| Signal | Llama 3.3 Nemotron Super 49B V1.5 | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 59 | -8 | |
Pricing | 0 | +0 | |
Context window size | 81 | -14 | |
Recency | 100 | -- | |
Output Capacity | 20 | -60 | |
| Overall Result | 1 wins | of 6 | 4 wins |
0
days ranked higher
0
days
30
days ranked higher
NVIDIA
Alibaba
Qwen3.5-Flash saves you $10.50/month
That's $126.00/year compared to Llama 3.3 Nemotron Super 49B V1.5 at your current usage level of 100K calls/month.
| Metric | Llama 3.3 Nemotron Super 49B V1.5 | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 69 | 79 | Qwen3.5-Flash |
| Rank | #163 | #80 | Qwen3.5-Flash |
| Quality Rank | #163 | #80 | Qwen3.5-Flash |
| Adoption Rank | #163 | #80 | Qwen3.5-Flash |
| Parameters | 49B | -- | -- |
| Context Window | 131K | 1000K | Qwen3.5-Flash |
| Pricing | $0.10/$0.40/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-Flash |
| Benchmarks | 59 | 67 | Qwen3.5-Flash |
| Pricing | 0 | 0 | Llama 3.3 Nemotron Super 49B V1.5 |
| Context window size | 81 | 95 | Qwen3.5-Flash |
| Recency | 100 | 100 | Llama 3.3 Nemotron Super 49B V1.5 |
| Output Capacity | 20 | 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 69/100 (rank #163), placing it in the top 44% 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 11-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Qwen3.5-Flash offers 35% better value per quality point. At 1M tokens/day, you'd spend $4.88/month with Qwen3.5-Flash vs $7.50/month with Llama 3.3 Nemotron Super 49B V1.5 - a $2.62 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 Llama 3.3 Nemotron Super 49B V1.5 with a significant 10.800000000000011-point lead. For most general use cases, Qwen3.5-Flash is the stronger choice. However, Llama 3.3 Nemotron Super 49B V1.5 may still excel in niche scenarios.
Best for Quality
Llama 3.3 Nemotron Super 49B V1.5
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-Flash
35% lower pricing; better value at scale
Best for Reliability
Llama 3.3 Nemotron Super 49B V1.5
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.3 Nemotron Super 49B V1.5
Stronger community support and better developer experience
Best for Production
Llama 3.3 Nemotron Super 49B V1.5
Wider enterprise adoption and proven at scale
by NVIDIA
| Capability | Llama 3.3 Nemotron Super 49B V1.5 | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
NVIDIA
Alibaba
Qwen3.5-Flash saves you $0.2310/month
That's 35% cheaper than Llama 3.3 Nemotron Super 49B V1.5 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 3.3 Nemotron Super 49B V1.5 | Qwen3.5-Flash |
|---|---|---|
| Context Window | 131K | 1M |
| Max Output Tokens | -- | 65,536 |
| Open Source | Yes | No |
| Created | Oct 10, 2025 | Feb 25, 2026 |
Qwen3.5-Flash scores 79/100 (rank #80) compared to Llama 3.3 Nemotron Super 49B V1.5's 69/100 (rank #163), giving it a 11-point advantage. Qwen3.5-Flash is the stronger overall choice, though Llama 3.3 Nemotron Super 49B V1.5 may excel in specific areas like certain benchmarks.
Llama 3.3 Nemotron Super 49B V1.5 is ranked #163 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 3.3 Nemotron Super 49B V1.5's $0.40/M output tokens - 1.5x more expensive. Input token pricing: Llama 3.3 Nemotron Super 49B V1.5 at $0.10/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 3.3 Nemotron Super 49B V1.5's 131,072 tokens. A larger context window means the model can process longer documents and conversations.