| Signal | Phi 4 | Delta | Qwen2.5 VL 72B Instruct |
|---|---|---|---|
Capabilities | 33 | -17 | |
Benchmarks | 68 | +68 | |
Pricing | 0 | -1 | |
Context window size | 67 | -5 | |
Recency | 53 | -4 | |
Output Capacity | 70 | -5 | |
| Overall Result | 1 wins | of 6 | 5 wins |
5
days ranked higher
3
days
22
days ranked higher
Microsoft
Alibaba
Phi 4 saves you $106.50/month
That's $1278.00/year compared to Qwen2.5 VL 72B Instruct at your current usage level of 100K calls/month.
| Metric | Phi 4 | Qwen2.5 VL 72B Instruct | Winner |
|---|---|---|---|
| Overall Score | 60 | 60 | Qwen2.5 VL 72B Instruct |
| Rank | #223 | #218 | Qwen2.5 VL 72B Instruct |
| Quality Rank | #223 | #218 | Qwen2.5 VL 72B Instruct |
| Adoption Rank | #223 | #218 | Qwen2.5 VL 72B Instruct |
| Parameters | -- | 72B | -- |
| Context Window | 16K | 33K | Qwen2.5 VL 72B Instruct |
| Pricing | $0.07/$0.14/M | $0.80/$0.80/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 50 | Qwen2.5 VL 72B Instruct |
| Benchmarks | 68 | -- | Phi 4 |
| Pricing | 0 | 1 | Qwen2.5 VL 72B Instruct |
| Context window size | 67 | 72 | Qwen2.5 VL 72B Instruct |
| Recency | 53 | 57 | Qwen2.5 VL 72B Instruct |
| Output Capacity | 70 | 75 | Qwen2.5 VL 72B Instruct |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 60/100 (rank #223), placing it in the top 23% of all 290 models tracked.
Scores 60/100 (rank #218), placing it in the top 25% of all 290 models tracked.
With only a 1-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Phi 4 offers 87% better value per quality point. At 1M tokens/day, you'd spend $3.08/month with Phi 4 vs $24.00/month with Qwen2.5 VL 72B Instruct - a $20.92 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Phi 4 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (33K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.14/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (60/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Phi 4 and Qwen2.5 VL 72B Instruct are extremely close in overall performance (only 0.6999999999999957 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Phi 4
Marginally better benchmark scores; both are excellent
Best for Cost
Phi 4
87% lower pricing; better value at scale
Best for Reliability
Phi 4
Higher uptime and faster response speeds
Best for Prototyping
Phi 4
Stronger community support and better developer experience
Best for Production
Phi 4
Wider enterprise adoption and proven at scale
by Microsoft
| Capability | Phi 4 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Microsoft
Alibaba
Phi 4 saves you $2.11/month
That's 88% cheaper than Qwen2.5 VL 72B Instruct at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Phi 4 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Context Window | 16K | 33K |
| Max Output Tokens | 16,384 | 32,768 |
| Open Source | Yes | Yes |
| Created | Jan 10, 2025 | Feb 1, 2025 |
Qwen2.5 VL 72B Instruct scores 60/100 (rank #218) compared to Phi 4's 60/100 (rank #223), giving it a 1-point advantage. Qwen2.5 VL 72B Instruct is the stronger overall choice, though Phi 4 may excel in specific areas like cost efficiency.
Phi 4 is ranked #223 and Qwen2.5 VL 72B Instruct is ranked #218 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Phi 4 is cheaper at $0.14/M output tokens vs Qwen2.5 VL 72B Instruct's $0.80/M output tokens - 5.7x more expensive. Input token pricing: Phi 4 at $0.07/M vs Qwen2.5 VL 72B Instruct at $0.80/M.
Qwen2.5 VL 72B Instruct has a larger context window of 32,768 tokens compared to Phi 4's 16,384 tokens. A larger context window means the model can process longer documents and conversations.