| Signal | Qwen3.5 Plus 2026-02-15 | Delta | Qwen3 VL 32B Instruct |
|---|---|---|---|
Capabilities | 83 | +17 | |
Pricing | 2 | +1 | |
Context window size | 95 | +14 | |
Recency | 100 | -- | |
Output Capacity | 80 | +5 | |
| Overall Result | 4 wins | of 5 | 0 wins |
22
days ranked higher
4
days
4
days ranked higher
Alibaba
Alibaba
Qwen3 VL 32B Instruct saves you $72.80/month
That's $873.60/year compared to Qwen3.5 Plus 2026-02-15 at your current usage level of 100K calls/month.
| Metric | Qwen3.5 Plus 2026-02-15 | Qwen3 VL 32B Instruct | Winner |
|---|---|---|---|
| Overall Score | 85 | 81 | Qwen3.5 Plus 2026-02-15 |
| Rank | #30 | #71 | Qwen3.5 Plus 2026-02-15 |
| Quality Rank | #30 | #71 | Qwen3.5 Plus 2026-02-15 |
| Adoption Rank | #30 | #71 | Qwen3.5 Plus 2026-02-15 |
| Parameters | -- | 32B | -- |
| Context Window | 1000K | 131K | Qwen3.5 Plus 2026-02-15 |
| Pricing | $0.26/$1.56/M | $0.10/$0.42/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 67 | Qwen3.5 Plus 2026-02-15 |
| Pricing | 2 | 0 | Qwen3.5 Plus 2026-02-15 |
| Context window size | 95 | 81 | Qwen3.5 Plus 2026-02-15 |
| Recency | 100 | 100 | Qwen3.5 Plus 2026-02-15 |
| Output Capacity | 80 | 75 | Qwen3.5 Plus 2026-02-15 |
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 85/100 (rank #30), placing it in the top 90% of all 290 models tracked.
Scores 81/100 (rank #71), placing it in the top 76% of all 290 models tracked.
With only a 4-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.
Qwen3 VL 32B Instruct offers 71% better value per quality point. At 1M tokens/day, you'd spend $7.80/month with Qwen3 VL 32B Instruct vs $27.30/month with Qwen3.5 Plus 2026-02-15 - a $19.50 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. Qwen3 VL 32B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.42/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (85/100) 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
Qwen3.5 Plus 2026-02-15 has a moderate advantage with a 4.099999999999994-point lead in composite score. It wins on more signal dimensions, but Qwen3 VL 32B Instruct has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Qwen3.5 Plus 2026-02-15
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3 VL 32B Instruct
71% lower pricing; better value at scale
Best for Reliability
Qwen3.5 Plus 2026-02-15
Higher uptime and faster response speeds
Best for Prototyping
Qwen3.5 Plus 2026-02-15
Stronger community support and better developer experience
Best for Production
Qwen3.5 Plus 2026-02-15
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3.5 Plus 2026-02-15 | Qwen3 VL 32B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Alibaba
Alibaba
Qwen3 VL 32B Instruct saves you $1.65/month
That's 71% cheaper than Qwen3.5 Plus 2026-02-15 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 | Qwen3.5 Plus 2026-02-15 | Qwen3 VL 32B Instruct |
|---|---|---|
| Context Window | 1M | 131K |
| Max Output Tokens | 65,536 | 32,768 |
| Open Source | No | Yes |
| Created | Feb 16, 2026 | Oct 23, 2025 |
Qwen3.5 Plus 2026-02-15 scores 85/100 (rank #30) compared to Qwen3 VL 32B Instruct's 81/100 (rank #71), giving it a 4-point advantage. Qwen3.5 Plus 2026-02-15 is the stronger overall choice, though Qwen3 VL 32B Instruct may excel in specific areas like cost efficiency.
Qwen3.5 Plus 2026-02-15 is ranked #30 and Qwen3 VL 32B Instruct is ranked #71 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.
Qwen3 VL 32B Instruct is cheaper at $0.42/M output tokens vs Qwen3.5 Plus 2026-02-15's $1.56/M output tokens - 3.8x more expensive. Input token pricing: Qwen3.5 Plus 2026-02-15 at $0.26/M vs Qwen3 VL 32B Instruct at $0.10/M.
Qwen3.5 Plus 2026-02-15 has a larger context window of 1,000,000 tokens compared to Qwen3 VL 32B Instruct's 131,072 tokens. A larger context window means the model can process longer documents and conversations.