| Signal | Qwen2.5 7B Instruct | Delta | WizardLM-2 8x22B |
|---|---|---|---|
Capabilities | 50 | +17 | |
Benchmarks | 39 | +8 | |
Pricing | 100 | +1 | |
Context window size | 73 | +4 | |
Recency | 21 | +21 | |
Output Capacity | 75 | +10 | |
| Overall Result | 6 wins | of 6 | 0 wins |
Score History
38.2
current score
Qwen2.5 7B Instruct
right now
28.5
current score
Alibaba
Microsoft
Qwen2.5 7B Instruct saves you $84.00/month
That's $1008.00/year compared to WizardLM-2 8x22B at your current usage level of 100K calls/month.
| Metric | Qwen2.5 7B Instruct | WizardLM-2 8x22B | Winner |
|---|---|---|---|
| Overall Score | 38 | 29 | Qwen2.5 7B Instruct |
| Rank | #301 | #312 | Qwen2.5 7B Instruct |
| Quality Rank | #301 | #312 | Qwen2.5 7B Instruct |
| Adoption Rank | #301 | #312 | Qwen2.5 7B Instruct |
| Parameters | 7B | 22B | -- |
| Context Window | 131K | 66K | Qwen2.5 7B Instruct |
| Pricing | $0.04/$0.10/M | $0.62/$0.62/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 33 | Qwen2.5 7B Instruct |
| Benchmarks | 39 | 31 | Qwen2.5 7B Instruct |
| Pricing | 100 | 99 | Qwen2.5 7B Instruct |
| Context window size | 73 | 69 | Qwen2.5 7B Instruct |
| Recency | 21 | 0 | Qwen2.5 7B Instruct |
| Output Capacity | 75 | 65 | Qwen2.5 7B Instruct |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 38/100 (rank #301), placing it in the top -3% of all 290 models tracked.
Scores 29/100 (rank #312), placing it in the top -7% of all 290 models tracked.
Qwen2.5 7B Instruct has a 10-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Qwen2.5 7B Instruct offers 89% better value per quality point. At 1M tokens/day, you'd spend $2.10/month with Qwen2.5 7B Instruct vs $18.60/month with WizardLM-2 8x22B - a $16.50 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
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Qwen2.5 7B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (38/100) correlates with better nuance, coherence, and style in long-form content
Qwen2.5 7B Instruct has a moderate advantage with a 9.700000000000003-point lead in composite score. It wins on more signal dimensions, but WizardLM-2 8x22B has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Qwen2.5 7B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen2.5 7B Instruct
89% lower pricing; better value at scale
Best for Reliability
Qwen2.5 7B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Qwen2.5 7B Instruct
Stronger community support and better developer experience
Best for Production
Qwen2.5 7B Instruct
Wider enterprise adoption and proven at scale
by Alibaba
| Capability | Qwen2.5 7B Instruct | WizardLM-2 8x22B |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Alibaba
Microsoft
Qwen2.5 7B Instruct saves you $1.67/month
That's 90% 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 | Qwen2.5 7B Instruct | WizardLM-2 8x22B |
|---|---|---|
| Context Window | 131K | 66K |
| Max Output Tokens | 32,768 | 8,000 |
| Open Source | Yes | Yes |
| Created | Oct 16, 2024 | Apr 16, 2024 |