| Signal | GPT-4o (2024-08-06) | Delta | Qwen2.5 72B Instruct |
|---|---|---|---|
Capabilities | 67 | +17 | |
Pricing | 90 | -10 | |
Context window size | 81 | +9 | |
Recency | 22 | -8 | |
Output Capacity | 70 | -- | |
| Overall Result | 2 wins | of 5 | 2 wins |
Score History
40
current score
Tied
right now
40
current score
OpenAI
Alibaba
Qwen2.5 72B Instruct saves you $718.50/month
That's $8622.00/year compared to GPT-4o (2024-08-06) at your current usage level of 100K calls/month.
| Metric | GPT-4o (2024-08-06) | Qwen2.5 72B Instruct | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #284 | #283 | Qwen2.5 72B Instruct |
| Quality Rank | #284 | #283 | Qwen2.5 72B Instruct |
| Adoption Rank | #284 | #283 | Qwen2.5 72B Instruct |
| Parameters | -- | 72B | -- |
| Context Window | 128K | 33K | GPT-4o (2024-08-06) |
| Pricing | $2.50/$10.00/M | $0.12/$0.39/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | GPT-4o (2024-08-06) |
| Pricing | 90 | 100 | Qwen2.5 72B Instruct |
| Context window size | 81 | 72 | GPT-4o (2024-08-06) |
| Recency | 22 | 30 | Qwen2.5 72B Instruct |
| Output Capacity | 70 | 70 | GPT-4o (2024-08-06) |
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 40/100 (rank #284), placing it in the top 2% of all 290 models tracked.
Scores 40/100 (rank #283), placing it in the top 3% 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.
Qwen2.5 72B Instruct offers 96% better value per quality point. At 1M tokens/day, you'd spend $7.65/month with Qwen2.5 72B Instruct vs $187.50/month with GPT-4o (2024-08-06) - a $179.85 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. Qwen2.5 72B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (128K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.39/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) 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
GPT-4o (2024-08-06) and Qwen2.5 72B Instruct are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
GPT-4o (2024-08-06)
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen2.5 72B Instruct
96% lower pricing; better value at scale
Best for Reliability
GPT-4o (2024-08-06)
Higher uptime and faster response speeds
Best for Prototyping
GPT-4o (2024-08-06)
Stronger community support and better developer experience
Best for Production
GPT-4o (2024-08-06)
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-4o (2024-08-06) | Qwen2.5 72B Instruct |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Alibaba
Qwen2.5 72B Instruct saves you $15.82/month
That's 96% cheaper than GPT-4o (2024-08-06) 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-4o (2024-08-06) | Qwen2.5 72B Instruct |
|---|---|---|
| Context Window | 128K | 33K |
| Max Output Tokens | 16,384 | 16,384 |
| Open Source | No | Yes |
| Created | Aug 6, 2024 | Sep 19, 2024 |
Both GPT-4o (2024-08-06) and Qwen2.5 72B Instruct score 40/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
GPT-4o (2024-08-06) is ranked #284 and Qwen2.5 72B Instruct is ranked #283 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
Qwen2.5 72B Instruct is cheaper at $0.39/M output tokens vs GPT-4o (2024-08-06)'s $10.00/M output tokens - 25.6x more expensive. Input token pricing: GPT-4o (2024-08-06) at $2.50/M vs Qwen2.5 72B Instruct at $0.12/M.
GPT-4o (2024-08-06) has a larger context window of 128,000 tokens compared to Qwen2.5 72B Instruct's 32,768 tokens. A larger context window means the model can process longer documents and conversations.