| Signal | Qwen3.5-Flash | Delta | WizardLM-2 8x22B |
|---|---|---|---|
Capabilities | 83 | +67 | |
Benchmarks | 67 | +67 | |
Pricing | 0 | 0 | |
Context window size | 95 | +19 | |
Recency | 100 | +97 | |
Output Capacity | 80 | +15 | |
| Overall Result | 5 wins | of 6 | 1 wins |
30
days higher
0
days
0
days higher
Alibaba
Microsoft
Qwen3.5-Flash saves you $73.50/month
That's $882.00/year compared to WizardLM-2 8x22B at your current usage level of 100K calls/month.
| Metric | Qwen3.5-Flash | WizardLM-2 8x22B | Winner |
|---|---|---|---|
| Overall Score | 79 | 32 | Qwen3.5-Flash |
| Rank | #85 | #306 | Qwen3.5-Flash |
| Quality Rank | #85 | #306 | Qwen3.5-Flash |
| Adoption Rank | #85 | #306 | Qwen3.5-Flash |
| Parameters | -- | 22B | -- |
| Context Window | 1000K | 66K | Qwen3.5-Flash |
| Pricing | $0.07/$0.26/M | $0.62/$0.62/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 17 | Qwen3.5-Flash |
| Benchmarks | 67 | -- | Qwen3.5-Flash |
| Pricing | 0 | 1 | WizardLM-2 8x22B |
| Context window size | 95 | 76 | Qwen3.5-Flash |
| Recency | 100 | 3 | Qwen3.5-Flash |
| Output Capacity | 80 | 65 | 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 #85), placing it in the top 71% of all 290 models tracked.
Scores 32/100 (rank #306), placing it in the top -5% of all 290 models tracked.
Qwen3.5-Flash has a 48-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 74% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $18.60/month with WizardLM-2 8x22B - a $13.73 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-Flash clearly outperforms WizardLM-2 8x22B with a significant 47.60000000000001-point lead. For most general use cases, Qwen3.5-Flash is the stronger choice. However, WizardLM-2 8x22B may still excel in niche scenarios.
Best for Quality
Qwen3.5-Flash
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
74% 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 | WizardLM-2 8x22B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Alibaba
Microsoft
Qwen3.5-Flash saves you $1.43/month
That's 77% cheaper than WizardLM-2 8x22B 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 | WizardLM-2 8x22B |
|---|---|---|
| Context Window | 1M | 66K |
| Max Output Tokens | 65,536 | 8,000 |
| Open Source | No | Yes |
| Created | Feb 25, 2026 | Apr 16, 2024 |
Qwen3.5-Flash scores 79/100 (rank #85) compared to WizardLM-2 8x22B's 32/100 (rank #306), giving it a 48-point advantage. Qwen3.5-Flash is the stronger overall choice, though WizardLM-2 8x22B may excel in specific areas like certain benchmarks.
Qwen3.5-Flash is ranked #85 and WizardLM-2 8x22B is ranked #306 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 WizardLM-2 8x22B's $0.62/M output tokens - 2.4x more expensive. Input token pricing: Qwen3.5-Flash at $0.07/M vs WizardLM-2 8x22B at $0.62/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to WizardLM-2 8x22B's 65,535 tokens. A larger context window means the model can process longer documents and conversations.