| Signal | Nemotron 3 Super | Delta | Qwen3.5-9B |
|---|---|---|---|
Capabilities | 67 | -17 | |
Pricing | 1 | +0 | |
Context window size | 86 | +0 | |
Recency | 100 | -- | |
Output Capacity | 20 | -55 | |
| Overall Result | 2 wins | of 5 | 2 wins |
8
days higher
3
days
19
days higher
NVIDIA
Alibaba
Qwen3.5-9B saves you $22.50/month
That's $270.00/year compared to Nemotron 3 Super at your current usage level of 100K calls/month.
| Metric | Nemotron 3 Super | Qwen3.5-9B | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #132 | #134 | Nemotron 3 Super |
| Quality Rank | #132 | #134 | Nemotron 3 Super |
| Adoption Rank | #132 | #134 | Nemotron 3 Super |
| Parameters | 120B | 9B | -- |
| Context Window | 262K | 256K | Nemotron 3 Super |
| Pricing | $0.10/$0.50/M | $0.05/$0.15/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-9B |
| Pricing | 1 | 0 | Nemotron 3 Super |
| Context window size | 86 | 86 | Nemotron 3 Super |
| Recency | 100 | 100 | Nemotron 3 Super |
| Output Capacity | 20 | 75 | Qwen3.5-9B |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 40/100 (rank #132), placing it in the top 55% of all 290 models tracked.
Scores 40/100 (rank #134), placing it in the top 54% 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.
Qwen3.5-9B offers 67% better value per quality point. At 1M tokens/day, you'd spend $3.00/month with Qwen3.5-9B vs $9.00/month with Nemotron 3 Super - a $6.00 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. Qwen3.5-9B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.15/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) 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
Nemotron 3 Super and Qwen3.5-9B are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Nemotron 3 Super
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3.5-9B
67% lower pricing; better value at scale
Best for Reliability
Nemotron 3 Super
Higher uptime and faster response speeds
Best for Prototyping
Nemotron 3 Super
Stronger community support and better developer experience
Best for Production
Nemotron 3 Super
Wider enterprise adoption and proven at scale
by NVIDIA
| Capability | Nemotron 3 Super | Qwen3.5-9B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
NVIDIA
Alibaba
Qwen3.5-9B saves you $0.5100/month
That's 65% cheaper than Nemotron 3 Super 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 3 Super | Qwen3.5-9B |
|---|---|---|
| Context Window | 262K | 256K |
| Max Output Tokens | -- | 32,768 |
| Open Source | Yes | Yes |
| Created | Mar 11, 2026 | Mar 10, 2026 |
Both Nemotron 3 Super and Qwen3.5-9B score 40/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
Nemotron 3 Super is ranked #132 and Qwen3.5-9B is ranked #134 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
Qwen3.5-9B is cheaper at $0.15/M output tokens vs Nemotron 3 Super's $0.50/M output tokens - 3.3x more expensive. Input token pricing: Nemotron 3 Super at $0.10/M vs Qwen3.5-9B at $0.05/M.
Nemotron 3 Super has a larger context window of 262,144 tokens compared to Qwen3.5-9B's 256,000 tokens. A larger context window means the model can process longer documents and conversations.