Phi-3.5-MoE-instruct vs Qwen3-Coder-Next
Phi-3.5-MoE-instruct (41.9B parameters) and Qwen3-Coder-Next (79.7B 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 | Phi-3.5-MoE-instruct | Qwen3-Coder-Next |
|---|---|---|
| Parameters | 41.9B | 79.7B (Mixture-of-experts: 10 of 512 experts active per token (exact active-parameter count not stated on the model card)) |
| Architecture | mixture of 16 experts, 2 active per token | Hybrid (some layers use full attention); mixture of 512 experts, 10 active per token |
| Context length | 131,072 tokens | 256K tokens (262,144) |
| License | – | Apache 2.0 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 93.6 GB | 178 GB |
| VRAM needed, FP8 | 46.8 GB | 89.0 GB |
| VRAM needed, INT4 | 23.4 GB | 44.5 GB |
| Cheapest live fit, As published | RTX PRO 6000 · $1.38/hr | RTX A5000 × 8 · $1.41/hr |
| Cheapest live fit, FP8 | L40 · $0.759/hr | RTX PRO 6000 · $1.38/hr |
| Cheapest live fit, INT4 | RTX A5000 · $0.176/hr | RTX A6000 · $0.363/hr |
| KV cache per token (16-bit) | Not published for this architecture | 24 KB |
| KV cache at 32k tokens | Not published for this architecture | 0.75 GB |
| KV cache at 128k tokens | Not published for this architecture | 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
- Phi-3.5-MoE-instruct needs less VRAM at its published precision (93.6 GB against 178 GB), so it fits on a smaller GPU.
- Qwen3-Coder-Next lists the longer context window (262,144 tokens against 131,072).
- Phi-3.5-MoE-instruct has the cheaper live GPU fit at its published precision ($1.38/hr against $1.41/hr).
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
- Phi-3.5-MoE-instruct: full VRAM table and live GPU fit
- Qwen3-Coder-Next: full VRAM table and live GPU fit
- The Phi model series
- The Qwen model series
- All models that fit in 96 GB
- All models that fit in 192 GB
Other comparisons
- Phi-3.5-MoE-instruct vs Qwen-72B
- Phi-3.5-MoE-instruct vs Llama-3.3-70B-Instruct
- Phi-3.5-MoE-instruct vs Llama-3.1-70B-Instruct
- Phi-3.5-MoE-instruct vs Qwen2.5-72B-Instruct
- Qwen3-Coder-Next vs Llama-3.3-70B-Instruct
- Qwen3-Coder-Next vs Llama-3.1-70B-Instruct
- Qwen3-Coder-Next vs Meta-Llama-3-70B
- Qwen3-Coder-Next vs Meta-Llama-3-70B-Instruct
