| Signal | Qwen3.5-27B | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 83 | -- | |
Benchmarks | 68 | +1 | |
Pricing | 2 | +1 | |
Context window size | 86 | -9 | |
Recency | 100 | -- | |
Output Capacity | 80 | -- | |
| Overall Result | 2 wins | of 6 | 1 wins |
7
days higher
4
days
19
days higher
Alibaba
Alibaba
Qwen3.5-Flash saves you $78.00/month
That's $936.00/year compared to Qwen3.5-27B at your current usage level of 100K calls/month.
| Metric | Qwen3.5-27B | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 79 | 79 | Qwen3.5-Flash |
| Rank | #86 | #85 | Qwen3.5-Flash |
| Quality Rank | #86 | #85 | Qwen3.5-Flash |
| Adoption Rank | #86 | #85 | Qwen3.5-Flash |
| Parameters | 27B | -- | -- |
| Context Window | 262K | 1000K | Qwen3.5-Flash |
| Pricing | $0.20/$1.56/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 83 | Qwen3.5-27B |
| Benchmarks | 68 | 67 | Qwen3.5-27B |
| Pricing | 2 | 0 | Qwen3.5-27B |
| Context window size | 86 | 95 | Qwen3.5-Flash |
| Recency | 100 | 100 | Qwen3.5-27B |
| Output Capacity | 80 | 80 | Qwen3.5-27B |
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 79/100 (rank #86), placing it in the top 71% of all 290 models tracked.
Scores 79/100 (rank #85), placing it in the top 71% 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.
Qwen3.5-Flash offers 81% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $26.33/month with Qwen3.5-27B - a $21.45 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. Qwen3.5-Flash 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 ($0.26/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (79/100) 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
Qwen3.5-27B and Qwen3.5-Flash are extremely close in overall performance (only 0.30000000000001137 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Qwen3.5-27B
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
81% lower pricing; better value at scale
Best for Reliability
Qwen3.5-27B
Higher uptime and faster response speeds
Best for Prototyping
Qwen3.5-27B
Stronger community support and better developer experience
Best for Production
Qwen3.5-27B
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3.5-27B | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Alibaba
Alibaba
Qwen3.5-Flash saves you $1.79/month
That's 81% cheaper than Qwen3.5-27B 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 | Qwen3.5-27B | Qwen3.5-Flash |
|---|---|---|
| Context Window | 262K | 1M |
| Max Output Tokens | 65,536 | 65,536 |
| Open Source | Yes | No |
| Created | Feb 25, 2026 | Feb 25, 2026 |
Qwen3.5-Flash scores 79/100 (rank #85) compared to Qwen3.5-27B's 79/100 (rank #86), giving it a 0-point advantage. Qwen3.5-Flash is the stronger overall choice, though Qwen3.5-27B may excel in specific areas like certain benchmarks.
Qwen3.5-27B is ranked #86 and Qwen3.5-Flash is ranked #85 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.
Qwen3.5-Flash is cheaper at $0.26/M output tokens vs Qwen3.5-27B's $1.56/M output tokens - 6.0x more expensive. Input token pricing: Qwen3.5-27B at $0.20/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to Qwen3.5-27B's 262,144 tokens. A larger context window means the model can process longer documents and conversations.