| Signal | Qwen3.5-Flash | Delta | autofixer-01 |
|---|---|---|---|
Capabilities | 83 | +67 | |
Benchmarks | 67 | +67 | |
Pricing | 0 | +0 | |
Context window size | 95 | +95 | |
Recency | 100 | -- | |
Output Capacity | 80 | +60 | |
| Overall Result | 5 wins | of 6 | 0 wins |
30
days ranked higher
0
days
0
days ranked higher
Alibaba
Vercel
autofixer-01 saves you $19.50/month
That's $234.00/year compared to Qwen3.5-Flash at your current usage level of 100K calls/month.
| Metric | Qwen3.5-Flash | autofixer-01 | Winner |
|---|---|---|---|
| Overall Score | 79 | 39 | Qwen3.5-Flash |
| Rank | #80 | #290 | Qwen3.5-Flash |
| Quality Rank | #80 | #290 | Qwen3.5-Flash |
| Adoption Rank | #80 | #290 | Qwen3.5-Flash |
| Parameters | -- | -- | -- |
| Context Window | 1000K | -- | -- |
| Pricing | $0.07/$0.26/M | Free | -- |
| Signal Scores | |||
| Capabilities | 83 | 17 | Qwen3.5-Flash |
| Benchmarks | 67 | -- | Qwen3.5-Flash |
| Pricing | 0 | 0 | Qwen3.5-Flash |
| Context window size | 95 | 0 | Qwen3.5-Flash |
| Recency | 100 | 100 | Qwen3.5-Flash |
| Output Capacity | 80 | 20 | Qwen3.5-Flash |
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 #80), placing it in the top 73% of all 290 models tracked.
Scores 39/100 (rank #290), placing it in the top 0% of all 290 models tracked.
Qwen3.5-Flash has a 41-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
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. autofixer-01 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 ($0.00/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-Flash clearly outperforms autofixer-01 with a significant 40.60000000000001-point lead. For most general use cases, Qwen3.5-Flash is the stronger choice. However, autofixer-01 may still excel in niche scenarios.
Best for Quality
Qwen3.5-Flash
Marginally better benchmark scores; both are excellent
Best for Cost
autofixer-01
100% lower pricing; better value at scale
Best for Reliability
Qwen3.5-Flash
Higher uptime and faster response speeds
Best for Prototyping
Qwen3.5-Flash
Stronger community support and better developer experience
Best for Production
Qwen3.5-Flash
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen3.5-Flash | autofixer-01 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Alibaba
Vercel
autofixer-01 saves you $0.4290/month
That's 100% cheaper than Qwen3.5-Flash 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-Flash | autofixer-01 |
|---|---|---|
| Context Window | 1M | -- |
| Max Output Tokens | 65,536 | -- |
| Open Source | Yes | No |
| Created | Feb 25, 2026 | Oct 1, 2025 |
Qwen3.5-Flash scores 79/100 (rank #80) compared to autofixer-01's 39/100 (rank #290), giving it a 41-point advantage. Qwen3.5-Flash is the stronger overall choice, though autofixer-01 may excel in specific areas like cost efficiency.
Qwen3.5-Flash is ranked #80 and autofixer-01 is ranked #290 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.
autofixer-01 is cheaper at $0.00/M output tokens vs Qwen3.5-Flash's $0.26/M output tokens - 260.0x more expensive. Input token pricing: Qwen3.5-Flash at $0.07/M vs autofixer-01 at $0.00/M.
Context window information is available on the individual model pages.