| Signal | Llama 4 Maverick | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 80 | +13 | |
Pricing | 1 | +0 | |
Context window size | 96 | +0 | |
Recency | 69 | -31 | |
Output Capacity | 70 | -10 | |
| Overall Result | 3 wins | of 6 | 3 wins |
3
days ranked higher
2
days
25
days ranked higher
Meta
Alibaba
Qwen3.5-Flash saves you $25.50/month
That's $306.00/year compared to Llama 4 Maverick at your current usage level of 100K calls/month.
| Metric | Llama 4 Maverick | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 77 | 79 | Qwen3.5-Flash |
| Rank | #99 | #80 | Qwen3.5-Flash |
| Quality Rank | #99 | #80 | Qwen3.5-Flash |
| Adoption Rank | #99 | #80 | Qwen3.5-Flash |
| Parameters | -- | -- | -- |
| Context Window | 1049K | 1000K | Llama 4 Maverick |
| Pricing | $0.15/$0.60/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-Flash |
| Benchmarks | 80 | 67 | Llama 4 Maverick |
| Pricing | 1 | 0 | Llama 4 Maverick |
| Context window size | 96 | 95 | Llama 4 Maverick |
| Recency | 69 | 100 | Qwen3.5-Flash |
| Output Capacity | 70 | 80 | 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 77/100 (rank #99), placing it in the top 66% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
With only a 3-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 57% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $11.25/month with Llama 4 Maverick - a $6.38 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 (1049K 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
Llama 4 Maverick and Qwen3.5-Flash are extremely close in overall performance (only 2.700000000000003 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 4 Maverick
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
57% lower pricing; better value at scale
Best for Reliability
Llama 4 Maverick
Higher uptime and faster response speeds
Best for Prototyping
Llama 4 Maverick
Stronger community support and better developer experience
Best for Production
Llama 4 Maverick
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
Alibaba
Qwen3.5-Flash saves you $0.5610/month
That's 57% cheaper than Llama 4 Maverick 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 Maverick | Qwen3.5-Flash |
|---|---|---|
| Context Window | 1.0M | 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 Maverick's 77/100 (rank #99), giving it a 3-point advantage. Qwen3.5-Flash is the stronger overall choice, though Llama 4 Maverick may excel in specific areas like certain benchmarks.
Llama 4 Maverick is ranked #99 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 Maverick's $0.60/M output tokens - 2.3x more expensive. Input token pricing: Llama 4 Maverick at $0.15/M vs Qwen3.5-Flash at $0.07/M.
Llama 4 Maverick has a larger context window of 1,048,576 tokens compared to Qwen3.5-Flash's 1,000,000 tokens. A larger context window means the model can process longer documents and conversations.