| Signal | Llama 4 Scout | Delta | o1 |
|---|---|---|---|
Capabilities | 67 | -17 | |
Pricing | 0 | -60 | |
Context window size | 88 | +3 | |
Recency | 70 | +20 | |
Output Capacity | 70 | -13 | |
Benchmarks | 0 | -81 | |
| Overall Result | 2 wins | of 6 | 4 wins |
3
days ranked higher
0
days
27
days ranked higher
Meta
OpenAI
Llama 4 Scout saves you $4477.00/month
That's $53724.00/year compared to o1 at your current usage level of 100K calls/month.
| Metric | Llama 4 Scout | o1 | Winner |
|---|---|---|---|
| Overall Score | 72 | 76 | o1 |
| Rank | #139 | #105 | o1 |
| Quality Rank | #139 | #105 | o1 |
| Adoption Rank | #139 | #105 | o1 |
| Parameters | -- | -- | -- |
| Context Window | 328K | 200K | Llama 4 Scout |
| Pricing | $0.08/$0.30/M | $15.00/$60.00/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | o1 |
| Pricing | 0 | 60 | o1 |
| Context window size | 88 | 84 | Llama 4 Scout |
| Recency | 70 | 50 | Llama 4 Scout |
| Output Capacity | 70 | 83 | o1 |
| Benchmarks | -- | 82 | o1 |
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 #139), placing it in the top 52% of all 290 models tracked.
Scores 76/100 (rank #105), placing it in the top 64% of all 290 models tracked.
With only a 4-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 99% better value per quality point. At 1M tokens/day, you'd spend $5.70/month with Llama 4 Scout vs $1125.00/month with o1 - a $1119.30 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 (76/100) 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
o1 has a moderate advantage with a 3.5-point lead in composite score. It wins on more signal dimensions, but Llama 4 Scout has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Llama 4 Scout
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Scout
99% 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 | o1 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
OpenAI
Llama 4 Scout saves you $98.50/month
That's 99% cheaper than o1 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 | o1 |
|---|---|---|
| Context Window | 328K | 200K |
| Max Output Tokens | 16,384 | 100,000 |
| Open Source | Yes | No |
| Created | Apr 5, 2025 | Dec 17, 2024 |
o1 scores 76/100 (rank #105) compared to Llama 4 Scout's 72/100 (rank #139), giving it a 4-point advantage. o1 is the stronger overall choice, though Llama 4 Scout may excel in specific areas like cost efficiency.
Llama 4 Scout is ranked #139 and o1 is ranked #105 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 o1's $60.00/M output tokens - 200.0x more expensive. Input token pricing: Llama 4 Scout at $0.08/M vs o1 at $15.00/M.
Llama 4 Scout has a larger context window of 327,680 tokens compared to o1's 200,000 tokens. A larger context window means the model can process longer documents and conversations.