Head to head · Open-weight models you run yourself · October 2026 research run
GLM-4.5-Air vs GLM-4.7-Flash
GLM-4.7-Flash scores 63 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on risk and documentation and upkeep. 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
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
- Documentation and upkeep, 38 against 28
Watch for
Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own
Best for Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime
Ahead on
- Capability, 59 against 50
- Reach, 80 against 65
Watch for
No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January
Score by category
| Category | Weight this run | GLM-4.5-Air | GLM-4.7-Flash | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 50 | 59 | GLM-4.7-Flash +9 |
| Reach | 25%25 | 65 | 80 | GLM-4.7-Flash +15 |
| Risk | 20%20 | 65 | 55 | GLM-4.5-Air +10 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 38 | 28 | GLM-4.5-Air +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59.1/100 | B 63/100 |
Facts side by side
| Fact | GLM-4.5-Air | GLM-4.7-Flash |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | 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 | 2025-08-11 | 2026-01-29 |
| 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 | 2.8/5 (4) | 2.8/5 (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.
GLM-4.7-Flash
A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.
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
GLM-4.7-Flash
- On our reference machine start it with
vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash - Or run
ollama run glm-4.7-flashfor the 19 GB q4_K_M build - Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
- Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
- Pass
chat_template_kwargs: {"enable_thinking": false}for short tasks, since thinking is on by default - Set
clear_thinking: falseto keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench - Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000
Questions
Which is better for AI agents, GLM-4.5-Air or GLM-4.7-Flash?
GLM-4.7-Flash scores 63 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on risk and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/glm-4-5-air-vs-glm-4-7-flash.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/glm-4-7-flash.json - From a terminal
anchor compare glm-4-5-air glm-4-7-flash(the CLI) - Over MCP
compare_tools {"a": "glm-4-5-air", "b": "glm-4-7-flash"}at/mcp, no key