Model Comparison

DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Llama 8BWhich is better in 2026?

Comparing DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Llama 8B across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Llama 8B — which is better?

DeepSeek-V3.2 (Non-thinking) (by DeepSeek) and DeepSeek R1 Distill Llama 8B (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.

Choose DeepSeek-V3.2 (Non-thinking) if…

  • you want the most recent training data — it shipped Dec 2025

Choose DeepSeek R1 Distill Llama 8B if…

  • you are already invested in the DeepSeek ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Llama 8Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

677.0B diff

DeepSeek-V3.2 (Non-thinking) has 677.0B more parameters than DeepSeek R1 Distill Llama 8B, making it 8430.5% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
DeepSeek
DeepSeek R1 Distill Llama 8B
8.0Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
8.0B
DeepSeek R1 Distill Llama 8B

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Non-thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input131,072 tokens
Output8,192 tokens
DeepSeek
DeepSeek R1 Distill Llama 8B
Input- tokens
Output- tokens
Tue Jul 28 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

DeepSeek R1 Distill Llama 8B

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 8B was released on 2025-01-20.

DeepSeek-V3.2 (Non-thinking) is 11 months newer than DeepSeek R1 Distill Llama 8B.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

7 months ago

10mo newer
DeepSeek R1 Distill Llama 8B

Jan 20, 2025

1.5 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
DeepSeek

No standout differentiators in the data we have for this pair.

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Llama 8B side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Non-thinking)
✓ Preferred
DeepSeek R1 Distill Llama 8B
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Non-thinking)
DeepSeek
DeepSeek R1 Distill Llama 8B

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Llama 8B.

Which is better, DeepSeek-V3.2 (Non-thinking) or DeepSeek R1 Distill Llama 8B?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and DeepSeek R1 Distill Llama 8B (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3.2 (Non-thinking) compare to DeepSeek R1 Distill Llama 8B in benchmarks?

DeepSeek R1 Distill Llama 8B scores MATH-500: 89.1%, AIME 2024: 80.0%, GPQA: 49.0%, LiveCodeBench: 39.6%.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Llama 8B?

DeepSeek-V3.2 (Non-thinking) supports 131K tokens and DeepSeek R1 Distill Llama 8B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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