| Signal | GPT-4o (2024-08-06) | Delta | Llama 3.1 Nemotron Ultra 253B v1 |
|---|---|---|---|
Capabilities | 67 | +17 | |
Benchmarks | 58 | +58 | |
Pricing | 10 | +8 | |
Context window size | 81 | 0 | |
Recency | 25 | -45 | |
Output Capacity | 70 | +50 | |
| Overall Result | 4 wins | of 6 | 2 wins |
4
days ranked higher
2
days
24
days ranked higher
OpenAI
NVIDIA
Llama 3.1 Nemotron Ultra 253B v1 saves you $600.00/month
That's $7200.00/year compared to GPT-4o (2024-08-06) at your current usage level of 100K calls/month.
| Metric | GPT-4o (2024-08-06) | Llama 3.1 Nemotron Ultra 253B v1 | Winner |
|---|---|---|---|
| Overall Score | 56 | 58 | Llama 3.1 Nemotron Ultra 253B v1 |
| Rank | #242 | #233 | Llama 3.1 Nemotron Ultra 253B v1 |
| Quality Rank | #242 | #233 | Llama 3.1 Nemotron Ultra 253B v1 |
| Adoption Rank | #242 | #233 | Llama 3.1 Nemotron Ultra 253B v1 |
| Parameters | -- | 253B | -- |
| Context Window | 128K | 131K | Llama 3.1 Nemotron Ultra 253B v1 |
| Pricing | $2.50/$10.00/M | $0.60/$1.80/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | GPT-4o (2024-08-06) |
| Benchmarks | 58 | -- | GPT-4o (2024-08-06) |
| Pricing | 10 | 2 | GPT-4o (2024-08-06) |
| Context window size | 81 | 81 | Llama 3.1 Nemotron Ultra 253B v1 |
| Recency | 25 | 69 | Llama 3.1 Nemotron Ultra 253B v1 |
| Output Capacity | 70 | 20 | GPT-4o (2024-08-06) |
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 56/100 (rank #242), placing it in the top 17% of all 290 models tracked.
Scores 58/100 (rank #233), placing it in the top 20% of all 290 models tracked.
With only a 2-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 3.1 Nemotron Ultra 253B v1 offers 81% better value per quality point. At 1M tokens/day, you'd spend $36.00/month with Llama 3.1 Nemotron Ultra 253B v1 vs $187.50/month with GPT-4o (2024-08-06) - a $151.50 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 3.1 Nemotron Ultra 253B v1 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 ($1.80/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (58/100) 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 (2024-08-06) and Llama 3.1 Nemotron Ultra 253B v1 are extremely close in overall performance (only 1.8999999999999986 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-4o (2024-08-06)
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.1 Nemotron Ultra 253B v1
81% lower pricing; better value at scale
Best for Reliability
GPT-4o (2024-08-06)
Higher uptime and faster response speeds
Best for Prototyping
GPT-4o (2024-08-06)
Stronger community support and better developer experience
Best for Production
GPT-4o (2024-08-06)
Wider enterprise adoption and proven at scale
by OpenAI
by NVIDIA
| Capability | GPT-4o (2024-08-06) | Llama 3.1 Nemotron Ultra 253B v1 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
OpenAI
NVIDIA
Llama 3.1 Nemotron Ultra 253B v1 saves you $13.26/month
That's 80% cheaper than GPT-4o (2024-08-06) 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 (2024-08-06) | Llama 3.1 Nemotron Ultra 253B v1 |
|---|---|---|
| Context Window | 128K | 131K |
| Max Output Tokens | 16,384 | -- |
| Open Source | No | Yes |
| Created | Aug 6, 2024 | Apr 8, 2025 |
Llama 3.1 Nemotron Ultra 253B v1 scores 58/100 (rank #233) compared to GPT-4o (2024-08-06)'s 56/100 (rank #242), giving it a 2-point advantage. Llama 3.1 Nemotron Ultra 253B v1 is the stronger overall choice, though GPT-4o (2024-08-06) may excel in specific areas like certain benchmarks.
GPT-4o (2024-08-06) is ranked #242 and Llama 3.1 Nemotron Ultra 253B v1 is ranked #233 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.
Llama 3.1 Nemotron Ultra 253B v1 is cheaper at $1.80/M output tokens vs GPT-4o (2024-08-06)'s $10.00/M output tokens - 5.6x more expensive. Input token pricing: GPT-4o (2024-08-06) at $2.50/M vs Llama 3.1 Nemotron Ultra 253B v1 at $0.60/M.
Llama 3.1 Nemotron Ultra 253B v1 has a larger context window of 131,072 tokens compared to GPT-4o (2024-08-06)'s 128,000 tokens. A larger context window means the model can process longer documents and conversations.