| Signal | GPT-4o (2024-05-13) | Delta | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|---|
Capabilities | 67 | -- | |
Benchmarks | 59 | +0 | |
Pricing | 15 | +15 | |
Context window size | 81 | 0 | |
Recency | 7 | -93 | |
Output Capacity | 60 | +40 | |
| Overall Result | 3 wins | of 6 | 2 wins |
7
days higher
6
days
17
days higher
OpenAI
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5 saves you $1220.00/month
That's $14640.00/year compared to GPT-4o (2024-05-13) at your current usage level of 100K calls/month.
| Metric | GPT-4o (2024-05-13) | Llama 3.3 Nemotron Super 49B V1.5 | Winner |
|---|---|---|---|
| Overall Score | 61 | 61 | GPT-4o (2024-05-13) |
| Rank | #95 | #97 | GPT-4o (2024-05-13) |
| Quality Rank | #95 | #97 | GPT-4o (2024-05-13) |
| Adoption Rank | #95 | #97 | GPT-4o (2024-05-13) |
| Parameters | -- | 49B | -- |
| Context Window | 128K | 131K | Llama 3.3 Nemotron Super 49B V1.5 |
| Pricing | $5.00/$15.00/M | $0.10/$0.40/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | GPT-4o (2024-05-13) |
| Benchmarks | 59 | 59 | GPT-4o (2024-05-13) |
| Pricing | 15 | 0 | GPT-4o (2024-05-13) |
| Context window size | 81 | 81 | Llama 3.3 Nemotron Super 49B V1.5 |
| Recency | 7 | 100 | Llama 3.3 Nemotron Super 49B V1.5 |
| Output Capacity | 60 | 20 | GPT-4o (2024-05-13) |
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 61/100 (rank #95), placing it in the top 68% of all 290 models tracked.
Scores 61/100 (rank #97), placing it in the top 67% 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.
Llama 3.3 Nemotron Super 49B V1.5 offers 98% better value per quality point. At 1M tokens/day, you'd spend $7.50/month with Llama 3.3 Nemotron Super 49B V1.5 vs $300.00/month with GPT-4o (2024-05-13) - a $292.50 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. Llama 3.3 Nemotron Super 49B V1.5 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 ($0.40/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (61/100) 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
GPT-4o (2024-05-13) and Llama 3.3 Nemotron Super 49B V1.5 are extremely close in overall performance (only 0.19999999999999574 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-4o (2024-05-13)
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.3 Nemotron Super 49B V1.5
98% lower pricing; better value at scale
Best for Reliability
GPT-4o (2024-05-13)
Higher uptime and faster response speeds
Best for Prototyping
GPT-4o (2024-05-13)
Stronger community support and better developer experience
Best for Production
GPT-4o (2024-05-13)
Wider enterprise adoption and proven at scale
by OpenAI
by NVIDIA
| Capability | GPT-4o (2024-05-13) | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
OpenAI
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5 saves you $26.34/month
That's 98% cheaper than GPT-4o (2024-05-13) 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 | GPT-4o (2024-05-13) | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| Context Window | 128K | 131K |
| Max Output Tokens | 4,096 | -- |
| Open Source | No | Yes |
| Created | May 13, 2024 | Oct 10, 2025 |
GPT-4o (2024-05-13) scores 61/100 (rank #95) compared to Llama 3.3 Nemotron Super 49B V1.5's 61/100 (rank #97), giving it a 0-point advantage. GPT-4o (2024-05-13) is the stronger overall choice, though Llama 3.3 Nemotron Super 49B V1.5 may excel in specific areas like cost efficiency.
GPT-4o (2024-05-13) is ranked #95 and Llama 3.3 Nemotron Super 49B V1.5 is ranked #97 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.
Llama 3.3 Nemotron Super 49B V1.5 is cheaper at $0.40/M output tokens vs GPT-4o (2024-05-13)'s $15.00/M output tokens - 37.5x more expensive. Input token pricing: GPT-4o (2024-05-13) at $5.00/M vs Llama 3.3 Nemotron Super 49B V1.5 at $0.10/M.
Llama 3.3 Nemotron Super 49B V1.5 has a larger context window of 131,072 tokens compared to GPT-4o (2024-05-13)'s 128,000 tokens. A larger context window means the model can process longer documents and conversations.