Model Comparison

Phi-3.5-MoE-instruct vs Qwen2 7B InstructWhich is better in 2026?

Phi-3.5-MoE-instruct significantly outperforms across most benchmarks.

Verdict: Phi-3.5-MoE-instruct vs Qwen2 7B Instruct — which is better?

Phi-3.5-MoE-instruct (by Microsoft) and Qwen2 7B Instruct (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Phi-3.5-MoE-instruct outperforms in 6 benchmarks (GPQA, GSM8k, MATH, MBPP, MMLU, MMLU-Pro), while Qwen2 7B Instruct is better at 1 benchmark (HumanEval). Phi-3.5-MoE-instruct significantly outperforms across most benchmarks.

Choose Phi-3.5-MoE-instruct if…

  • you want the strongest raw capability — it leads on 6 of 7 shared benchmarks
  • you want the most recent training data — it shipped Aug 2024

Choose Qwen2 7B Instruct if…

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

7 benchmarks

Phi-3.5-MoE-instruct outperforms in 6 benchmarks (GPQA, GSM8k, MATH, MBPP, MMLU, MMLU-Pro), while Qwen2 7B Instruct is better at 1 benchmark (HumanEval).

Phi-3.5-MoE-instruct significantly outperforms across most benchmarks.

Sat Jun 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

52.4B diff

Phi-3.5-MoE-instruct has 52.4B more parameters than Qwen2 7B Instruct, making it 687.4% larger.

Microsoft
Phi-3.5-MoE-instruct
60.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2 7B Instruct
7.6Bparameters
60.0B
Phi-3.5-MoE-instruct
7.6B
Qwen2 7B Instruct

License

Usage and distribution terms

Phi-3.5-MoE-instruct is licensed under MIT, while Qwen2 7B Instruct uses Apache 2.0.

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

Phi-3.5-MoE-instruct

MIT

Open weights

Qwen2 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Phi-3.5-MoE-instruct was released on 2024-08-23, while Qwen2 7B Instruct was released on 2024-07-23.

Phi-3.5-MoE-instruct is 1 month newer than Qwen2 7B Instruct.

Phi-3.5-MoE-instruct

Aug 23, 2024

1.8 years ago

1mo newer
Qwen2 7B Instruct

Jul 23, 2024

1.9 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

Key Takeaways

Higher GPQA score (36.8% vs 25.3%)
Higher GSM8k score (88.7% vs 82.3%)
Higher MATH score (59.5% vs 49.6%)
Higher MBPP score (80.8% vs 67.2%)
Higher MMLU score (78.9% vs 70.5%)
Higher MMLU-Pro score (45.3% vs 44.1%)
Alibaba Cloud / Qwen Team

Qwen2 7B Instruct

View details

Alibaba Cloud / Qwen Team

Higher HumanEval score (79.9% vs 70.7%)

Detailed Comparison

AI Model Comparison Table
Feature
Microsoft
Phi-3.5-MoE-instruct
Alibaba Cloud / Qwen Team
Qwen2 7B Instruct

FAQ

Common questions about Phi-3.5-MoE-instruct vs Qwen2 7B Instruct.

Which is better, Phi-3.5-MoE-instruct or Qwen2 7B Instruct?

Phi-3.5-MoE-instruct significantly outperforms across most benchmarks. Phi-3.5-MoE-instruct is made by Microsoft and Qwen2 7B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Phi-3.5-MoE-instruct compare to Qwen2 7B Instruct in benchmarks?

Phi-3.5-MoE-instruct scores ARC-C: 91.0%, OpenBookQA: 89.6%, GSM8k: 88.7%, PIQA: 88.6%, RULER: 87.1%. Qwen2 7B Instruct scores MT-Bench: 84.1%, GSM8k: 82.3%, HumanEval: 79.9%, C-Eval: 77.2%, AlignBench: 72.1%.

What are the main differences between Phi-3.5-MoE-instruct and Qwen2 7B Instruct?

Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Phi-3.5-MoE-instruct and Qwen2 7B Instruct?

Phi-3.5-MoE-instruct is developed by Microsoft and Qwen2 7B Instruct is developed by Alibaba Cloud / Qwen Team.