Model Comparison

Phi-3.5-mini-instruct vs Qwen3 VL 30B A3B Thinking

Qwen3 VL 30B A3B Thinking significantly outperforms across most benchmarks. Phi-3.5-mini-instruct is 4.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

Phi-3.5-mini-instruct outperforms in 0 benchmarks, while Qwen3 VL 30B A3B Thinking is better at 3 benchmarks (GPQA, MMLU, MMLU-Pro).

Qwen3 VL 30B A3B Thinking significantly outperforms across most benchmarks.

Tue May 12 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, Phi-3.5-mini-instruct ($0.10/1M tokens) is 2.0x cheaper than Qwen3 VL 30B A3B Thinking ($0.20/1M tokens).

For output processing, Phi-3.5-mini-instruct ($0.10/1M tokens) is 9.9x cheaper than Qwen3 VL 30B A3B Thinking ($0.99/1M tokens).

In conclusion, Qwen3 VL 30B A3B Thinking 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

Tue May 12 2026 • llm-stats.com

Phi-3.5-mini-instruct

Input tokens$0.10

Output tokens$0.10

Best providerAzure

Qwen3 VL 30B A3B Thinking

Input tokens$0.20

Output tokens$0.99

Best providerNovita

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Model Size

Parameter count comparison

27.2B diff

Qwen3 VL 30B A3B Thinking has 27.2B more parameters than Phi-3.5-mini-instruct, making it 715.8% larger.

Phi-3.5-mini-instruct

3.8Bparameters

Qwen3 VL 30B A3B Thinking

31.0Bparameters

3.8B

Phi-3.5-mini-instruct

31.0B

Qwen3 VL 30B A3B Thinking

Context Window

Maximum input and output token capacity

Qwen3 VL 30B A3B Thinking accepts 131,072 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. Phi-3.5-mini-instruct can generate longer responses up to 128,000 tokens, while Qwen3 VL 30B A3B Thinking is limited to 32,768 tokens.

Phi-3.5-mini-instruct

Input128,000 tokens

Output128,000 tokens

Qwen3 VL 30B A3B Thinking

Input131,072 tokens

Output32,768 tokens

Tue May 12 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 30B A3B Thinking supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.

Qwen3 VL 30B A3B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Phi-3.5-mini-instruct

Text

Images

Audio

Video

Qwen3 VL 30B A3B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

Phi-3.5-mini-instruct is licensed under MIT, while Qwen3 VL 30B A3B Thinking uses Apache 2.0.

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

Phi-3.5-mini-instruct

MIT

Open weights

Qwen3 VL 30B A3B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Phi-3.5-mini-instruct was released on 2024-08-23, while Qwen3 VL 30B A3B Thinking was released on 2025-09-22.

Qwen3 VL 30B A3B Thinking is 13 months newer than Phi-3.5-mini-instruct.

Phi-3.5-mini-instruct

Aug 23, 2024

1.7 years ago

Qwen3 VL 30B A3B Thinking

Sep 22, 2025

7 months ago

1.1yr newer

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

Phi-3.5-mini-instruct is available from Azure. Qwen3 VL 30B A3B Thinking is available from Novita, DeepInfra.

Phi-3.5-mini-instruct

Azure

Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M

Qwen3 VL 30B A3B Thinking

Novita

Input Price:Input: $0.20/1MOutput Price:Output: $1.00/1M

Deepinfra

Input Price:Input: $0.29/1MOutput Price:Output: $0.99/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Phi-3.5-mini-instruct

View details

Microsoft

Less expensive input tokens

Less expensive output tokens

Qwen3 VL 30B A3B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (131,072 tokens)

Supports multimodal inputs

Higher GPQA score (74.4% vs 30.4%)

Higher MMLU score (87.6% vs 69.0%)

Higher MMLU-Pro score (80.5% vs 47.4%)

Detailed Comparison

AI Model Comparison Table
Feature	Phi-3.5-mini-instruct	Qwen3 VL 30B A3B Thinking

FAQ

Common questions about Phi-3.5-mini-instruct vs Qwen3 VL 30B A3B Thinking.

Which is better, Phi-3.5-mini-instruct or Qwen3 VL 30B A3B Thinking?

Qwen3 VL 30B A3B Thinking significantly outperforms across most benchmarks. Phi-3.5-mini-instruct is made by Microsoft and Qwen3 VL 30B A3B Thinking 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-mini-instruct compare to Qwen3 VL 30B A3B Thinking in benchmarks?

Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%. Qwen3 VL 30B A3B Thinking scores DocVQAtest: 95.0%, ScreenSpot: 94.7%, MMLU-Redux: 90.9%, MMBench-V1.1: 88.9%, MMLU: 87.6%.

Is Phi-3.5-mini-instruct cheaper than Qwen3 VL 30B A3B Thinking?

Phi-3.5-mini-instruct is 2.0x cheaper for input tokens. Phi-3.5-mini-instruct costs $0.10/M input and $0.10/M output via azure. Qwen3 VL 30B A3B Thinking costs $0.20/M input and $0.99/M output via novita.

What are the context window sizes for Phi-3.5-mini-instruct and Qwen3 VL 30B A3B Thinking?

Phi-3.5-mini-instruct supports 128K tokens and Qwen3 VL 30B A3B Thinking 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 Phi-3.5-mini-instruct and Qwen3 VL 30B A3B Thinking?

Key differences include context window (128K vs 131K), input pricing ($0.10 vs $0.20/M), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Phi-3.5-mini-instruct and Qwen3 VL 30B A3B Thinking?

Phi-3.5-mini-instruct is developed by Microsoft and Qwen3 VL 30B A3B Thinking is developed by Alibaba Cloud / Qwen Team.