| Signal | DeepSeek V3.1 Terminus | Delta | Nemotron Nano 9B V2 |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 1 | +1 | |
Context window size | 83 | +2 | |
Recency | 100 | +2 | |
Output Capacity | 20 | -- | |
| Overall Result | 3 wins | of 5 | 0 wins |
14
days ranked higher
3
days
13
days ranked higher
DeepSeek
NVIDIA
Nemotron Nano 9B V2 saves you $48.50/month
That's $582.00/year compared to DeepSeek V3.1 Terminus at your current usage level of 100K calls/month.
| Metric | DeepSeek V3.1 Terminus | Nemotron Nano 9B V2 | Winner |
|---|---|---|---|
| Overall Score | 70 | 69 | DeepSeek V3.1 Terminus |
| Rank | #133 | #147 | DeepSeek V3.1 Terminus |
| Quality Rank | #133 | #147 | DeepSeek V3.1 Terminus |
| Adoption Rank | #133 | #147 | DeepSeek V3.1 Terminus |
| Parameters | -- | 9B | -- |
| Context Window | 164K | 131K | DeepSeek V3.1 Terminus |
| Pricing | $0.21/$0.79/M | $0.04/$0.16/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | DeepSeek V3.1 Terminus |
| Pricing | 1 | 0 | DeepSeek V3.1 Terminus |
| Context window size | 83 | 81 | DeepSeek V3.1 Terminus |
| Recency | 100 | 98 | DeepSeek V3.1 Terminus |
| Output Capacity | 20 | 20 | DeepSeek V3.1 Terminus |
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 70/100 (rank #133), placing it in the top 54% of all 290 models tracked.
Scores 69/100 (rank #147), placing it in the top 50% of all 290 models tracked.
With only a 1-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 80% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with Nemotron Nano 9B V2 vs $15.00/month with DeepSeek V3.1 Terminus — a $12.00 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 (164K 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 (70/100) correlates with better nuance, coherence, and style in long-form content
DeepSeek V3.1 Terminus and Nemotron Nano 9B V2 are extremely close in overall performance (only 0.7999999999999972 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
DeepSeek V3.1 Terminus
Marginally better benchmark scores; both are excellent
Best for Cost
Nemotron Nano 9B V2
80% lower pricing; better value at scale
Best for Reliability
DeepSeek V3.1 Terminus
Higher uptime and faster response speeds
Best for Prototyping
DeepSeek V3.1 Terminus
Stronger community support and better developer experience
Best for Production
DeepSeek V3.1 Terminus
Wider enterprise adoption and proven at scale
by DeepSeek
| Capability | DeepSeek V3.1 Terminus | Nemotron Nano 9B V2 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
DeepSeek
NVIDIA
Nemotron Nano 9B V2 saves you $1.06/month
That's 80% cheaper than DeepSeek V3.1 Terminus 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 | DeepSeek V3.1 Terminus | Nemotron Nano 9B V2 |
|---|---|---|
| Context Window | 164K | 131K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | Yes |
| Created | Sep 22, 2025 | Sep 5, 2025 |
DeepSeek V3.1 Terminus scores 70/100 (rank #133) compared to Nemotron Nano 9B V2's 69/100 (rank #147), giving it a 1-point advantage. DeepSeek V3.1 Terminus is the stronger overall choice, though Nemotron Nano 9B V2 may excel in specific areas like cost efficiency.
DeepSeek V3.1 Terminus is ranked #133 and Nemotron Nano 9B V2 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 DeepSeek V3.1 Terminus's $0.79/M output tokens — 4.9x more expensive. Input token pricing: DeepSeek V3.1 Terminus at $0.21/M vs Nemotron Nano 9B V2 at $0.04/M.
DeepSeek V3.1 Terminus has a larger context window of 163,840 tokens compared to Nemotron Nano 9B V2's 131,072 tokens. A larger context window means the model can process longer documents and conversations.