| Signal | Llama 3 8B Instruct | Delta | WizardLM-2 8x22B |
|---|---|---|---|
Capabilities | 50 | +33 | |
Benchmarks | 24 | +24 | |
Pricing | 0 | -1 | |
Context window size | 62 | -14 | |
Recency | 3 | +0 | |
Output Capacity | 70 | +5 | |
| Overall Result | 4 wins | of 6 | 2 wins |
5
days higher
2
days
23
days higher
Meta
Microsoft
Llama 3 8B Instruct saves you $88.00/month
That's $1056.00/year compared to WizardLM-2 8x22B at your current usage level of 100K calls/month.
| Metric | Llama 3 8B Instruct | WizardLM-2 8x22B | Winner |
|---|---|---|---|
| Overall Score | 30 | 32 | WizardLM-2 8x22B |
| Rank | #308 | #306 | WizardLM-2 8x22B |
| Quality Rank | #308 | #306 | WizardLM-2 8x22B |
| Adoption Rank | #308 | #306 | WizardLM-2 8x22B |
| Parameters | 8B | 22B | -- |
| Context Window | 8K | 66K | WizardLM-2 8x22B |
| Pricing | $0.03/$0.04/M | $0.62/$0.62/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 17 | Llama 3 8B Instruct |
| Benchmarks | 24 | -- | Llama 3 8B Instruct |
| Pricing | 0 | 1 | WizardLM-2 8x22B |
| Context window size | 62 | 76 | WizardLM-2 8x22B |
| Recency | 3 | 3 | Llama 3 8B Instruct |
| Output Capacity | 70 | 65 | Llama 3 8B Instruct |
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 30/100 (rank #308), placing it in the top -6% of all 290 models tracked.
Scores 32/100 (rank #306), placing it in the top -5% 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 3 8B Instruct offers 94% better value per quality point. At 1M tokens/day, you'd spend $1.05/month with Llama 3 8B Instruct vs $18.60/month with WizardLM-2 8x22B - a $17.55 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 3 8B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (66K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.04/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (32/100) correlates with better nuance, coherence, and style in long-form content
Llama 3 8B Instruct and WizardLM-2 8x22B are extremely close in overall performance (only 1.6999999999999993 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 3 8B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3 8B Instruct
94% lower pricing; better value at scale
Best for Reliability
Llama 3 8B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3 8B Instruct
Stronger community support and better developer experience
Best for Production
Llama 3 8B Instruct
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3 8B Instruct | WizardLM-2 8x22B |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Microsoft
Llama 3 8B Instruct saves you $1.76/month
That's 95% 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 3 8B Instruct | WizardLM-2 8x22B |
|---|---|---|
| Context Window | 8K | 66K |
| Max Output Tokens | 16,384 | 8,000 |
| Open Source | Yes | Yes |
| Created | Apr 18, 2024 | Apr 16, 2024 |
WizardLM-2 8x22B scores 32/100 (rank #306) compared to Llama 3 8B Instruct's 30/100 (rank #308), giving it a 2-point advantage. WizardLM-2 8x22B is the stronger overall choice, though Llama 3 8B Instruct may excel in specific areas like cost efficiency.
Llama 3 8B Instruct is ranked #308 and WizardLM-2 8x22B is ranked #306 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 3 8B Instruct is cheaper at $0.04/M output tokens vs WizardLM-2 8x22B's $0.62/M output tokens - 15.5x more expensive. Input token pricing: Llama 3 8B Instruct at $0.03/M vs WizardLM-2 8x22B at $0.62/M.
WizardLM-2 8x22B has a larger context window of 65,535 tokens compared to Llama 3 8B Instruct's 8,192 tokens. A larger context window means the model can process longer documents and conversations.