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Phi-4-multimodal-instruct vs Qwen3 VL 8B Thinking

Qwen3 VL 8B Thinking leads the LLM Stats Score 16.3 to 2.9. Phi-4-multimodal-instruct is 10.5x cheaper per token.

Microsoft · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3 VL 8B Thinking leads the overall LLM Stats Score 16.3 to 2.9, ranking #222 overall.

In the 5 individual benchmarks reported for both models, Qwen3 VL 8B Thinking wins 4; this is a narrower head-to-head signal than the composite indexes.

On price, Phi-4-multimodal-instruct is roughly 10.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Qwen3 VL 8B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Phi-4-multimodal-instruct

  • cost matters — it's about 10.5x cheaper per token

Choose Qwen3 VL 8B Thinking

  • overall performance matters — it scores 16.3 and ranks #222 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 4 of 5 exact shared results
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

Core performance indexes
2.9
#310
16.3
#222
-0.1
#318
17.3
#209
Cost, coverage & limits
Benchmark wins
1 of 5
4 of 5
Input price
$0.05 / M
$0.18 / M
Output price
$0.10 / M
$2.09 / M
Context window
128,000
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
Phi-4-multimodal-instruct
Qwen3 VL 8B Thinking
1.6#171
10.7#115
3.6#140
13.4#97
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for Phi-4-multimodal-instruct · 50 for Qwen3 VL 8B Thinking

5 shared

Phi-4-multimodal-instruct outperforms in 1 benchmarks (OCRBench), while Qwen3 VL 8B Thinking is better at 4 benchmarks (AI2D, BLINK, MMMU-Pro, Video-MME).

Qwen3 VL 8B Thinking significantly outperforms across most benchmarks.

Thu Sep 10 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Phi-4-multimodal-instruct costs less

For input processing, Phi-4-multimodal-instruct ($0.05/1M tokens) is 3.6x cheaper than Qwen3 VL 8B Thinking ($0.18/1M tokens).

For output processing, Phi-4-multimodal-instruct ($0.10/1M tokens) is 20.9x cheaper than Qwen3 VL 8B Thinking ($2.09/1M tokens).

In conclusion, Qwen3 VL 8B Thinking is more expensive than Phi-4-multimodal-instruct.*

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

Lowest available price from all providers
Thu Sep 10 2026 • llm-stats.com
Microsoft
Phi-4-multimodal-instruct
Input tokens$0.05
Output tokens$0.10
Best providerDeepinfra
Alibaba Cloud / Qwen Team
Qwen3 VL 8B Thinking
Input tokens$0.18
Output tokens$2.09
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

3.4B diff

Qwen3 VL 8B Thinking has 3.4B more parameters than Phi-4-multimodal-instruct, making it 60.7% larger.

Microsoft
Phi-4-multimodal-instruct
5.6Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 8B Thinking
9.0Bparameters
5.6B
Phi-4-multimodal-instruct
9.0B
Qwen3 VL 8B Thinking

Context Window

Maximum input and output token capacity

Qwen3 VL 8B Thinking accepts 262,144 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while Phi-4-multimodal-instruct is limited to 128,000 tokens.

Microsoft
Phi-4-multimodal-instruct
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 8B Thinking
Input262,144 tokens
Output262,144 tokens
Thu Sep 10 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Phi-4-multimodal-instruct and Qwen3 VL 8B Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Phi-4-multimodal-instruct

Text
Images
Audio
Video

Qwen3 VL 8B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Phi-4-multimodal-instruct is licensed under MIT, while Qwen3 VL 8B Thinking uses Apache 2.0.

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

Phi-4-multimodal-instruct

MIT

Open weights

Qwen3 VL 8B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Phi-4-multimodal-instruct was released on 2025-02-01, while Qwen3 VL 8B Thinking was released on 2025-09-22.

Qwen3 VL 8B Thinking is 8 months newer than Phi-4-multimodal-instruct.

Phi-4-multimodal-instruct

Feb 1, 2025

1.6 years ago

Qwen3 VL 8B Thinking

Sep 22, 2025

11 months ago

7mo newer

Knowledge Cutoff

When training data ends

Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while Qwen3 VL 8B Thinking's cutoff date is not specified.

We can confirm Phi-4-multimodal-instruct's training data extends to 2024-06-01, but cannot make a direct comparison without Qwen3 VL 8B Thinking's cutoff date.

Phi-4-multimodal-instruct

Jun 2024

Qwen3 VL 8B Thinking

Provider Availability

Phi-4-multimodal-instruct is available from DeepInfra. Qwen3 VL 8B Thinking is available from DeepInfra.

Phi-4-multimodal-instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.05/1MOutput Price:Output: $0.10/1M

Qwen3 VL 8B Thinking

deepinfra logo
Deepinfra
Input Price:Input: $0.18/1MOutput Price:Output: $2.09/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Phi-4-multimodal-instruct and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.

Phi-4-multimodal-instruct
✓ Preferred
Qwen3 VL 8B Thinking
Open in Playground

FAQ

Common questions about Phi-4-multimodal-instruct vs Qwen3 VL 8B Thinking.

Which is better, Phi-4-multimodal-instruct or Qwen3 VL 8B Thinking?

Qwen3 VL 8B Thinking leads the LLM Stats Score 16.3 to 2.9. Phi-4-multimodal-instruct is made by Microsoft and Qwen3 VL 8B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Phi-4-multimodal-instruct compare to Qwen3 VL 8B Thinking in benchmarks?

Phi-4-multimodal-instruct scores ScienceQA Visual: 97.5%, DocVQA: 93.2%, MMBench: 86.7%, POPE: 85.6%, OCRBench: 84.4%. Qwen3 VL 8B Thinking scores DocVQAtest: 95.3%, ScreenSpot: 93.6%, MMLU-Redux: 88.8%, MMBench-V1.1: 87.5%, InfoVQAtest: 86.0%.

Is Phi-4-multimodal-instruct cheaper than Qwen3 VL 8B Thinking?

Phi-4-multimodal-instruct is 3.6x cheaper for input tokens. Phi-4-multimodal-instruct costs $0.05/M input and $0.10/M output via deepinfra. Qwen3 VL 8B Thinking costs $0.18/M input and $2.09/M output via deepinfra.

What are the context window sizes for Phi-4-multimodal-instruct and Qwen3 VL 8B Thinking?

Phi-4-multimodal-instruct supports 128K tokens and Qwen3 VL 8B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Phi-4-multimodal-instruct and Qwen3 VL 8B Thinking?

Key differences include LLM Stats Score (2.9 vs 16.3), context window (128K vs 262K), input pricing ($0.05 vs $0.18/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Phi-4-multimodal-instruct and Qwen3 VL 8B Thinking?

Phi-4-multimodal-instruct is developed by Microsoft and Qwen3 VL 8B Thinking is developed by Alibaba Cloud / Qwen Team.