DeepSeek-V3.2 (Thinking) vs Llama 3.3 70B Instruct: Specs & Benchmark Comparison
| Characteristic | DeepSeek-V3.2 (Thinking) | Llama 3.3 70B Instruct |
|---|---|---|
| Company | DeepSeek | Meta |
| Release Date | November 30, 2025 | December 6, 2024 |
| Parameters | 685B | 70B |
| Multimodal | No | No |
| Context (input) | 131K | 128K |
| Context (output) | 66K | 128K |
| Input Price / 1M | $0.28 | $0.88 |
| Output Price / 1M | $0.42 | $0.88 |
| Average Score | 0.9 | 0.8 |
| Benchmarks | ||
| GPQA | 0.8 | 0.5 |
| MMLU-Pro | 0.8 | 0.7 |
Visual Benchmark Comparison
DeepSeek-V3.2 (Thinking)
Llama 3.3 70B Instruct
GPQA0.8 vs 0.5
0.8
0.5
MMLU-Pro0.8 vs 0.7
0.8
0.7
Verdict
DeepSeek-V3.2 (Thinking) leads in 3 out of 4 comparison categories.
Overall Performance
Both models show comparable average scores: DeepSeek-V3.2 (Thinking) — 0.9, Llama 3.3 70B Instruct — 0.8.
API Cost
DeepSeek-V3.2 (Thinking) is 2.5x cheaper: input $0.28/1M vs $0.88/1M tokens.
Context Window
DeepSeek-V3.2 (Thinking) supports a larger context: 131K vs 128K tokens.
Recency
DeepSeek-V3.2 (Thinking) is newer: released 11/30/2025 vs 12/6/2024.
More About These Models
Related Comparisons
Frequently Asked Questions
Which is better for coding — DeepSeek-V3.2 (Thinking) or Llama 3.3 70B Instruct?
Direct comparison on the SWE-Bench benchmark is not available. We recommend reviewing other metrics on the comparison page.
Which model is cheaper — DeepSeek-V3.2 (Thinking) or Llama 3.3 70B Instruct?
DeepSeek-V3.2 (Thinking) is cheaper for input: $0.28 per 1M tokens vs $0.88.
Which has a larger context window — DeepSeek-V3.2 (Thinking) or Llama 3.3 70B Instruct?
DeepSeek-V3.2 (Thinking) supports a larger context: 131,072 tokens vs 128,000.
The DeepSeek-V3.2 (Thinking) and Llama 3.3 70B Instruct comparison is updated for 2026. Data includes benchmark results, API pricing, context window size and other specifications. For more detailed information, visit the DeepSeek-V3.2 (Thinking) or Llama 3.3 70B Instruct page. See also the complete list of AI model comparisons.