NVIDIA (9 models) vs Qwen (Alibaba) (52 models) - compared across composite scores, pricing, capabilities, and context windows.
| Capability | NVIDIA | Qwen (Alibaba) | Leader |
|---|---|---|---|
Vision | 2/9 | 22/52 | Qwen (Alibaba) |
Reasoning | 9/9 | 27/52 | Qwen (Alibaba) |
Function Calling | 9/9 | 49/52 | Qwen (Alibaba) |
JSON Mode | 6/9 | 50/52 | Qwen (Alibaba) |
Web Search | 0/9 | 0/52 | Tie |
Streaming | 9/9 | 52/52 | Qwen (Alibaba) |
Image Output | 0/9 | 0/52 | Tie |
| Metric | NVIDIA | Qwen (Alibaba) |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.040 Nemotron Nano 9B V2 | $0.033 Qwen3 235B A22B Instruct 2507 |
| Cheapest Output (per 1M tokens) | $0.160 | $0.100 |
| Most Expensive Input (per 1M tokens) | $0.100 Nemotron 3 Super | $1.04 Qwen3.6 Max Preview |
| Most Expensive Output (per 1M tokens) | $0.450 | $6.24 |
| Free Models | 5 | 2 |
| Max Context Window | 262K | 1.0M |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Llama 3.3 Nemotron Super 49B V1.5 | 61 | $0.100 | $0.400 |
| Nemotron 3 Nano Omni (free) | 40 | Free | Free |
| Nemotron 3 Super (free) | 40 | Free | Free |
| Nemotron 3 Super | 40 | $0.090 | $0.450 |
| Nemotron 3 Nano 30B A3B (free) | 40 | Free | Free |
| Nemotron 3 Nano 30B A3B | 40 | $0.050 | $0.200 |
| Nemotron Nano 12B 2 VL (free) | 40 | Free | Free |
| Nemotron Nano 9B V2 (free) | 40 | Free | Free |
| Nemotron Nano 9B V2 | 40 | $0.040 | $0.160 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Qwen3.5 397B A17B | 80 | $0.390 | $2.34 |
| Qwen3.5-122B-A10B | 78 | $0.260 | $2.08 |
| Qwen3.5-27B | 77 | $0.195 | $1.56 |
| Qwen3.5-35B-A3B | 76 | $0.140 | $1.00 |
| Qwen3.6 Plus | 75 | $0.325 | $1.95 |
| Qwen3.6 Max Preview | 75 | $1.04 | $6.24 |
| Qwen3 VL 235B A22B Instruct | 69 | $0.200 | $0.880 |
| Qwen3.5-Flash | 69 | $0.065 | $0.260 |
| Qwen3 Max Thinking | 68 | $0.780 | $3.90 |
| Qwen3 VL 235B A22B Thinking | 68 | $0.260 | $2.60 |
| Qwen3 Max | 67 | $0.780 | $3.90 |
| Qwen3 Next 80B A3B Instruct (free) | 67 | Free | Free |
| Qwen3 Next 80B A3B Instruct | 67 | $0.090 | $1.10 |
| Qwen3.5-9B | 67 | $0.040 | $0.150 |
| Qwen3 235B A22B Thinking 2507 | 65 | $0.150 | $1.50 |
| Qwen3 235B A22B Instruct 2507 | 65 | $0.071 | $0.100 |
| Qwen3 30B A3B Thinking 2507 | 64 | $0.080 | $0.400 |
| Qwen3 Next 80B A3B Thinking | 64 | $0.098 | $0.780 |
| Qwen3 30B A3B | 64 | $0.090 | $0.450 |
| Qwen3 8B | 61 | $0.050 | $0.400 |
Compare any two AI providers side-by-side.
NVIDIA's strategy appears focused on open-source accessibility with 100% of their models being open source and 36% available for free, likely targeting researchers and startups. In contrast, Qwen maintains only 72% open source (36 of 50 models) and reserves their free tier for just 4% of their portfolio, suggesting a more commercial-first approach despite their larger model selection.
The gap represents a 33% performance advantage for Qwen's top model, but more importantly, Qwen's average score of 45/100 matches NVIDIA's best model performance, indicating consistently stronger models across their portfolio. NVIDIA's 40/100 average suggests their models cluster in the lower performance tier, making them suitable primarily for cost-sensitive applications rather than performance-critical ones.
NVIDIA's pricing reflects their focus on specialized capabilities - 91% of their models support reasoning (10 of 11) compared to just 48% for Qwen (24 of 50), and 82% support function calling versus Qwen's 90%. For applications requiring consistent reasoning capabilities across a model family, NVIDIA's smaller but more uniformly capable portfolio at $0.160-$1.80/M may offer better value than cherry-picking from Qwen's broader $0.090-$4.16/M range.
Qwen's 4x larger context window aligns with their broader portfolio strategy - supporting 19 vision models (38%) versus NVIDIA's 2 (18%) suggests Qwen targets multimodal document processing and long-form analysis use cases. NVIDIA's 262K limit positions them for traditional text processing and real-time inference scenarios where their smaller model count (11 vs 50) and tighter capability focus become advantages.
Qwen's combination of 1.0M token context window and 90% function calling support (45 of 50 models) makes them superior for RAG pipelines that need to process large document chunks and integrate with external retrievers. NVIDIA's 82% function calling coverage (9 of 11 models) with only 262K context limits them to smaller-scale RAG implementations, though their higher reasoning capability ratio (91% vs 48%) could provide better synthesis of retrieved information.