Alibaba's 72B parameter flagship matching GPT-4o on MMLU (86.1) and coding benchmarks at a fraction of commercial API costs.
Serverless Input / 1M
$0.35
DeepInfra / Together AI
Serverless Output / 1M
$0.40
High throughput FP8
Context Window
131,072
128k native tokens
MMLU Score
86.1%
Outperforms Llama 3.3
🧮 Live Qwen 2.5 72B Cost Calculator
Estimate your monthly API expenditures with Qwen 2.5 72B compared directly against closed-source alternatives.
Input Tokens (Millions / month)25M tokens
Output Tokens (Millions / month)10M tokens
Qwen 2.5 72B Cost
$12.75
GPT-4o ($2.50 / $10.00)
$162.50
Claude 3.5 Sonnet ($3 / $15)
$225.00
Monthly Savings
94.3%
🌐 Qwen 2.5 72B Provider Rate Card
Current audited rates across major serverless model providers:
Provider
Input / 1M
Output / 1M
Context
Avg Latency / Speed
Rating
DeepInfra
$0.35
$0.40
128k
~68 tok/s
Cheapest
Together AI
$0.90
$0.90
32k / 128k
~75 tok/s
Enterprise SLA
Fireworks AI
$0.90
$0.90
128k
~88 tok/s
Fastest TTFT
OpenRouter
$0.35 - $0.90
$0.40 - $0.90
128k
Variable
Dynamic Failover
🖥️ Self-Hosting Specs (vLLM & SGLang)
If you prefer self-hosting on cloud GPU instances (RunPod, Lambda, AWS), here is the required hardware footprint:
AWQ / GPTQ 4-Bit: ~48GB VRAM. Runs on 2x RTX 3090/4090 (24GB each) or 1x NVIDIA A100 (80GB). Cost: ~$0.70/hr on RunPod.
FP8 Quantization: ~85GB VRAM. Runs on 2x NVIDIA A100 (80GB) or 2x H100 (80GB). Offers virtually zero accuracy degradation.
FP16 Full Precision: ~160GB VRAM. Requires 4x A100 (80GB) or 2x H200 (141GB). Recommended only for fine-tuning or evaluation.
💡 Rule of thumb: Unless you generate over 350M tokens per month consistently, serverless API providers ($0.35/$0.40) are cheaper than running a 24/7 dedicated 2x A100 instance.
Frequently Asked Questions
What is the average API cost for Qwen 2.5 72B?
Across major serverless providers (DeepInfra, Together AI, Fireworks AI, and OpenRouter), Qwen 2.5 72B averages $0.35 per 1 million input tokens and $0.40 per 1 million output tokens, making it roughly 88% cheaper than GPT-4o while matching or exceeding it on MMLU and coding benchmarks.
How does Qwen 2.5 72B compare with Llama 3.3 70B?
Qwen 2.5 72B outperforms Llama 3.3 70B on mathematical reasoning (MATH 83.1 vs 70.0) and multilingual tasks, while Llama 3.3 70B holds a slight edge in conversational English nuance and instruction following. Both share similar pricing ($0.35-$0.60/1M tokens) and hardware footprints.
What GPU hardware is needed to self-host Qwen 2.5 72B?
For 4-bit quantized inference (AWQ/GPTQ), Qwen 2.5 72B requires ~48GB VRAM (running on 2x RTX 3090/4090 or 1x A100 80GB). For FP8 or unquantized FP16 inference with 128k context, 2x to 4x 80GB A100/H100 SXM GPUs are required.