Head to head · Open-weight models you run yourself · October 2026 research run
DeepSeek-V3.2 vs GLM-4.5-Air
GLM-4.5-Air and DeepSeek-V3.2 score within a point of each other on agent readiness, 59.1 (C) and 58.6 (C). DeepSeek-V3.2 leads on capability. 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
DeepSeek-V3.2DeepSeek · Open-weight model
C58.6/100#65 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
Best for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
Ahead on
- Capability, 69 against 50
Watch for
689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB
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 60
- 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
Score by category
| Category | Weight this run | DeepSeek-V3.2 | GLM-4.5-Air | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 69 | 50 | DeepSeek-V3.2 +19 |
| Reach | 25%25 | 60 | 65 | GLM-4.5-Air +5 |
| Risk | 20%20 | 50 | 65 | GLM-4.5-Air +15 |
| Agent interface | 10%10 | 60 | 85 | GLM-4.5-Air +25 |
| Documentation and upkeep | 10%10 | 34 | 38 | GLM-4.5-Air +4 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58.6/100 | C 59.1/100 |
Facts side by side
| Fact | DeepSeek-V3.2 | GLM-4.5-Air |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Z.ai (Zhipu AI) |
| 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 | 2025-12-01 | 2025-08-11 |
| 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 | 2.3/5 (4) | 2.8/5 (4) |
Verdicts
DeepSeek-V3.2
DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.
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
DeepSeek-V3.2
- Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
- It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
- Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
- Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
- Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
- Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens
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
Questions
Which is better for AI agents, DeepSeek-V3.2 or GLM-4.5-Air?
GLM-4.5-Air and DeepSeek-V3.2 score within a point of each other on agent readiness, 59.1 (C) and 58.6 (C). DeepSeek-V3.2 leads on capability.
Other comparisons with DeepSeek-V3.2 or GLM-4.5-Air
- Agents-A1-4B vs DeepSeek-V3.2
- Agents-A1-4B vs GLM-4.5-Air
- Apertus v1.5 8B vs DeepSeek-V3.2
- Apertus v1.5 8B vs GLM-4.5-Air
- Apodex-1.1-mini vs DeepSeek-V3.2
- Apodex-1.1-mini vs GLM-4.5-Air
- BigBang-v1 vs DeepSeek-V3.2
- BigBang-v1 vs GLM-4.5-Air
- Cosmos-Reason2-2B vs DeepSeek-V3.2
- Cosmos-Reason2-2B vs GLM-4.5-Air
- Darwin-36B-Opus vs DeepSeek-V3.2
- Darwin-36B-Opus vs GLM-4.5-Air
- DeepSeek-V3.2 vs DeepSeek V4.1 Flash
- DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731
- DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 12B IT
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 31B IT
- DeepSeek-V3.2 vs Gemma 4 E2B IT
- DeepSeek-V3.2 vs Gemma 4 E4B IT
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs GLM-5.3-Flash
- DeepSeek-V3.2 vs gpt-oss-120b
- DeepSeek-V3.2 vs gpt-oss-20b
- DeepSeek-V3.2 vs Granite 4.2 3B
- DeepSeek-V3.2 vs Hy3
- DeepSeek-V3.2 vs Inkling
- DeepSeek-V3.2 vs Inkling-Small
- DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- DeepSeek-V3.2 vs Kimi K3
- DeepSeek-V3.2 vs Kolibri-1
- DeepSeek-V3.2 vs Laguna M.1
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs Laguna XS 2.1
- DeepSeek-V3.2 vs LFM2.5-2.6B
- DeepSeek-V3.2 vs Ling-3.0-tiny
- DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V3.2 vs MiniCPM-o 4.5
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek-V3.2 vs MiniMax-M2.5
- DeepSeek-V3.2 vs MiniMax-M2.7
- DeepSeek-V3.2 vs Muse Glimmer 30B
- DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 4B
- DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B
- DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B
- DeepSeek-V3.2 vs occamy-1.0
- DeepSeek-V3.2 vs Olmo 3 7B Think
- DeepSeek-V3.2 vs Ornith-1.5-35B-A3B
- DeepSeek-V3.2 vs Ornith-1.5-397B
- DeepSeek-V3.2 vs Ornith-1.5-9B
- DeepSeek-V3.2 vs Qwen3-30B-A3B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3-4B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3.5-0.8B
- DeepSeek-V3.2 vs Qwen3.5-122B-A10B
- DeepSeek-V3.2 vs Qwen3.5-2B
- DeepSeek-V3.2 vs Qwen3.5-397B-A17B
- DeepSeek-V3.2 vs Qwen3.5-4B
- DeepSeek-V3.2 vs Qwen3.5-9B
- DeepSeek-V3.2 vs Qwen3.6-27B
- DeepSeek-V3.2 vs Qwen3.6-35B-A3B
- DeepSeek-V3.2 vs Qwen3.8-27B
- DeepSeek-V3.2 vs Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs Qwen3-Coder-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-Coder-Next
- DeepSeek-V3.2 vs Qwen3-Omni-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-2B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Qwopus3.8-27B-Flash-V2
- DeepSeek-V3.2 vs SmolLM3-3B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)
- DeepSeek V4.1 Flash vs GLM-4.5-Air
- DeepSeek-V4-Flash-0731 vs GLM-4.5-Air
- DiffusionGemma 26B A4B IT vs GLM-4.5-Air
- Gemma 4 12B IT vs GLM-4.5-Air
- Gemma 4 26B A4B IT vs GLM-4.5-Air
- Gemma 4 31B IT vs GLM-4.5-Air
- Gemma 4 E2B IT vs GLM-4.5-Air
- Gemma 4 E4B IT vs GLM-4.5-Air
- 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 3B
- 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 Kolibri-1
- 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 LFM2.5-2.6B
- GLM-4.5-Air vs Ling-3.0-tiny
- GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.5-Air vs MiniCPM-o 4.5
- 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 Muse Glimmer 30B
- 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 occamy-1.0
- GLM-4.5-Air vs Olmo 3 7B Think
- 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 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 SmolLM3-3B
- GLM-4.5-Air vs Spark-X2.6-4B
- 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)
Machine-readable
- This page as Markdown
/compare/deepseek-v3-2-vs-glm-4-5-air.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/deepseek-v3-2.json·/api/v1/tools/glm-4-5-air.json - From a terminal
anchor compare deepseek-v3-2 glm-4-5-air(the CLI) - Over MCP
compare_tools {"a": "deepseek-v3-2", "b": "glm-4-5-air"}at/mcp, no key