Model Comparison
DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Llama 70BWhich is better in 2026?
Comparing DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Llama 70B across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Llama 70B — which is better?
DeepSeek-V3.2 (Non-thinking) (by DeepSeek) and DeepSeek R1 Distill Llama 70B (by DeepSeek) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, DeepSeek R1 Distill Llama 70B is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3.2 (Non-thinking) also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.2 (Non-thinking) if…
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Dec 2025
Choose DeepSeek R1 Distill Llama 70B if…
- cost matters — it's about 1.8x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Llama 70Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 2.8x more expensive than DeepSeek R1 Distill Llama 70B ($0.10/1M tokens).
For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 1.0x more expensive than DeepSeek R1 Distill Llama 70B ($0.40/1M tokens).
In conclusion, DeepSeek-V3.2 (Non-thinking) is more expensive than DeepSeek R1 Distill Llama 70B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (Non-thinking) has 614.4B more parameters than DeepSeek R1 Distill Llama 70B, making it 870.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3.2 (Non-thinking) accepts 131,072 input tokens compared to DeepSeek R1 Distill Llama 70B's 128,000 tokens. DeepSeek R1 Distill Llama 70B can generate longer responses up to 128,000 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while DeepSeek R1 Distill Llama 70B was released on 2025-01-20.
DeepSeek-V3.2 (Non-thinking) is 11 months newer than DeepSeek R1 Distill Llama 70B.
Dec 1, 2025
6 months ago
10mo newerJan 20, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. DeepSeek R1 Distill Llama 70B is available from DeepInfra.
DeepSeek-V3.2 (Non-thinking)
DeepSeek R1 Distill Llama 70B
Outputs Comparison
Key Takeaways
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Llama 70B.