| Signal | Llama 4 Scout | Delta | WizardLM-2 8x22B |
|---|---|---|---|
Capabilities | 67 | +50 | |
Pricing | 0 | 0 | |
Context window size | 88 | +11 | |
Recency | 70 | +65 | |
Output Capacity | 70 | +5 | |
| Overall Result | 4 wins | of 5 | 1 wins |
30
days ranked higher
0
days
0
days ranked higher
Meta
Microsoft
Llama 4 Scout saves you $70.00/month
That's $840.00/year compared to WizardLM-2 8x22B at your current usage level of 100K calls/month.
| Metric | Llama 4 Scout | WizardLM-2 8x22B | Winner |
|---|---|---|---|
| Overall Score | 72 | 34 | Llama 4 Scout |
| Rank | #108 | #290 | Llama 4 Scout |
| Quality Rank | #108 | #290 | Llama 4 Scout |
| Adoption Rank | #108 | #290 | Llama 4 Scout |
| Parameters | -- | 22B | -- |
| Context Window | 328K | 66K | Llama 4 Scout |
| Pricing | $0.08/$0.30/M | $0.62/$0.62/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 17 | Llama 4 Scout |
| Pricing | 0 | 1 | WizardLM-2 8x22B |
| Context window size | 88 | 76 | Llama 4 Scout |
| Recency | 70 | 6 | Llama 4 Scout |
| Output Capacity | 70 | 65 | 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 72/100 (rank #108), placing it in the top 63% of all 290 models tracked.
Scores 34/100 (rank #290), placing it in the top 0% of all 290 models tracked.
Llama 4 Scout has a 38-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Llama 4 Scout offers 69% better value per quality point. At 1M tokens/day, you'd spend $5.70/month with Llama 4 Scout vs $18.60/month with WizardLM-2 8x22B — a $12.90 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 WizardLM-2 8x22B with a significant 38.39999999999999-point lead. For most general use cases, Llama 4 Scout is the stronger choice. However, WizardLM-2 8x22B may still excel in niche scenarios.
Best for Quality
Llama 4 Scout
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Scout
69% 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 | WizardLM-2 8x22B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Microsoft
Llama 4 Scout saves you $1.36/month
That's 73% cheaper than WizardLM-2 8x22B 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 | WizardLM-2 8x22B |
|---|---|---|
| Context Window | 328K | 66K |
| Max Output Tokens | 16,384 | 8,000 |
| Open Source | Yes | Yes |
| Created | Apr 5, 2025 | Apr 16, 2024 |
Llama 4 Scout scores 72/100 (rank #108) compared to WizardLM-2 8x22B's 34/100 (rank #290), giving it a 38-point advantage. Llama 4 Scout is the stronger overall choice, though WizardLM-2 8x22B may excel in specific areas like certain benchmarks.
Llama 4 Scout is ranked #108 and WizardLM-2 8x22B is ranked #290 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 WizardLM-2 8x22B's $0.62/M output tokens — 2.1x more expensive. Input token pricing: Llama 4 Scout at $0.08/M vs WizardLM-2 8x22B at $0.62/M.
Llama 4 Scout has a larger context window of 327,680 tokens compared to WizardLM-2 8x22B's 65,535 tokens. A larger context window means the model can process longer documents and conversations.