| Signal | Llama 4 Scout | Delta | R1 0528 |
|---|---|---|---|
Capabilities | 67 | -- | |
Pricing | 0 | -2 | |
Context window size | 88 | +5 | |
Recency | 70 | -10 | |
Output Capacity | 70 | -10 | |
| Overall Result | 1 wins | of 5 | 3 wins |
4
days ranked higher
0
days
26
days ranked higher
Meta
DeepSeek
Llama 4 Scout saves you $129.50/month
That's $1554.00/year compared to R1 0528 at your current usage level of 100K calls/month.
| Metric | Llama 4 Scout | R1 0528 | Winner |
|---|---|---|---|
| Overall Score | 72 | 75 | R1 0528 |
| Rank | #108 | #93 | R1 0528 |
| Quality Rank | #108 | #93 | R1 0528 |
| Adoption Rank | #108 | #93 | R1 0528 |
| Parameters | -- | -- | -- |
| Context Window | 328K | 164K | Llama 4 Scout |
| Pricing | $0.08/$0.30/M | $0.45/$2.15/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Llama 4 Scout |
| Pricing | 0 | 2 | R1 0528 |
| Context window size | 88 | 83 | Llama 4 Scout |
| Recency | 70 | 80 | R1 0528 |
| Output Capacity | 70 | 80 | R1 0528 |
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 72/100 (rank #108), placing it in the top 63% of all 290 models tracked.
Scores 75/100 (rank #93), placing it in the top 68% 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 4 Scout offers 85% better value per quality point. At 1M tokens/day, you'd spend $5.70/month with Llama 4 Scout vs $39.00/month with R1 0528 — a $33.30 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 4 Scout also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (328K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.30/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (75/100) 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 4 Scout and R1 0528 are extremely close in overall performance (only 2.4000000000000057 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 4 Scout
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Scout
85% lower pricing; better value at scale
Best for Reliability
Llama 4 Scout
Higher uptime and faster response speeds
Best for Prototyping
Llama 4 Scout
Stronger community support and better developer experience
Best for Production
Llama 4 Scout
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 4 Scout | R1 0528 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
DeepSeek
Llama 4 Scout saves you $2.89/month
That's 85% cheaper than R1 0528 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 4 Scout | R1 0528 |
|---|---|---|
| Context Window | 328K | 164K |
| Max Output Tokens | 16,384 | 65,536 |
| Open Source | Yes | Yes |
| Created | Apr 5, 2025 | May 28, 2025 |
R1 0528 scores 75/100 (rank #93) compared to Llama 4 Scout's 72/100 (rank #108), giving it a 2-point advantage. R1 0528 is the stronger overall choice, though Llama 4 Scout may excel in specific areas like cost efficiency.
Llama 4 Scout is ranked #108 and R1 0528 is ranked #93 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 4 Scout is cheaper at $0.30/M output tokens vs R1 0528's $2.15/M output tokens — 7.2x more expensive. Input token pricing: Llama 4 Scout at $0.08/M vs R1 0528 at $0.45/M.
Llama 4 Scout has a larger context window of 327,680 tokens compared to R1 0528's 163,840 tokens. A larger context window means the model can process longer documents and conversations.