| Signal | Llama 4 Scout | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 67 | -17 | |
Pricing | 0 | -- | |
Context window size | 88 | -8 | |
Recency | 69 | -31 | |
Output Capacity | 70 | -10 | |
Benchmarks | 0 | -67 | |
| Overall Result | 0 wins | of 6 | 5 wins |
0
days ranked higher
0
days
30
days ranked higher
Meta
Alibaba
Qwen3.5-Flash saves you $3.50/month
That's $42.00/year compared to Llama 4 Scout at your current usage level of 100K calls/month.
| Metric | Llama 4 Scout | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 72 | 79 | Qwen3.5-Flash |
| Rank | #144 | #80 | Qwen3.5-Flash |
| Quality Rank | #144 | #80 | Qwen3.5-Flash |
| Adoption Rank | #144 | #80 | Qwen3.5-Flash |
| Parameters | -- | -- | -- |
| Context Window | 328K | 1000K | Qwen3.5-Flash |
| Pricing | $0.08/$0.30/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-Flash |
| Pricing | 0 | 0 | Llama 4 Scout |
| Context window size | 88 | 95 | Qwen3.5-Flash |
| Recency | 69 | 100 | Qwen3.5-Flash |
| Output Capacity | 70 | 80 | Qwen3.5-Flash |
| Benchmarks | -- | 67 | 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 72/100 (rank #144), placing it in the top 51% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
Qwen3.5-Flash has a 7-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 14% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $5.70/month with Llama 4 Scout - a $0.83 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 has a moderate advantage with a 7.400000000000006-point lead in composite score. It wins on more signal dimensions, but Llama 4 Scout has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Llama 4 Scout
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
14% lower pricing; better value at scale
Best for Reliability
Llama 4 Scout
Higher uptime and faster response speeds
Best for Prototyping
Llama 4 Scout
Stronger community support and better developer experience
Best for Production
Llama 4 Scout
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
Alibaba
Qwen3.5-Flash saves you $0.0750/month
That's 15% cheaper than Llama 4 Scout 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 | Llama 4 Scout | Qwen3.5-Flash |
|---|---|---|
| Context Window | 328K | 1M |
| Max Output Tokens | 16,384 | 65,536 |
| Open Source | Yes | No |
| Created | Apr 5, 2025 | Feb 25, 2026 |
Qwen3.5-Flash scores 79/100 (rank #80) compared to Llama 4 Scout's 72/100 (rank #144), giving it a 7-point advantage. Qwen3.5-Flash is the stronger overall choice, though Llama 4 Scout may excel in specific areas like certain benchmarks.
Llama 4 Scout is ranked #144 and Qwen3.5-Flash is ranked #80 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 Llama 4 Scout's $0.30/M output tokens - 1.2x more expensive. Input token pricing: Llama 4 Scout at $0.08/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to Llama 4 Scout's 327,680 tokens. A larger context window means the model can process longer documents and conversations.