| Signal | Virtuoso Large | Delta | Gemma 3n 4B |
|---|---|---|---|
Capabilities | 33 | -- | |
Pricing | 99 | -1 | |
Context window size | 73 | +9 | |
Recency | 57 | -3 | |
Output Capacity | 80 | +60 | |
| Overall Result | 2 wins | of 5 | 2 wins |
Score History
40
current score
Tied
right now
40
current score
arcee-ai
Gemma 3n 4B saves you $123.00/month
That's $1476.00/year compared to Virtuoso Large at your current usage level of 100K calls/month.
| Metric | Virtuoso Large | Gemma 3n 4B | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #259 | #258 | Gemma 3n 4B |
| Quality Rank | #259 | #258 | Gemma 3n 4B |
| Adoption Rank | #259 | #258 | Gemma 3n 4B |
| Parameters | -- | 4B | -- |
| Context Window | 131K | 33K | Virtuoso Large |
| Pricing | $0.75/$1.20/M | $0.06/$0.12/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 33 | Virtuoso Large |
| Pricing | 99 | 100 | Gemma 3n 4B |
| Context window size | 73 | 65 | Virtuoso Large |
| Recency | 57 | 60 | Gemma 3n 4B |
| Output Capacity | 80 | 20 | 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 #258), placing it in the top 11% 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.
Gemma 3n 4B offers 91% better value per quality point. At 1M tokens/day, you'd spend $2.70/month with Gemma 3n 4B vs $29.25/month with Virtuoso Large - a $26.55 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. Gemma 3n 4B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.12/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 Gemma 3n 4B 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
Gemma 3n 4B
91% 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 | Gemma 3n 4B |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
arcee-ai
Gemma 3n 4B saves you $2.54/month
That's 91% 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 | Gemma 3n 4B |
|---|---|---|
| Context Window | 131K | 33K |
| Max Output Tokens | 64,000 | -- |
| Open Source | No | Yes |
| Created | May 5, 2025 | May 20, 2025 |