| Signal | Llama 3.3 Nemotron Super 49B V1.5 | Delta | Qwen3 235B A22B Thinking 2507 |
|---|---|---|---|
Capabilities | 67 | -- | |
Benchmarks | 59 | +59 | |
Pricing | 0 | -1 | |
Context window size | 81 | -- | |
Recency | 100 | +12 | |
Output Capacity | 20 | -- | |
| Overall Result | 2 wins | of 6 | 1 wins |
8
days higher
0
days
22
days higher
NVIDIA
Alibaba
Llama 3.3 Nemotron Super 49B V1.5 saves you $59.70/month
That's $716.40/year compared to Qwen3 235B A22B Thinking 2507 at your current usage level of 100K calls/month.
| Metric | Llama 3.3 Nemotron Super 49B V1.5 | Qwen3 235B A22B Thinking 2507 | Winner |
|---|---|---|---|
| Overall Score | 69 | 69 | Qwen3 235B A22B Thinking 2507 |
| Rank | #170 | #169 | Qwen3 235B A22B Thinking 2507 |
| Quality Rank | #170 | #169 | Qwen3 235B A22B Thinking 2507 |
| Adoption Rank | #170 | #169 | Qwen3 235B A22B Thinking 2507 |
| Parameters | 49B | 235B | -- |
| Context Window | 131K | 131K | -- |
| Pricing | $0.10/$0.40/M | $0.15/$1.50/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Llama 3.3 Nemotron Super 49B V1.5 |
| Benchmarks | 59 | -- | Llama 3.3 Nemotron Super 49B V1.5 |
| Pricing | 0 | 2 | Qwen3 235B A22B Thinking 2507 |
| Context window size | 81 | 81 | Llama 3.3 Nemotron Super 49B V1.5 |
| Recency | 100 | 88 | Llama 3.3 Nemotron Super 49B V1.5 |
| Output Capacity | 20 | 20 | Llama 3.3 Nemotron Super 49B V1.5 |
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 #170), placing it in the top 42% of all 290 models tracked.
Scores 69/100 (rank #169), placing it in the top 42% of all 290 models tracked.
With only a 0-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.
Llama 3.3 Nemotron Super 49B V1.5 offers 70% better value per quality point. At 1M tokens/day, you'd spend $7.50/month with Llama 3.3 Nemotron Super 49B V1.5 vs $24.67/month with Qwen3 235B A22B Thinking 2507 - a $17.17 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. Llama 3.3 Nemotron Super 49B V1.5 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.40/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (69/100) correlates with better nuance, coherence, and style in long-form content
Llama 3.3 Nemotron Super 49B V1.5 and Qwen3 235B A22B Thinking 2507 are extremely close in overall performance (only 0.30000000000001137 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 3.3 Nemotron Super 49B V1.5
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.3 Nemotron Super 49B V1.5
70% 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 235B A22B Thinking 2507 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
NVIDIA
Alibaba
Llama 3.3 Nemotron Super 49B V1.5 saves you $1.40/month
That's 68% cheaper than Qwen3 235B A22B Thinking 2507 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 235B A22B Thinking 2507 |
|---|---|---|
| Context Window | 131K | 131K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | Yes |
| Created | Oct 10, 2025 | Jul 25, 2025 |
Qwen3 235B A22B Thinking 2507 scores 69/100 (rank #169) compared to Llama 3.3 Nemotron Super 49B V1.5's 69/100 (rank #170), giving it a 0-point advantage. Qwen3 235B A22B Thinking 2507 is the stronger overall choice, though Llama 3.3 Nemotron Super 49B V1.5 may excel in specific areas like cost efficiency.
Llama 3.3 Nemotron Super 49B V1.5 is ranked #170 and Qwen3 235B A22B Thinking 2507 is ranked #169 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.
Llama 3.3 Nemotron Super 49B V1.5 is cheaper at $0.40/M output tokens vs Qwen3 235B A22B Thinking 2507's $1.50/M output tokens - 3.7x more expensive. Input token pricing: Llama 3.3 Nemotron Super 49B V1.5 at $0.10/M vs Qwen3 235B A22B Thinking 2507 at $0.15/M.
Llama 3.3 Nemotron Super 49B V1.5 has a larger context window of 131,072 tokens compared to Qwen3 235B A22B Thinking 2507's 131,072 tokens. A larger context window means the model can process longer documents and conversations.