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DeepSeek R1 Distill Qwen 1.5B vs DeepSeek-V3.2 (Thinking)

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

DeepSeek · DeepSeek · Updated for 2026

Which is better?

DeepSeek R1 Distill Qwen 1.5B outperforms in 0 benchmarks, while DeepSeek-V3.2 (Thinking) is better at 2 benchmarks (GPQA, LiveCodeBench). DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek R1 Distill Qwen 1.5B

  • you are already invested in the DeepSeek ecosystem

Choose DeepSeek-V3.2 (Thinking)

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Dec 2025

At a glance

The differences that matter most.

Benchmark wins
0 of 2
2 of 2
Input price
— / M
$0.28 / M
Output price
— / M
$0.42 / M
Context window
131,072
Released
Jan 2025
Dec 2025
License
MIT
MIT

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek R1 Distill Qwen 1.5B outperforms in 0 benchmarks, while DeepSeek-V3.2 (Thinking) is better at 2 benchmarks (GPQA, LiveCodeBench).

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Wed Aug 26 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

683.2B diff

DeepSeek-V3.2 (Thinking) has 683.2B more parameters than DeepSeek R1 Distill Qwen 1.5B, making it 38383.1% larger.

DeepSeek
DeepSeek R1 Distill Qwen 1.5B
1.8Bparameters
DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
1.8B
DeepSeek R1 Distill Qwen 1.5B
685.0B
DeepSeek-V3.2 (Thinking)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).

DeepSeek
DeepSeek R1 Distill Qwen 1.5B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Wed Aug 26 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 R1 Distill Qwen 1.5B

MIT

Open weights

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 1.5B was released on 2025-01-20, while DeepSeek-V3.2 (Thinking) was released on 2025-12-01.

DeepSeek-V3.2 (Thinking) is 11 months newer than DeepSeek R1 Distill Qwen 1.5B.

DeepSeek R1 Distill Qwen 1.5B

Jan 20, 2025

1.6 years ago

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

8 months ago

10mo newer

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

Judge for yourself.

Run your own prompts against DeepSeek R1 Distill Qwen 1.5B and DeepSeek-V3.2 (Thinking) side-by-side, then vote on the output you prefer.

DeepSeek R1 Distill Qwen 1.5B
✓ Preferred
DeepSeek-V3.2 (Thinking)
Open in Playground

FAQ

Common questions about DeepSeek R1 Distill Qwen 1.5B vs DeepSeek-V3.2 (Thinking).

Which is better, DeepSeek R1 Distill Qwen 1.5B or DeepSeek-V3.2 (Thinking)?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek R1 Distill Qwen 1.5B is made by DeepSeek and DeepSeek-V3.2 (Thinking) is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek R1 Distill Qwen 1.5B compare to DeepSeek-V3.2 (Thinking) in benchmarks?

DeepSeek R1 Distill Qwen 1.5B scores MATH-500: 83.9%, AIME 2024: 52.7%, GPQA: 33.8%, LiveCodeBench: 16.9%. DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%.

What are the context window sizes for DeepSeek R1 Distill Qwen 1.5B and DeepSeek-V3.2 (Thinking)?

DeepSeek R1 Distill Qwen 1.5B supports an unknown number of tokens and DeepSeek-V3.2 (Thinking) supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.