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DeepSeek R1 Zero vs Phi 4 Reasoning

DeepSeek R1 Zero and Phi 4 Reasoning are closely matched at 16.0 and 12.0 on the LLM Stats Score.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek R1 Zero and Phi 4 Reasoning are closely matched on the overall LLM Stats Score at 16.0 and 12.0.

In the 3 individual benchmarks reported for both models, DeepSeek R1 Zero wins 2; 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 2 of 3 exact shared results

Choose Phi 4 Reasoning

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

At a glance

The differences that matter most.

Core performance indexes
16.0
#234
12.0
#262
16.3
#224
12.6
#251
4.3
#217
6.4
#198
Cost, coverage & limits
Benchmark wins
2 of 3
1 of 3
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek R1 Zero
Phi 4 Reasoning
17.5#196
13.6#228
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

4 reported for DeepSeek R1 Zero · 11 for Phi 4 Reasoning

3 shared

DeepSeek R1 Zero outperforms in 2 benchmarks (AIME 2024, GPQA), while Phi 4 Reasoning is better at 1 benchmark (LiveCodeBench).

DeepSeek R1 Zero shows notably better performance in the majority of benchmarks.

Fri Oct 02 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

657.0B diff

DeepSeek R1 Zero has 657.0B more parameters than Phi 4 Reasoning, making it 4692.9% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Microsoft
Phi 4 Reasoning
14.0Bparameters
671.0B
DeepSeek R1 Zero
14.0B
Phi 4 Reasoning

License

Usage and distribution terms

Both models are licensed under MIT.

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

DeepSeek R1 Zero

MIT

Open weights

Phi 4 Reasoning

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Phi 4 Reasoning was released on 2025-04-30.

Phi 4 Reasoning is 3 months newer than DeepSeek R1 Zero.

DeepSeek R1 Zero

Jan 20, 2025

1.7 years ago

Phi 4 Reasoning

Apr 30, 2025

1.4 years ago

3mo newer

Knowledge Cutoff

When training data ends

Phi 4 Reasoning has a documented knowledge cutoff of 2025-03-01, while DeepSeek R1 Zero's cutoff date is not specified.

We can confirm Phi 4 Reasoning's training data extends to 2025-03-01, but cannot make a direct comparison without DeepSeek R1 Zero's cutoff date.

DeepSeek R1 Zero

—

Phi 4 Reasoning

Mar 2025

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek R1 Zero and Phi 4 Reasoning side-by-side, then vote on the output you prefer.

DeepSeek R1 Zero
✓ Preferred
Phi 4 Reasoning
Open in Playground

FAQ

Common questions about DeepSeek R1 Zero vs Phi 4 Reasoning.

Which is better, DeepSeek R1 Zero or Phi 4 Reasoning?

DeepSeek R1 Zero and Phi 4 Reasoning are closely matched on the LLM Stats Score at 16.0 and 12.0. DeepSeek R1 Zero is made by DeepSeek and Phi 4 Reasoning is made by Microsoft. 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 Phi 4 Reasoning in benchmarks?

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Phi 4 Reasoning scores FlenQA: 97.7%, HumanEval+: 92.9%, IFEval: 83.4%, OmniMath: 76.6%, AIME 2024: 75.3%.

What are the main differences between DeepSeek R1 Zero and Phi 4 Reasoning?

Key differences include LLM Stats Score (16.0 vs 12.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek R1 Zero and Phi 4 Reasoning?

DeepSeek R1 Zero is developed by DeepSeek and Phi 4 Reasoning is developed by Microsoft.