| Signal | Virtuoso Large | Delta | Qwen3 14B |
|---|---|---|---|
Capabilities | 33 | -33 | |
Pricing | 99 | -1 | |
Context window size | 73 | -- | |
Recency | 57 | +1 | |
Output Capacity | 80 | +3 | |
| Overall Result | 2 wins | of 5 | 2 wins |
Score History
40
current score
Tied
right now
40
current score
arcee-ai
Alibaba
Qwen3 14B saves you $113.00/month
That's $1356.00/year compared to Virtuoso Large at your current usage level of 100K calls/month.
| Metric | Virtuoso Large | Qwen3 14B | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #259 | #261 | Virtuoso Large |
| Quality Rank | #259 | #261 | Virtuoso Large |
| Adoption Rank | #259 | #261 | Virtuoso Large |
| Parameters | -- | 14B | -- |
| Context Window | 131K | 132K | Qwen3 14B |
| Pricing | $0.75/$1.20/M | $0.10/$0.24/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 67 | Qwen3 14B |
| Pricing | 99 | 100 | Qwen3 14B |
| Context window size | 73 | 73 | Virtuoso Large |
| Recency | 57 | 56 | Virtuoso Large |
| Output Capacity | 80 | 77 | Virtuoso Large |
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%). Learn more about our methodology.
Scores 40/100 (rank #259), placing it in the top 11% of all 290 models tracked.
Scores 40/100 (rank #261), placing it in the top 10% 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 14B offers 83% better value per quality point. At 1M tokens/day, you'd spend $5.10/month with Qwen3 14B vs $29.25/month with Virtuoso Large - a $24.15 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
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Qwen3 14B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (132K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.24/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
Virtuoso Large and Qwen3 14B 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
Virtuoso Large
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3 14B
83% lower pricing; better value at scale
Best for Reliability
Virtuoso Large
Higher uptime and faster response speeds
Best for Prototyping
Virtuoso Large
Stronger community support and better developer experience
Best for Production
Virtuoso Large
Wider enterprise adoption and proven at scale
by arcee-ai
| Capability | Virtuoso Large | Qwen3 14B |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
arcee-ai
Alibaba
Qwen3 14B saves you $2.32/month
That's 83% cheaper than Virtuoso Large 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 | Virtuoso Large | Qwen3 14B |
|---|---|---|
| Context Window | 131K | 132K |
| Max Output Tokens | 64,000 | 40,960 |
| Open Source | No | Yes |
| Created | May 5, 2025 | Apr 28, 2025 |