith-1.5-122B
- GLM-4.5-Air vs Ornith-1.5-35B-A3B
- GLM-4.5-Air vs Ornith-1.5-397B
- GLM-4.5-Air vs Ornith-1.5-9B
- GLM-4.5-Air vs OTel-2.0-LLM-31B-IT
- GLM-4.5-Air vs Qwen-AgentWorld-35B-A3B
- GLM-4.5-Air vs Qwen3-30B-A3B-Instruct-2507
- GLM-4.5-Air vs Qwen3-4B-Instruct-2507
- GLM-4.5-Air vs Qwen3.5-0.8B
- GLM-4.5-Air vs Qwen3.5-122B-A10B
- GLM-4.5-Air vs Qwen3.5-2B
- GLM-4.5-Air vs Qwen3.5-397B-A17B
- GLM-4.5-Air vs Qwen3.5-4B
- GLM-4.5-Air vs Qwen3.5-9B
- GLM-4.5-Air vs Qwen3.6-27B
- GLM-4.5-Air vs Qwen3.6-35B-A3B
- GLM-4.5-Air vs Qwen3.8-27B
- GLM-4.5-Air vs Qwen3.8-Flash-Next
- GLM-4.5-Air vs Qwen3-Coder-30B-A3B-Instruct
- GLM-4.5-Air vs Qwen3-Coder-Next
- GLM-4.5-Air vs Qwen3-Omni-30B-A3B-Instruct
- GLM-4.5-Air vs Qwen3-VL-2B-Instruct
- GLM-4.5-Air vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.5-Air vs Qwen3-VL-32B-Instruct
- GLM-4.5-Air vs Qwen3-VL-4B-Instruct
- GLM-4.5-Air vs Qwen3-VL-8B-Instruct
- GLM-4.5-Air vs Qwopus3.8-27B-Flash-V2
- GLM-4.5-Air vs Recursive Flint
- GLM-4.5-Air vs Ripple-65B Preview
- GLM-4.5-Air vs SenseNova-Loop-9B-Preview
- GLM-4.5-Air vs SmolLM3-3B
- GLM-4.5-Air vs Spark-X2.6-4B
- GLM-4.5-Air vs Step 3.7 Flash
- GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B
- GLM-4.5-Air vs Swift 1.5 Qwen3.8-Flash-Next
- GLM-4.5-Air vs ThinkingCap (Qwen3.8 27B)
- GLM-4.5-Air vs Baidu Unlimited-OCR-2
- GLM-4.5-Air vs WeVisOne-VL-2B
- GLM-4.5-Air vs WeVisOne-VL-4B
- GLM-4.7-Flash vs ZAYA1-8B
- GLM-5.3-Flash vs ZAYA1-8B
- GLM-5.3 vs ZAYA1-8B
- gpt-oss-120b vs ZAYA1-8B
- gpt-oss-20b vs ZAYA1-8B
- Granite 4.2 30B vs ZAYA1-8B
- Granite 4.2 3B vs ZAYA1-8B
- Granite 4.2 8B vs ZAYA1-8B
- Hy-Memory-30B-A3B vs ZAYA1-8B
- Hy3 vs ZAYA1-8B
- Inkling-Small vs ZAYA1-8B
- Inkling vs ZAYA1-8B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B
- Kimi K3.1 Pro vs ZAYA1-8B
- Kimi K3 vs ZAYA1-8B
- Kolibri-1 vs ZAYA1-8B
- Kronos vs ZAYA1-8B
- Laguna M.1 vs ZAYA1-8B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs ZAYA1-8B
- LBS-27B vs ZAYA1-8B
- LFM2.5-2.6B vs ZAYA1-8B
- LFM3-1.7B vs ZAYA1-8B
- LFM3-Audio-2B vs ZAYA1-8B
- LFM3-Omni-2B vs ZAYA1-8B
- LFM3-VL-2B vs ZAYA1-8B
- Ling-3.0-tiny vs ZAYA1-8B
- Ling-3.1-flash-1019 vs ZAYA1-8B
- LongCat-2.5 vs ZAYA1-8B
- MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B
- MiMo-V2.6-Flash-RL vs ZAYA1-8B
- MiniCPM-o 4.5 vs ZAYA1-8B
- MiniCPM-V 4.7 1B vs ZAYA1-8B
- MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B
- MiniCPM5-2B vs ZAYA1-8B
- MiniMax-M2.5 vs ZAYA1-8B
- MiniMax-M2.7 vs ZAYA1-8B
- MiniMax-M3.1-Flash vs ZAYA1-8B
- Mistral Large 4 vs ZAYA1-8B
- Muse Glimmer 30B vs ZAYA1-8B
- Nanbeige4.2-3B vs ZAYA1-8B
- Nanbeige4.3-33B vs ZAYA1-8B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ZAYA1-8B
- Nemotron 3.5 Lightning 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 4B vs ZAYA1-8B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs ZAYA1-8B
- Nemotron 3 Super 120B A12B vs ZAYA1-8B
- Nemotron 3 Ultra 550B A55B vs ZAYA1-8B
- North Mini Code 1.0 vs ZAYA1-8B
- occamy-1.0 vs ZAYA1-8B
- Olmo 3 7B Think vs ZAYA1-8B
- Ornith-1.5-122B vs ZAYA1-8B
- Ornith-1.5-35B-A3B vs ZAYA1-8B
- Ornith-1.5-397B vs ZAYA1-8B
- Ornith-1.5-9B vs ZAYA1-8B
- OTel-2.0-LLM-31B-IT vs ZAYA1-8B
- Qwen-AgentWorld-35B-A3B vs ZAYA1-8B
- Qwen3-30B-A3B-Instruct-2507 vs ZAYA1-8B
- Qwen3-4B-Instruct-2507 vs ZAYA1-8B
- Qwen3.5-0.8B vs ZAYA1-8B
- Qwen3.5-122B-A10B vs ZAYA1-8B
- Qwen3.5-2B vs ZAYA1-8B
- Qwen3.5-397B-A17B vs ZAYA1-8B
- Qwen3.5-4B vs ZAYA1-8B
- Qwen3.5-9B vs ZAYA1-8B
- Qwen3.6-27B vs ZAYA1-8B
- Qwen3.6-35B-A3B vs ZAYA1-8B
- Qwen3.8-27B vs ZAYA1-8B
- Qwen3.8-Flash-Next vs ZAYA1-8B
- Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-Coder-Next vs ZAYA1-8B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-2B-Instruct vs ZAYA1-8B
- Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-32B-Instruct vs ZAYA1-8B
- Qwen3-VL-4B-Instruct vs ZAYA1-8B
- Qwen3-VL-8B-Instruct vs ZAYA1-8B
- Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B
- Recursive Flint vs ZAYA1-8B
- Ripple-65B Preview vs ZAYA1-8B
- SenseNova-Loop-9B-Preview vs ZAYA1-8B
- SmolLM3-3B vs ZAYA1-8B
- Spark-X2.6-4B vs ZAYA1-8B
- Step 3.7 Flash vs ZAYA1-8B
- Swift 1.5 Qwen3.8-27B vs ZAYA1-8B
- Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B
- ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B
- Baidu Unlimited-OCR-2 vs ZAYA1-8B
- WeVisOne-VL-2B vs ZAYA1-8B
- WeVisOne-VL-4B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/glm-4-5-air-vs-zaya1-8b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-4-5-air.json·/api/v1/tools/zaya1-8b.json - From a terminal
anchor compare glm-4-5-air zaya1-8b(the CLI) - Over MCP
compare_tools {"a": "glm-4-5-air", "b": "zaya1-8b"}at/mcp, no key