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DeepSeek R1 Zero vs Phi-3.5-MoE-instruct

DeepSeek R1 Zero leads the LLM Stats Score 16.0 to 1.9.

DeepSeek · Microsoft · Updated for 2026

Which is better?

DeepSeek R1 Zero leads the overall LLM Stats Score 16.0 to 1.9, ranking #234 overall.

In the 1 individual benchmarks reported for both models, DeepSeek R1 Zero wins 1; 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

  • overall performance matters — it scores 16.0 and ranks #234 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Jan 2025

Choose Phi-3.5-MoE-instruct

  • you are already invested in the Microsoft ecosystem

At a glance

The differences that matter most.

Core performance indexes
16.0
#234
1.9
#326
16.3
#224
1.4
#319
4.3
#217
-3.8
#262
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
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-3.5-MoE-instruct
17.5#195
4.3#286
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

4 reported for DeepSeek R1 Zero · 31 for Phi-3.5-MoE-instruct

1 shared

DeepSeek R1 Zero outperforms in 1 benchmarks (GPQA), while Phi-3.5-MoE-instruct is better at 0 benchmarks.

DeepSeek R1 Zero significantly outperforms across most benchmarks.

Thu Oct 01 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

611.0B diff

DeepSeek R1 Zero has 611.0B more parameters than Phi-3.5-MoE-instruct, making it 1018.3% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
671.0B
DeepSeek R1 Zero
60.0B
Phi-3.5-MoE-instruct

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-3.5-MoE-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Phi-3.5-MoE-instruct was released on 2024-08-23.

DeepSeek R1 Zero is 5 months newer than Phi-3.5-MoE-instruct.

DeepSeek R1 Zero

Jan 20, 2025

1.7 years ago

5mo newer
Phi-3.5-MoE-instruct

Aug 23, 2024

2.1 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?

Judge for yourself.

Run your own prompts against DeepSeek R1 Zero and Phi-3.5-MoE-instruct side-by-side, then vote on the output you prefer.

DeepSeek R1 Zero
✓ Preferred
Phi-3.5-MoE-instruct
Open in Playground

FAQ

Common questions about DeepSeek R1 Zero vs Phi-3.5-MoE-instruct.

Which is better, DeepSeek R1 Zero or Phi-3.5-MoE-instruct?

DeepSeek R1 Zero leads the LLM Stats Score 16.0 to 1.9. DeepSeek R1 Zero is made by DeepSeek and Phi-3.5-MoE-instruct 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-3.5-MoE-instruct in benchmarks?

DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%.

What are the main differences between DeepSeek R1 Zero and Phi-3.5-MoE-instruct?

Key differences include LLM Stats Score (16.0 vs 1.9). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek R1 Zero and Phi-3.5-MoE-instruct?

DeepSeek R1 Zero is developed by DeepSeek and Phi-3.5-MoE-instruct is developed by Microsoft.