| Signal | Qwen3 VL 235B A22B Thinking | Delta | R1 0528 |
|---|---|---|---|
Capabilities | 83 | +17 | |
Benchmarks | 66 | -13 | |
Pricing | 3 | +0 | |
Context window size | 81 | -2 | |
Recency | 100 | +22 | |
Output Capacity | 75 | -5 | |
| Overall Result | 3 wins | of 6 | 3 wins |
8
days ranked higher
3
days
19
days ranked higher
Alibaba
DeepSeek
R1 0528 saves you $3.50/month
That's $42.00/year compared to Qwen3 VL 235B A22B Thinking at your current usage level of 100K calls/month.
| Metric | Qwen3 VL 235B A22B Thinking | R1 0528 | Winner |
|---|---|---|---|
| Overall Score | 77 | 78 | R1 0528 |
| Rank | #91 | #89 | R1 0528 |
| Quality Rank | #91 | #89 | R1 0528 |
| Adoption Rank | #91 | #89 | R1 0528 |
| Parameters | 235B | -- | -- |
| Context Window | 131K | 164K | R1 0528 |
| Pricing | $0.26/$2.60/M | $0.45/$2.15/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 67 | Qwen3 VL 235B A22B Thinking |
| Benchmarks | 66 | 80 | R1 0528 |
| Pricing | 3 | 2 | Qwen3 VL 235B A22B Thinking |
| Context window size | 81 | 83 | R1 0528 |
| Recency | 100 | 79 | Qwen3 VL 235B A22B Thinking |
| Output Capacity | 75 | 80 | R1 0528 |
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 77/100 (rank #91), placing it in the top 69% of all 290 models tracked.
Scores 78/100 (rank #89), placing it in the top 70% of all 290 models tracked.
With only a 0-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.
R1 0528 offers 9% better value per quality point. At 1M tokens/day, you'd spend $39.00/month with R1 0528 vs $42.90/month with Qwen3 VL 235B A22B Thinking - a $3.90 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. R1 0528 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (164K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($2.15/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (78/100) 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
Qwen3 VL 235B A22B Thinking and R1 0528 are extremely close in overall performance (only 0.29999999999999716 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Qwen3 VL 235B A22B Thinking
Marginally better benchmark scores; both are excellent
Best for Cost
R1 0528
9% lower pricing; better value at scale
Best for Reliability
Qwen3 VL 235B A22B Thinking
Higher uptime and faster response speeds
Best for Prototyping
Qwen3 VL 235B A22B Thinking
Stronger community support and better developer experience
Best for Production
Qwen3 VL 235B A22B Thinking
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3 VL 235B A22B Thinking | R1 0528 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Alibaba
DeepSeek
R1 0528 saves you $0.1980/month
That's 6% cheaper than Qwen3 VL 235B A22B Thinking 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 | Qwen3 VL 235B A22B Thinking | R1 0528 |
|---|---|---|
| Context Window | 131K | 164K |
| Max Output Tokens | 32,768 | 65,536 |
| Open Source | Yes | Yes |
| Created | Sep 23, 2025 | May 28, 2025 |
R1 0528 scores 78/100 (rank #89) compared to Qwen3 VL 235B A22B Thinking's 77/100 (rank #91), giving it a 0-point advantage. R1 0528 is the stronger overall choice, though Qwen3 VL 235B A22B Thinking may excel in specific areas like certain benchmarks.
Qwen3 VL 235B A22B Thinking is ranked #91 and R1 0528 is ranked #89 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.
R1 0528 is cheaper at $2.15/M output tokens vs Qwen3 VL 235B A22B Thinking's $2.60/M output tokens - 1.2x more expensive. Input token pricing: Qwen3 VL 235B A22B Thinking at $0.26/M vs R1 0528 at $0.45/M.
R1 0528 has a larger context window of 163,840 tokens compared to Qwen3 VL 235B A22B Thinking's 131,072 tokens. A larger context window means the model can process longer documents and conversations.