Models / Compare /DeepSeek V3.2 vs Llama 3.3 70B Instruct
Head to head
DeepSeek V3.2 vs Llama 3.3 70B Instruct
Compared on live catalog data — pricing synced daily, benchmark scores with provenance.
The short version
- Llama 3.3 70B Instruct is cheaper to run hosted: $0.32/M output vs $0.40/M for DeepSeek V3.2 (1.3× difference at the cheapest tracked provider).
- DeepSeek V3.2 takes 164K tokens of context vs 131K for Llama 3.3 70B Instruct.
- DeepSeek V3.2 has downloadable weights (OPEN) so you can self-host it; we hold no downloadable copy of Llama 3.3 70B Instruct, so it runs through a provider's API.
- DeepSeek V3.2 scores 1469.5929 on Arena Coding vs 1345.5887 for Llama 3.3 70B Instruct.
- DeepSeek V3.2 scores 1400.3738 on Arena Creative Writing vs 1286.3107 for Llama 3.3 70B Instruct.
| Spec | ||
|---|---|---|
| Vendor | DeepSeek | Meta |
| Parameters | 685B / 37B | 70.6B |
| 164K | 131K | |
| Weights | OPEN | OPEN* |
| License | MIT License | llama3.3 |
| Modality | text->text | text->text |
| Released | Dec 1, 2025 | Nov 26, 2024 |
| 432.1 GBest | 44.5 GBest | |
| Cheapest hosted | $0.27 / $0.40 per 1M (Novita AI) | $0.10 / $0.32 per 1M (OpenRouter) |
| Tracked providers | 19 | 17 |
DeepSeek V3.2 — our take
DeepSeek V3.2 is a large downloadable text model with a permissive MIT licence and a 163,840-token request limit. Only 37 billion of its 685.4 billion total parameters are active per token, keeping inference costs down while measured coding performance is the clear peak in its benchmark profile.
full breakdown →Llama 3.3 70B Instruct — our take
Llama 3.3 is a 70.6-billion-parameter text model from Meta with a 131,072-token request limit and a licence that permits commercial use with specific conditions. It is a strong instruction-follower with wide hosting choice, though its reasoning scores sit below what its size might suggest.
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