Llama-3.3-70B-Instruct vs Qwen3-Coder-Next
Llama-3.3-70B-Instruct (70.6B 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 | Llama-3.3-70B-Instruct | Qwen3-Coder-Next |
|---|---|---|
| Parameters | 70.6B | 79.7B (Mixture-of-experts: 10 of 512 experts active per token (exact active-parameter count not stated on the model card)) |
| Architecture | Grouped-query attention | Hybrid (some layers use full attention); mixture of 512 experts, 10 active per token |
| Context length | 128K tokens | 256K tokens (262,144) |
| License | Llama 3.3 Community License Agreement | Apache 2.0 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 158 GB | 178 GB |
| VRAM needed, FP8 | 78.8 GB | 89.0 GB |
| VRAM needed, INT4 | 39.4 GB | 44.5 GB |
| Cheapest live fit, As published | RTX A5000 × 7 · $1.23/hr | RTX A5000 × 8 · $1.41/hr |
| Cheapest live fit, FP8 | RTX PRO 6000 · $1.38/hr | RTX PRO 6000 · $1.38/hr |
| Cheapest live fit, INT4 | RTX A6000 · $0.363/hr | RTX A6000 · $0.363/hr |
| KV cache per token (16-bit) | 320 KB | 24 KB |
| KV cache at 32k tokens | 10.0 GB | 0.75 GB |
| KV cache at 128k tokens | 40.0 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
- Llama-3.3-70B-Instruct needs less VRAM at its published precision (158 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).
- Qwen3-Coder-Next caches less per sequence at 32k tokens (0.8 GB against 10.0 GB), leaving more memory for batching.
- Llama-3.3-70B-Instruct has the cheaper live GPU fit at its published precision ($1.23/hr against $1.41/hr).
- Licenses differ: Qwen3-Coder-Next is under Apache 2.0, which our catalog notes as permissive; Llama-3.3-70B-Instruct is under Llama 3.3 Community License Agreement, so read its terms 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
- Llama-3.3-70B-Instruct: full VRAM table and live GPU fit
- Qwen3-Coder-Next: full VRAM table and live GPU fit
- The Llama model series
- The Qwen model series
- All models that fit in 192 GB
Other comparisons
- Llama-3.3-70B-Instruct vs Qwen-72B
- Llama-3.3-70B-Instruct vs Qwen2.5-72B-Instruct
- Llama-3.3-70B-Instruct vs Phi-3.5-MoE-instruct
- Llama-3.3-70B-Instruct vs Qwen2.5-72B
- Qwen3-Coder-Next vs Llama-3.1-70B-Instruct
- Qwen3-Coder-Next vs Phi-3.5-MoE-instruct
- Qwen3-Coder-Next vs Meta-Llama-3-70B
- Qwen3-Coder-Next vs Meta-Llama-3-70B-Instruct
