| Signal | Llama 3.3 Nemotron Super 49B V1.5 | Delta | Mistral Small 3.2 24B |
|---|---|---|---|
Capabilities | 67 | -- | |
Benchmarks | 59 | +59 | |
Pricing | 0 | +0 | |
Context window size | 81 | +0 | |
Recency | 100 | +17 | |
Output Capacity | 20 | -- | |
| Overall Result | 4 wins | of 6 | 0 wins |
14
days ranked higher
3
days
13
days ranked higher
NVIDIA
Mistral AI
Mistral Small 3.2 24B saves you $12.50/month
That's $150.00/year compared to Llama 3.3 Nemotron Super 49B V1.5 at your current usage level of 100K calls/month.
| Metric | Llama 3.3 Nemotron Super 49B V1.5 | Mistral Small 3.2 24B | Winner |
|---|---|---|---|
| Overall Score | 69 | 67 | Llama 3.3 Nemotron Super 49B V1.5 |
| Rank | #161 | #171 | Llama 3.3 Nemotron Super 49B V1.5 |
| Quality Rank | #161 | #171 | Llama 3.3 Nemotron Super 49B V1.5 |
| Adoption Rank | #161 | #171 | Llama 3.3 Nemotron Super 49B V1.5 |
| Parameters | 49B | 24B | -- |
| Context Window | 131K | 128K | Llama 3.3 Nemotron Super 49B V1.5 |
| Pricing | $0.10/$0.40/M | $0.07/$0.20/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Llama 3.3 Nemotron Super 49B V1.5 |
| Benchmarks | 59 | -- | Llama 3.3 Nemotron Super 49B V1.5 |
| Pricing | 0 | 0 | Llama 3.3 Nemotron Super 49B V1.5 |
| Context window size | 81 | 81 | Llama 3.3 Nemotron Super 49B V1.5 |
| Recency | 100 | 83 | Llama 3.3 Nemotron Super 49B V1.5 |
| Output Capacity | 20 | 20 | Llama 3.3 Nemotron Super 49B V1.5 |
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 69/100 (rank #161), placing it in the top 45% of all 290 models tracked.
Scores 67/100 (rank #171), placing it in the top 41% of all 290 models tracked.
With only a 1-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.
Mistral Small 3.2 24B offers 45% better value per quality point. At 1M tokens/day, you'd spend $4.13/month with Mistral Small 3.2 24B vs $7.50/month with Llama 3.3 Nemotron Super 49B V1.5 - a $3.37 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. Mistral Small 3.2 24B 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 ($0.20/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (69/100) 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
Llama 3.3 Nemotron Super 49B V1.5 and Mistral Small 3.2 24B are extremely close in overall performance (only 1.2999999999999972 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 3.3 Nemotron Super 49B V1.5
Marginally better benchmark scores; both are excellent
Best for Cost
Mistral Small 3.2 24B
45% lower pricing; better value at scale
Best for Reliability
Llama 3.3 Nemotron Super 49B V1.5
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.3 Nemotron Super 49B V1.5
Stronger community support and better developer experience
Best for Production
Llama 3.3 Nemotron Super 49B V1.5
Wider enterprise adoption and proven at scale
by NVIDIA
| Capability | Llama 3.3 Nemotron Super 49B V1.5 | Mistral Small 3.2 24B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
NVIDIA
Mistral AI
Mistral Small 3.2 24B saves you $0.2850/month
That's 43% cheaper than Llama 3.3 Nemotron Super 49B V1.5 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 | Llama 3.3 Nemotron Super 49B V1.5 | Mistral Small 3.2 24B |
|---|---|---|
| Context Window | 131K | 128K |
| Max Output Tokens | -- | -- |
| Open Source | Yes | Yes |
| Created | Oct 10, 2025 | Jun 20, 2025 |
Llama 3.3 Nemotron Super 49B V1.5 scores 69/100 (rank #161) compared to Mistral Small 3.2 24B's 67/100 (rank #171), giving it a 1-point advantage. Llama 3.3 Nemotron Super 49B V1.5 is the stronger overall choice, though Mistral Small 3.2 24B may excel in specific areas like cost efficiency.
Llama 3.3 Nemotron Super 49B V1.5 is ranked #161 and Mistral Small 3.2 24B is ranked #171 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.
Mistral Small 3.2 24B is cheaper at $0.20/M output tokens vs Llama 3.3 Nemotron Super 49B V1.5's $0.40/M output tokens - 2.0x more expensive. Input token pricing: Llama 3.3 Nemotron Super 49B V1.5 at $0.10/M vs Mistral Small 3.2 24B at $0.07/M.
Llama 3.3 Nemotron Super 49B V1.5 has a larger context window of 131,072 tokens compared to Mistral Small 3.2 24B's 128,000 tokens. A larger context window means the model can process longer documents and conversations.