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DeepSeek R1 Distill Qwen 1.5B vs DeepSeek-V3

DeepSeek-V3 leads the LLM Stats Score 15.7 to -3.0.

DeepSeek · DeepSeek · Updated for 2026

Which is better?

DeepSeek-V3 leads the overall LLM Stats Score 15.7 to -3.0, ranking #227 overall.

In the 4 individual benchmarks reported for both models, DeepSeek-V3 wins 3; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek R1 Distill Qwen 1.5B

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

Choose DeepSeek-V3

  • overall performance matters — it scores 15.7 and ranks #227 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 4 exact shared results

At a glance

The differences that matter most.

DeepSeek R1 Distill Qwen 1.5B
DeepSeek
DeepSeek-V3
DeepSeek
Core performance indexes
LLM Stats Score
-3.0
#341
15.7
#227
Reasoning
-2.6
#331
14.8
#226
Coding
-4.6
#258
6.2
#191
Cost, coverage & limits
Benchmark wins
1 of 4
3 of 4
Input price
— / M
$0.27 / M
Output price
— / M
$0.89 / M
Context window
—
131,072

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek R1 Distill Qwen 1.5B
DeepSeek-V3
Math
5.4#271
18.0#188
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

4 reported for DeepSeek R1 Distill Qwen 1.5B · 20 for DeepSeek-V3

4 shared

DeepSeek R1 Distill Qwen 1.5B outperforms in 1 benchmarks (AIME 2024), while DeepSeek-V3 is better at 3 benchmarks (GPQA, LiveCodeBench, MATH-500).

DeepSeek-V3 shows notably better performance in the majority of benchmarks.

Tue Sep 15 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

669.2B diff

DeepSeek-V3 has 669.2B more parameters than DeepSeek R1 Distill Qwen 1.5B, making it 37596.6% larger.

DeepSeek
DeepSeek R1 Distill Qwen 1.5B
1.8Bparameters
DeepSeek
DeepSeek-V3
671.0Bparameters
1.8B
DeepSeek R1 Distill Qwen 1.5B
671.0B
DeepSeek-V3

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek R1 Distill Qwen 1.5B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Tue Sep 15 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 1.5B is licensed under MIT, while DeepSeek-V3 uses MIT + Model License (Commercial use allowed).

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek R1 Distill Qwen 1.5B

MIT

Open weights

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 1.5B was released on 2025-01-20, while DeepSeek-V3 was released on 2024-12-25.

DeepSeek R1 Distill Qwen 1.5B is 1 month newer than DeepSeek-V3.

DeepSeek R1 Distill Qwen 1.5B

Jan 20, 2025

1.6 years ago

3w newer
DeepSeek-V3

Dec 25, 2024

1.7 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→

Judge for yourself.

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

DeepSeek R1 Distill Qwen 1.5B
✓ Preferred
DeepSeek-V3
Open in Playground

FAQ

Common questions about DeepSeek R1 Distill Qwen 1.5B vs DeepSeek-V3.

Which is better, DeepSeek R1 Distill Qwen 1.5B or DeepSeek-V3?

DeepSeek-V3 leads the LLM Stats Score 15.7 to -3.0. DeepSeek R1 Distill Qwen 1.5B is made by DeepSeek and DeepSeek-V3 is made by DeepSeek. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek R1 Distill Qwen 1.5B compare to DeepSeek-V3 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 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%.

What are the context window sizes for DeepSeek R1 Distill Qwen 1.5B and DeepSeek-V3?

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

What are the main differences between DeepSeek R1 Distill Qwen 1.5B and DeepSeek-V3?

Key differences include LLM Stats Score (-3.0 vs 15.7), licensing (MIT vs MIT + Model License (Commercial use allowed)). See the full comparison above for benchmark-by-benchmark results.

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