| Signal | GPT-4.1 Nano | Delta | Llama 3.1 Nemotron 70B Instruct |
|---|---|---|---|
Capabilities | 83 | +33 | |
Benchmarks | 44 | -3 | |
Pricing | 100 | +1 | |
Context window size | 96 | +14 | |
Recency | 68 | +33 | |
Output Capacity | 75 | +5 | |
| Overall Result | 5 wins | of 6 | 1 wins |
Score History
42.1
current score
Llama 3.1 Nemotron 70B Instruct
right now
44.6
current score
OpenAI
NVIDIA
GPT-4.1 Nano saves you $150.00/month
That's $1800.00/year compared to Llama 3.1 Nemotron 70B Instruct at your current usage level of 100K calls/month.
| Metric | GPT-4.1 Nano | Llama 3.1 Nemotron 70B Instruct | Winner |
|---|---|---|---|
| Overall Score | 42 | 45 | Llama 3.1 Nemotron 70B Instruct |
| Rank | #117 | #116 | Llama 3.1 Nemotron 70B Instruct |
| Quality Rank | #117 | #116 | Llama 3.1 Nemotron 70B Instruct |
| Adoption Rank | #117 | #116 | Llama 3.1 Nemotron 70B Instruct |
| Parameters | -- | 70B | -- |
| Context Window | 1048K | 131K | GPT-4.1 Nano |
| Pricing | $0.10/$0.40/M | $1.20/$1.20/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 50 | GPT-4.1 Nano |
| Benchmarks | 44 | 47 | Llama 3.1 Nemotron 70B Instruct |
| Pricing | 100 | 99 | GPT-4.1 Nano |
| Context window size | 96 | 81 | GPT-4.1 Nano |
| Recency | 68 | 35 | GPT-4.1 Nano |
| Output Capacity | 75 | 70 | GPT-4.1 Nano |
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 42/100 (rank #117), placing it in the top 60% of all 290 models tracked.
Scores 45/100 (rank #116), placing it in the top 60% of all 290 models tracked.
With only a 3-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.
GPT-4.1 Nano offers 79% better value per quality point. At 1M tokens/day, you'd spend $7.50/month with GPT-4.1 Nano vs $36.00/month with Llama 3.1 Nemotron 70B Instruct - a $28.50 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. GPT-4.1 Nano also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1048K 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 (45/100) 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-4.1 Nano and Llama 3.1 Nemotron 70B Instruct are extremely close in overall performance (only 2.5 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-4.1 Nano
Marginally better benchmark scores; both are excellent
Best for Cost
GPT-4.1 Nano
79% lower pricing; better value at scale
Best for Reliability
GPT-4.1 Nano
Higher uptime and faster response speeds
Best for Prototyping
GPT-4.1 Nano
Stronger community support and better developer experience
Best for Production
GPT-4.1 Nano
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-4.1 Nano | Llama 3.1 Nemotron 70B Instruct |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
NVIDIA
GPT-4.1 Nano saves you $2.94/month
That's 82% cheaper than Llama 3.1 Nemotron 70B Instruct 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-4.1 Nano | Llama 3.1 Nemotron 70B Instruct |
|---|---|---|
| Context Window | 1.0M | 131K |
| Max Output Tokens | 32,768 | 16,384 |
| Open Source | No | Yes |
| Created | Apr 14, 2025 | Oct 15, 2024 |
Llama 3.1 Nemotron 70B Instruct scores 45/100 (rank #116) compared to GPT-4.1 Nano's 42/100 (rank #117), giving it a 3-point advantage. Llama 3.1 Nemotron 70B Instruct is the stronger overall choice, though GPT-4.1 Nano may excel in specific areas like cost efficiency.
GPT-4.1 Nano is ranked #117 and Llama 3.1 Nemotron 70B Instruct is ranked #116 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.
GPT-4.1 Nano is cheaper at $0.40/M output tokens vs Llama 3.1 Nemotron 70B Instruct's $1.20/M output tokens - 3.0x more expensive. Input token pricing: GPT-4.1 Nano at $0.10/M vs Llama 3.1 Nemotron 70B Instruct at $1.20/M.
GPT-4.1 Nano has a larger context window of 1,047,576 tokens compared to Llama 3.1 Nemotron 70B Instruct's 131,072 tokens. A larger context window means the model can process longer documents and conversations.