| Signal | Nemotron Nano 9B V2 | Delta | Qwen3 14B |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 0 | -- | |
Context window size | 81 | +8 | |
Recency | 97 | +24 | |
Output Capacity | 20 | -57 | |
| Overall Result | 2 wins | of 5 | 1 wins |
9
days ranked higher
7
days
14
days ranked higher
NVIDIA
Alibaba
Nemotron Nano 9B V2 saves you $6.00/month
That's $72.00/year compared to Qwen3 14B at your current usage level of 100K calls/month.
| Metric | Nemotron Nano 9B V2 | Qwen3 14B | Winner |
|---|---|---|---|
| Overall Score | 72 | 71 | Nemotron Nano 9B V2 |
| Rank | #144 | #147 | Nemotron Nano 9B V2 |
| Quality Rank | #144 | #147 | Nemotron Nano 9B V2 |
| Adoption Rank | #144 | #147 | Nemotron Nano 9B V2 |
| Parameters | 9B | 14B | -- |
| Context Window | 131K | 41K | Nemotron Nano 9B V2 |
| Pricing | $0.04/$0.16/M | $0.06/$0.24/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Nemotron Nano 9B V2 |
| Pricing | 0 | 0 | Nemotron Nano 9B V2 |
| Context window size | 81 | 73 | Nemotron Nano 9B V2 |
| Recency | 97 | 73 | Nemotron Nano 9B V2 |
| Output Capacity | 20 | 77 | Qwen3 14B |
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 71/100 (rank #147), placing it in the top 50% 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.
Nemotron Nano 9B V2 offers 33% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with Nemotron Nano 9B V2 vs $4.50/month with Qwen3 14B - a $1.50 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. Nemotron Nano 9B V2 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 ($0.16/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (72/100) correlates with better nuance, coherence, and style in long-form content
Nemotron Nano 9B V2 and Qwen3 14B are extremely close in overall performance (only 0.19999999999998863 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Nemotron Nano 9B V2
Marginally better benchmark scores; both are excellent
Best for Cost
Nemotron Nano 9B V2
33% lower pricing; better value at scale
Best for Reliability
Nemotron Nano 9B V2
Higher uptime and faster response speeds
Best for Prototyping
Nemotron Nano 9B V2
Stronger community support and better developer experience
Best for Production
Nemotron Nano 9B V2
Wider enterprise adoption and proven at scale
by NVIDIA
| Capability | Nemotron Nano 9B V2 | Qwen3 14B |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
NVIDIA
Alibaba
Nemotron Nano 9B V2 saves you $0.1320/month
That's 33% cheaper than Qwen3 14B 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 | Nemotron Nano 9B V2 | Qwen3 14B |
|---|---|---|
| Context Window | 131K | 41K |
| Max Output Tokens | -- | 40,960 |
| Open Source | Yes | Yes |
| Created | Sep 5, 2025 | Apr 28, 2025 |
Nemotron Nano 9B V2 scores 72/100 (rank #144) compared to Qwen3 14B's 71/100 (rank #147), giving it a 0-point advantage. Nemotron Nano 9B V2 is the stronger overall choice, though Qwen3 14B may excel in specific areas like certain benchmarks.
Nemotron Nano 9B V2 is ranked #144 and Qwen3 14B is ranked #147 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.
Nemotron Nano 9B V2 is cheaper at $0.16/M output tokens vs Qwen3 14B's $0.24/M output tokens - 1.5x more expensive. Input token pricing: Nemotron Nano 9B V2 at $0.04/M vs Qwen3 14B at $0.06/M.
Nemotron Nano 9B V2 has a larger context window of 131,072 tokens compared to Qwen3 14B's 40,960 tokens. A larger context window means the model can process longer documents and conversations.