| Signal | DeepSeek V3.2 Exp | Delta | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 70 | +4 | |
Pricing | 0 | -2 | |
Context window size | 83 | +2 | |
Recency | 100 | +1 | |
Output Capacity | 80 | +5 | |
| Overall Result | 4 wins | of 6 | 2 wins |
9
days higher
4
days
17
days higher
DeepSeek
Alibaba
DeepSeek V3.2 Exp saves you $108.50/month
That's $1302.00/year compared to Qwen3 VL 235B A22B Thinking at your current usage level of 100K calls/month.
| Metric | DeepSeek V3.2 Exp | Qwen3 VL 235B A22B Thinking | Winner |
|---|---|---|---|
| Overall Score | 77 | 77 | -- |
| Rank | #98 | #99 | DeepSeek V3.2 Exp |
| Quality Rank | #98 | #99 | DeepSeek V3.2 Exp |
| Adoption Rank | #98 | #99 | DeepSeek V3.2 Exp |
| Parameters | -- | 235B | -- |
| Context Window | 164K | 131K | DeepSeek V3.2 Exp |
| Pricing | $0.27/$0.41/M | $0.26/$2.60/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3 VL 235B A22B Thinking |
| Benchmarks | 70 | 66 | DeepSeek V3.2 Exp |
| Pricing | 0 | 3 | Qwen3 VL 235B A22B Thinking |
| Context window size | 83 | 81 | DeepSeek V3.2 Exp |
| Recency | 100 | 99 | DeepSeek V3.2 Exp |
| Output Capacity | 80 | 75 | DeepSeek V3.2 Exp |
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 #98), placing it in the top 67% of all 290 models tracked.
Scores 77/100 (rank #99), placing it in the top 66% 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.
DeepSeek V3.2 Exp offers 76% better value per quality point. At 1M tokens/day, you'd spend $10.20/month with DeepSeek V3.2 Exp vs $42.90/month with Qwen3 VL 235B A22B Thinking - a $32.70 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. DeepSeek V3.2 Exp 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 ($0.41/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (77/100) 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
DeepSeek V3.2 Exp and Qwen3 VL 235B A22B Thinking are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
DeepSeek V3.2 Exp
Marginally better benchmark scores; both are excellent
Best for Cost
DeepSeek V3.2 Exp
76% lower pricing; better value at scale
Best for Reliability
DeepSeek V3.2 Exp
Higher uptime and faster response speeds
Best for Prototyping
DeepSeek V3.2 Exp
Stronger community support and better developer experience
Best for Production
DeepSeek V3.2 Exp
Wider enterprise adoption and proven at scale
by DeepSeek
| Capability | DeepSeek V3.2 Exp | Qwen3 VL 235B A22B Thinking |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
DeepSeek
Alibaba
DeepSeek V3.2 Exp saves you $2.61/month
That's 73% 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 | DeepSeek V3.2 Exp | Qwen3 VL 235B A22B Thinking |
|---|---|---|
| Context Window | 164K | 131K |
| Max Output Tokens | 65,536 | 32,768 |
| Open Source | Yes | Yes |
| Created | Sep 29, 2025 | Sep 23, 2025 |
Both DeepSeek V3.2 Exp and Qwen3 VL 235B A22B Thinking score 77/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
DeepSeek V3.2 Exp is ranked #98 and Qwen3 VL 235B A22B Thinking is ranked #99 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.
DeepSeek V3.2 Exp is cheaper at $0.41/M output tokens vs Qwen3 VL 235B A22B Thinking's $2.60/M output tokens - 6.3x more expensive. Input token pricing: DeepSeek V3.2 Exp at $0.27/M vs Qwen3 VL 235B A22B Thinking at $0.26/M.
DeepSeek V3.2 Exp 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.