| Signal | Command R (08-2024) | Delta | Llama 4 Scout |
|---|---|---|---|
Capabilities | 50 | -17 | |
Pricing | 1 | +0 | |
Context window size | 81 | -6 | |
Recency | 30 | -40 | |
Output Capacity | 60 | -10 | |
| Overall Result | 1 wins | of 5 | 4 wins |
0
days ranked higher
0
days
30
days ranked higher
Cohere
Meta
Llama 4 Scout saves you $22.00/month
That's $264.00/year compared to Command R (08-2024) at your current usage level of 100K calls/month.
| Metric | Command R (08-2024) | Llama 4 Scout | Winner |
|---|---|---|---|
| Overall Score | 54 | 72 | Llama 4 Scout |
| Rank | #235 | #108 | Llama 4 Scout |
| Quality Rank | #235 | #108 | Llama 4 Scout |
| Adoption Rank | #235 | #108 | Llama 4 Scout |
| Parameters | -- | -- | -- |
| Context Window | 128K | 328K | Llama 4 Scout |
| Pricing | $0.15/$0.60/M | $0.08/$0.30/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 67 | Llama 4 Scout |
| Pricing | 1 | 0 | Command R (08-2024) |
| Context window size | 81 | 88 | Llama 4 Scout |
| Recency | 30 | 70 | Llama 4 Scout |
| Output Capacity | 60 | 70 | Llama 4 Scout |
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 54/100 (rank #235), placing it in the top 19% of all 290 models tracked.
Scores 72/100 (rank #108), placing it in the top 63% of all 290 models tracked.
Llama 4 Scout has a 18-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Llama 4 Scout offers 49% better value per quality point. At 1M tokens/day, you'd spend $5.70/month with Llama 4 Scout vs $11.25/month with Command R (08-2024) — a $5.55 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 (72/100) 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 clearly outperforms Command R (08-2024) with a significant 18.299999999999997-point lead. For most general use cases, Llama 4 Scout is the stronger choice. However, Command R (08-2024) may still excel in niche scenarios.
Best for Quality
Command R (08-2024)
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Scout
49% lower pricing; better value at scale
Best for Reliability
Command R (08-2024)
Higher uptime and faster response speeds
Best for Prototyping
Command R (08-2024)
Stronger community support and better developer experience
Best for Production
Command R (08-2024)
Wider enterprise adoption and proven at scale
by Cohere
| Capability | Command R (08-2024) | Llama 4 Scout |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Cohere
Meta
Llama 4 Scout saves you $0.4860/month
That's 49% cheaper than Command R (08-2024) 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 | Command R (08-2024) | Llama 4 Scout |
|---|---|---|
| Context Window | 128K | 328K |
| Max Output Tokens | 4,000 | 16,384 |
| Open Source | Yes | Yes |
| Created | Aug 30, 2024 | Apr 5, 2025 |
Llama 4 Scout scores 72/100 (rank #108) compared to Command R (08-2024)'s 54/100 (rank #235), giving it a 18-point advantage. Llama 4 Scout is the stronger overall choice, though Command R (08-2024) may excel in specific areas like certain benchmarks.
Command R (08-2024) is ranked #235 and Llama 4 Scout is ranked #108 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 Command R (08-2024)'s $0.60/M output tokens — 2.0x more expensive. Input token pricing: Command R (08-2024) at $0.15/M vs Llama 4 Scout at $0.08/M.
Llama 4 Scout has a larger context window of 327,680 tokens compared to Command R (08-2024)'s 128,000 tokens. A larger context window means the model can process longer documents and conversations.