| Signal | GPT-3.5 Turbo Instruct | Delta | WizardLM-2 8x22B |
|---|---|---|---|
Capabilities | 33 | +17 | |
Pricing | 2 | +1 | |
Context window size | 57 | -19 | |
Recency | 0 | -3 | |
Output Capacity | 60 | -5 | |
| Overall Result | 2 wins | of 5 | 3 wins |
11
days higher
3
days
16
days higher
OpenAI
Microsoft
WizardLM-2 8x22B saves you $157.00/month
That's $1884.00/year compared to GPT-3.5 Turbo Instruct at your current usage level of 100K calls/month.
| Metric | GPT-3.5 Turbo Instruct | WizardLM-2 8x22B | Winner |
|---|---|---|---|
| Overall Score | 32 | 32 | GPT-3.5 Turbo Instruct |
| Rank | #304 | #306 | GPT-3.5 Turbo Instruct |
| Quality Rank | #304 | #306 | GPT-3.5 Turbo Instruct |
| Adoption Rank | #304 | #306 | GPT-3.5 Turbo Instruct |
| Parameters | -- | 22B | -- |
| Context Window | 4K | 66K | WizardLM-2 8x22B |
| Pricing | $1.50/$2.00/M | $0.62/$0.62/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 17 | GPT-3.5 Turbo Instruct |
| Pricing | 2 | 1 | GPT-3.5 Turbo Instruct |
| Context window size | 57 | 76 | WizardLM-2 8x22B |
| Recency | 0 | 3 | WizardLM-2 8x22B |
| Output Capacity | 60 | 65 | WizardLM-2 8x22B |
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 32/100 (rank #304), placing it in the top -4% of all 290 models tracked.
Scores 32/100 (rank #306), placing it in the top -5% of all 290 models tracked.
With only a 0-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.
WizardLM-2 8x22B offers 65% better value per quality point. At 1M tokens/day, you'd spend $18.60/month with WizardLM-2 8x22B vs $52.50/month with GPT-3.5 Turbo Instruct - a $33.90 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. WizardLM-2 8x22B 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 ($0.62/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
GPT-3.5 Turbo Instruct and WizardLM-2 8x22B are extremely close in overall performance (only 0.40000000000000213 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-3.5 Turbo Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
WizardLM-2 8x22B
65% lower pricing; better value at scale
Best for Reliability
GPT-3.5 Turbo Instruct
Higher uptime and faster response speeds
Best for Prototyping
GPT-3.5 Turbo Instruct
Stronger community support and better developer experience
Best for Production
GPT-3.5 Turbo Instruct
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-3.5 Turbo Instruct | WizardLM-2 8x22B |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Microsoft
WizardLM-2 8x22B saves you $3.24/month
That's 64% cheaper than GPT-3.5 Turbo Instruct 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 | GPT-3.5 Turbo Instruct | WizardLM-2 8x22B |
|---|---|---|
| Context Window | 4K | 66K |
| Max Output Tokens | 4,096 | 8,000 |
| Open Source | No | Yes |
| Created | Sep 28, 2023 | Apr 16, 2024 |
GPT-3.5 Turbo Instruct scores 32/100 (rank #304) compared to WizardLM-2 8x22B's 32/100 (rank #306), giving it a 0-point advantage. GPT-3.5 Turbo Instruct is the stronger overall choice, though WizardLM-2 8x22B may excel in specific areas like cost efficiency.
GPT-3.5 Turbo Instruct is ranked #304 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.
WizardLM-2 8x22B is cheaper at $0.62/M output tokens vs GPT-3.5 Turbo Instruct's $2.00/M output tokens - 3.2x more expensive. Input token pricing: GPT-3.5 Turbo Instruct at $1.50/M vs WizardLM-2 8x22B at $0.62/M.
WizardLM-2 8x22B has a larger context window of 65,535 tokens compared to GPT-3.5 Turbo Instruct's 4,095 tokens. A larger context window means the model can process longer documents and conversations.