GLM-4.5-Air vs Qwen3-Next-80B-A3B-Thinking
GLM-4.5-Air (110.5B parameters) and Qwen3-Next-80B-A3B-Thinking (81.3B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | GLM-4.5-Air | Qwen3-Next-80B-A3B-Thinking |
|---|---|---|
| Parameters | 110.5B (Mixture-of-experts: 8 of 128 experts active per token (exact active-parameter count not stated on the model card)) | 81.3B (~3B active per token (mixture-of-experts; see total parameters above)) |
| Architecture | Grouped-query attention; mixture of 128 experts, 8 active per token | Hybrid (some layers use full attention); mixture of 512 experts, 10 active per token |
| Context length | 128K tokens (131,072) | 256K tokens (262,144) |
| License | MIT | Apache 2.0 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 247 GB | 182 GB |
| VRAM needed, FP8 | 123 GB | 90.9 GB |
| VRAM needed, INT4 | 61.7 GB | 45.4 GB |
| Cheapest live fit, As published | RTX A6000 × 6 · $2.18/hr | RTX A5000 × 8 · $1.41/hr |
| Cheapest live fit, FP8 | RTX 4080 Super × 4 · $1.35/hr | RTX PRO 6000 · $1.38/hr |
| Cheapest live fit, INT4 | A100 · $1.21/hr | RTX A6000 · $0.363/hr |
| KV cache per token (16-bit) | 184 KB | 24 KB |
| KV cache at 32k tokens | 5.75 GB | 0.75 GB |
| KV cache at 128k tokens | 23.0 GB | 3.00 GB |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- Qwen3-Next-80B-A3B-Thinking needs less VRAM at its published precision (182 GB against 247 GB), so it fits on a smaller GPU.
- Qwen3-Next-80B-A3B-Thinking lists the longer context window (262,144 tokens against 131,072).
- Qwen3-Next-80B-A3B-Thinking caches less per sequence at 32k tokens (0.8 GB against 5.8 GB), leaving more memory for batching.
- Qwen3-Next-80B-A3B-Thinking has the cheaper live GPU fit at its published precision ($1.41/hr against $2.18/hr).
- Licenses differ: GLM-4.5-Air is under MIT and Qwen3-Next-80B-A3B-Thinking under Apache 2.0. Read both before commercial use.
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- GLM-4.5-Air: full VRAM table and live GPU fit
- Qwen3-Next-80B-A3B-Thinking: full VRAM table and live GPU fit
- The GLM model series
- The Qwen model series
- All models that fit in 288 GB
- All models that fit in 192 GB
Other comparisons
- GLM-4.5-Air vs NVIDIA-Nemotron-3-Super-120B-A12B-BF16
- GLM-4.5-Air vs Qwen3-Next-80B-A3B-Instruct
- GLM-4.5-Air vs Hunyuan-A13B-Instruct
- GLM-4.5-Air vs NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16
- Qwen3-Next-80B-A3B-Thinking vs NVIDIA-Nemotron-3-Super-120B-A12B-BF16
- Qwen3-Next-80B-A3B-Thinking vs Hunyuan-A13B-Instruct
- Qwen3-Next-80B-A3B-Thinking vs NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16
- Qwen3-Next-80B-A3B-Thinking vs GLM-4.5-Air-Base
