The AI arena is free today

Open Superagent

DeepSeek R1 Zero vs DeepSeek-V3

DeepSeek R1 Zero and DeepSeek-V3 are closely matched at 16.0 and 15.7 on the LLM Stats Score.

DeepSeek · DeepSeek · Updated for 2026

Which is better?

DeepSeek R1 Zero and DeepSeek-V3 are closely matched on the overall LLM Stats Score at 16.0 and 15.7.

In the 4 individual benchmarks reported for both models, DeepSeek R1 Zero wins 4; 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 Zero

  • you value its reported benchmark strengths — it wins 4 of 4 exact shared results
  • you want the most recent training data — it shipped Jan 2025

Choose DeepSeek-V3

  • you want predictable pricing at $0.27/M input and $0.89/M output

At a glance

The differences that matter most.

DeepSeek R1 Zero
DeepSeek
DeepSeek-V3
DeepSeek
Core performance indexes
LLM Stats Score
16.0
#232
15.7
#234
Reasoning
16.3
#222
14.8
#233
Coding
4.3
#215
6.2
#198
Cost, coverage & limits
Benchmark wins
4 of 4
0 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 Zero
DeepSeek-V3
Math
17.5#195
18.0#189
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

4 reported for DeepSeek R1 Zero · 20 for DeepSeek-V3

4 shared

DeepSeek R1 Zero outperforms in 4 benchmarks (AIME 2024, GPQA, LiveCodeBench, MATH-500), while DeepSeek-V3 is better at 0 benchmarks.

DeepSeek R1 Zero significantly outperforms across most benchmarks.

Wed Sep 23 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

0.0M diff

DeepSeek-V3 has 0.0B more parameters than DeepSeek R1 Zero, making it 0.0% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
DeepSeek
DeepSeek-V3
671.0Bparameters
671.0B
DeepSeek R1 Zero
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 Zero
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Wed Sep 23 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek R1 Zero 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 Zero

MIT

Open weights

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while DeepSeek-V3 was released on 2024-12-25.

DeepSeek R1 Zero is 1 month newer than DeepSeek-V3.

DeepSeek R1 Zero

Jan 20, 2025

1.7 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 Zero and DeepSeek-V3 side-by-side, then vote on the output you prefer.

DeepSeek R1 Zero
✓ Preferred
DeepSeek-V3
Open in Playground

FAQ

Common questions about DeepSeek R1 Zero vs DeepSeek-V3.

Which is better, DeepSeek R1 Zero or DeepSeek-V3?

DeepSeek R1 Zero and DeepSeek-V3 are closely matched on the LLM Stats Score at 16.0 and 15.7. DeepSeek R1 Zero 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 Zero compare to DeepSeek-V3 in benchmarks?

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. 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 Zero and DeepSeek-V3?

DeepSeek R1 Zero 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 Zero and DeepSeek-V3?

Key differences include LLM Stats Score (16.0 vs 15.7), licensing (MIT vs MIT + Model License (Commercial use allowed)). See the full comparison above for benchmark-by-benchmark results.

Related comparisons