| Signal | GPT-3.5 Turbo 16k | Delta | Qwen2.5 72B Instruct |
|---|---|---|---|
Capabilities | 50 | -- | |
Pricing | 96 | -4 | |
Context window size | 60 | -13 | |
Recency | 0 | -21 | |
Output Capacity | 60 | -10 | |
| Overall Result | 0 wins | of 5 | 4 wins |
Score History
40
current score
Tied
right now
40
current score
OpenAI
Alibaba
Qwen2.5 72B Instruct saves you $444.00/month
That's $5328.00/year compared to GPT-3.5 Turbo 16k at your current usage level of 100K calls/month.
| Metric | GPT-3.5 Turbo 16k | Qwen2.5 72B Instruct | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #300 | #298 | Qwen2.5 72B Instruct |
| Quality Rank | #300 | #298 | Qwen2.5 72B Instruct |
| Adoption Rank | #300 | #298 | Qwen2.5 72B Instruct |
| Parameters | -- | 72B | -- |
| Context Window | 16K | 131K | Qwen2.5 72B Instruct |
| Pricing | $3.00/$4.00/M | $0.36/$0.40/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | GPT-3.5 Turbo 16k |
| Pricing | 96 | 100 | Qwen2.5 72B Instruct |
| Context window size | 60 | 73 | Qwen2.5 72B Instruct |
| Recency | 0 | 21 | Qwen2.5 72B Instruct |
| Output Capacity | 60 | 70 | Qwen2.5 72B 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 40/100 (rank #300), placing it in the top -3% of all 290 models tracked.
Scores 40/100 (rank #298), placing it in the top -2% 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 89% better value per quality point. At 1M tokens/day, you'd spend $11.40/month with Qwen2.5 72B Instruct vs $105.00/month with GPT-3.5 Turbo 16k - a $93.60 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 72B 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 ($0.40/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
GPT-3.5 Turbo 16k 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-3.5 Turbo 16k
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen2.5 72B Instruct
89% lower pricing; better value at scale
Best for Reliability
GPT-3.5 Turbo 16k
Higher uptime and faster response speeds
Best for Prototyping
GPT-3.5 Turbo 16k
Stronger community support and better developer experience
Best for Production
GPT-3.5 Turbo 16k
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-3.5 Turbo 16k | Qwen2.5 72B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Alibaba
Qwen2.5 72B Instruct saves you $9.07/month
That's 89% cheaper than GPT-3.5 Turbo 16k 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 16k | Qwen2.5 72B Instruct |
|---|---|---|
| Context Window | 16K | 131K |
| Max Output Tokens | 4,096 | 16,384 |
| Open Source | No | Yes |
| Created | Aug 28, 2023 | Sep 19, 2024 |