| Signal | Grok 4 | Delta | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|---|
Capabilities | 100 | +33 | |
Pricing | 15 | +15 | |
Context window size | 86 | +5 | |
Recency | 87 | -13 | |
Output Capacity | 20 | -- | |
| Overall Result | 3 wins | of 5 | 1 wins |
30
days ranked higher
0
days
0
days ranked higher
xAI
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5 saves you $1020.00/month
That's $12240.00/year compared to Grok 4 at your current usage level of 100K calls/month.
| Metric | Grok 4 | Llama 3.3 Nemotron Super 49B V1.5 | Winner |
|---|---|---|---|
| Overall Score | 83 | 69 | Grok 4 |
| Rank | #62 | #143 | Grok 4 |
| Quality Rank | #62 | #143 | Grok 4 |
| Adoption Rank | #62 | #143 | Grok 4 |
| Parameters | -- | 49B | -- |
| Context Window | 256K | 131K | Grok 4 |
| Pricing | $3.00/$15.00/M | $0.10/$0.40/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 67 | Grok 4 |
| Pricing | 15 | 0 | Grok 4 |
| Context window size | 86 | 81 | Grok 4 |
| Recency | 87 | 100 | Llama 3.3 Nemotron Super 49B V1.5 |
| Output Capacity | 20 | 20 | Grok 4 |
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 83/100 (rank #62), placing it in the top 79% of all 290 models tracked.
Scores 69/100 (rank #143), placing it in the top 51% of all 290 models tracked.
Grok 4 has a 14-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Llama 3.3 Nemotron Super 49B V1.5 offers 97% 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 $270.00/month with Grok 4 — a $262.50 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 (256K 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 (83/100) 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
Grok 4 clearly outperforms Llama 3.3 Nemotron Super 49B V1.5 with a significant 13.5-point lead. For most general use cases, Grok 4 is the stronger choice. However, Llama 3.3 Nemotron Super 49B V1.5 may still excel in niche scenarios.
Best for Quality
Grok 4
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.3 Nemotron Super 49B V1.5
97% lower pricing; better value at scale
Best for Reliability
Grok 4
Higher uptime and faster response speeds
Best for Prototyping
Grok 4
Stronger community support and better developer experience
Best for Production
Grok 4
Wider enterprise adoption and proven at scale
by xAI
by NVIDIA
| Capability | Grok 4 | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Searchdiffers | ||
| Image Output |
xAI
NVIDIA
Llama 3.3 Nemotron Super 49B V1.5 saves you $22.74/month
That's 97% cheaper than Grok 4 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 | Grok 4 | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| Context Window | 256K | 131K |
| Max Output Tokens | -- | -- |
| Open Source | No | Yes |
| Created | Jul 9, 2025 | Oct 10, 2025 |
Grok 4 scores 83/100 (rank #62) compared to Llama 3.3 Nemotron Super 49B V1.5's 69/100 (rank #143), giving it a 14-point advantage. Grok 4 is the stronger overall choice, though Llama 3.3 Nemotron Super 49B V1.5 may excel in specific areas like cost efficiency.
Grok 4 is ranked #62 and Llama 3.3 Nemotron Super 49B V1.5 is ranked #143 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 3.3 Nemotron Super 49B V1.5 is cheaper at $0.40/M output tokens vs Grok 4's $15.00/M output tokens — 37.5x more expensive. Input token pricing: Grok 4 at $3.00/M vs Llama 3.3 Nemotron Super 49B V1.5 at $0.10/M.
Grok 4 has a larger context window of 256,000 tokens compared to Llama 3.3 Nemotron Super 49B V1.5's 131,072 tokens. A larger context window means the model can process longer documents and conversations.