| Signal | Nemotron Nano 9B V2 | Delta | Qwen3 235B A22B Instruct 2507 |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 0 | +0 | |
Context window size | 81 | -5 | |
Recency | 98 | +8 | |
Output Capacity | 20 | -- | |
| Overall Result | 2 wins | of 5 | 1 wins |
11
days ranked higher
7
days
12
days ranked higher
NVIDIA
Alibaba
Nemotron Nano 9B V2 saves you $0.10/month
That's $1.20/year compared to Qwen3 235B A22B Instruct 2507 at your current usage level of 100K calls/month.
| Metric | Nemotron Nano 9B V2 | Qwen3 235B A22B Instruct 2507 | Winner |
|---|---|---|---|
| Overall Score | 69 | 68 | Nemotron Nano 9B V2 |
| Rank | #147 | #151 | Nemotron Nano 9B V2 |
| Quality Rank | #147 | #151 | Nemotron Nano 9B V2 |
| Adoption Rank | #147 | #151 | Nemotron Nano 9B V2 |
| Parameters | 9B | 235B | -- |
| Context Window | 131K | 262K | Qwen3 235B A22B Instruct 2507 |
| Pricing | $0.04/$0.16/M | $0.07/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Nemotron Nano 9B V2 |
| Pricing | 0 | 0 | Nemotron Nano 9B V2 |
| Context window size | 81 | 86 | Qwen3 235B A22B Instruct 2507 |
| Recency | 98 | 90 | Nemotron Nano 9B V2 |
| Output Capacity | 20 | 20 | Nemotron Nano 9B V2 |
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 #147), placing it in the top 50% of all 290 models tracked.
Scores 68/100 (rank #151), placing it in the top 48% 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.
Qwen3 235B A22B Instruct 2507 offers 15% better value per quality point. At 1M tokens/day, you'd spend $2.56/month with Qwen3 235B A22B Instruct 2507 vs $3.00/month with Nemotron Nano 9B V2 — a $0.44 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 235B A22B Instruct 2507 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 ($0.10/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
Nemotron Nano 9B V2 and Qwen3 235B A22B Instruct 2507 are extremely close in overall performance (only 0.7000000000000028 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
Qwen3 235B A22B Instruct 2507
15% 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 235B A22B Instruct 2507 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
NVIDIA
Alibaba
Qwen3 235B A22B Instruct 2507 saves you $0.0162/month
That's 6% cheaper than Nemotron Nano 9B V2 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 235B A22B Instruct 2507 |
|---|---|---|
| Context Window | 131K | 262K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | Yes |
| Created | Sep 5, 2025 | Jul 21, 2025 |
Nemotron Nano 9B V2 scores 69/100 (rank #147) compared to Qwen3 235B A22B Instruct 2507's 68/100 (rank #151), giving it a 1-point advantage. Nemotron Nano 9B V2 is the stronger overall choice, though Qwen3 235B A22B Instruct 2507 may excel in specific areas like cost efficiency.
Nemotron Nano 9B V2 is ranked #147 and Qwen3 235B A22B Instruct 2507 is ranked #151 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 235B A22B Instruct 2507 is cheaper at $0.10/M output tokens vs Nemotron Nano 9B V2's $0.16/M output tokens — 1.6x more expensive. Input token pricing: Nemotron Nano 9B V2 at $0.04/M vs Qwen3 235B A22B Instruct 2507 at $0.07/M.
Qwen3 235B A22B Instruct 2507 has a larger context window of 262,144 tokens compared to Nemotron Nano 9B V2's 131,072 tokens. A larger context window means the model can process longer documents and conversations.