Model Comparison

DeepSeek R1 Distill Qwen 32B vs Phi-3.5-mini-instruct

DeepSeek R1 Distill Qwen 32B significantly outperforms across most benchmarks. Phi-3.5-mini-instruct is 1.4x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek R1 Distill Qwen 32B outperforms in 1 benchmarks (GPQA), while Phi-3.5-mini-instruct is better at 0 benchmarks.

DeepSeek R1 Distill Qwen 32B significantly outperforms across most benchmarks.

Fri Apr 03 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Phi-3.5-mini-instruct costs less

For input processing, DeepSeek R1 Distill Qwen 32B ($0.12/1M tokens) is 1.2x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, DeepSeek R1 Distill Qwen 32B ($0.18/1M tokens) is 1.8x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, DeepSeek R1 Distill Qwen 32B is more expensive than Phi-3.5-mini-instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Fri Apr 03 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
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Model Size

Parameter count comparison

29.0B diff

DeepSeek R1 Distill Qwen 32B has 29.0B more parameters than Phi-3.5-mini-instruct, making it 763.2% larger.

DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
32.8B
DeepSeek R1 Distill Qwen 32B
3.8B
Phi-3.5-mini-instruct

Context Window

Maximum input and output token capacity

Both models have the same input context window of 128,000 tokens. Both models can generate responses up to 128,000 tokens.

DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Fri Apr 03 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

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

DeepSeek R1 Distill Qwen 32B

MIT

Open weights

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while Phi-3.5-mini-instruct was released on 2024-08-23.

DeepSeek R1 Distill Qwen 32B is 5 months newer than Phi-3.5-mini-instruct.

DeepSeek R1 Distill Qwen 32B

Jan 20, 2025

1.2 years ago

5mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

1.6 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

Provider Availability

DeepSeek R1 Distill Qwen 32B is available from DeepInfra. Phi-3.5-mini-instruct is available from Azure.

DeepSeek R1 Distill Qwen 32B

deepinfra logo
Deepinfra
Input Price:Input: $0.12/1MOutput Price:Output: $0.18/1M

Phi-3.5-mini-instruct

azure logo
Azure
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Higher GPQA score (62.1% vs 30.4%)
Microsoft
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Qwen 32B
Microsoft
Phi-3.5-mini-instruct

FAQ

Common questions about DeepSeek R1 Distill Qwen 32B vs Phi-3.5-mini-instruct

DeepSeek R1 Distill Qwen 32B significantly outperforms across most benchmarks. DeepSeek R1 Distill Qwen 32B is made by DeepSeek and Phi-3.5-mini-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek R1 Distill Qwen 32B scores MATH-500: 94.3%, AIME 2024: 83.3%, GPQA: 62.1%, LiveCodeBench: 57.2%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.
Phi-3.5-mini-instruct is 1.2x cheaper for input tokens. DeepSeek R1 Distill Qwen 32B costs $0.12/M input and $0.18/M output via deepinfra. Phi-3.5-mini-instruct costs $0.10/M input and $0.10/M output via azure.
DeepSeek R1 Distill Qwen 32B supports 128K tokens and Phi-3.5-mini-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include input pricing ($0.12 vs $0.10/M). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Distill Qwen 32B is developed by DeepSeek and Phi-3.5-mini-instruct is developed by Microsoft.