Alibaba's premier open-weights flagship LLM matching top closed models in coding, mathematics, and multilingual instruction following.
Technical Specifications
- Provider
- Alibaba
- Category
- open-source
- Release Date
- 2024-09
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- qwen-2-5-72b-instruct
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in Alibaba lineup)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.35/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.07/1M tokens
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 45 GB VRAM (4-bit Quantized) · 155 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Qwen 2.5 72B Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~433 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Qwen 2.5 72B:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 45.2%
- GPQA Diamond
- 52.8%
- MMLU-Pro
- 71.4%
- HumanEval
- 86.4%
- Humanity's Last Exam
- 7.2%
- Terminal-Bench
- 28.1%
- SciCode
- 28.9%
- LiveCodeBench
- 46.5%
- AIME
- 59.2%
- Long-Context Reasoning
- 66.8%
- Chatbot Arena Elo
- 1282pts
Strengths
- Superior mathematical and code generation performance among dense open-weights models
- Extensive support for over 29 languages with culturally authentic nuances
- Full 128K context window with high retrieval fidelity
- Fully customizable and license-friendly for enterprise deployment
Limitations & Trade-offs
- Requires substantial GPU memory (2x A100 or 4x RTX 4090) to run at full FP16 precision
- Lacks native vision processing (text-only model)
Editorial Assessment
The reigning open-source dense model of late 2024, competing directly with GPT-4o on technical workloads.
Best for: Self-hosted enterprise agents, mathematical computing, and private coding assistants
Compare Qwen 2.5 72B
Frequently Asked Questions about Qwen 2.5 72B
How much does the Qwen 2.5 72B API cost per 1M tokens?+
Qwen 2.5 72B by Alibaba is priced at $0.35 per 1M input tokens and $0.7 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.07 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Qwen 2.5 72B pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Qwen 2.5 72B's context window?+
Qwen 2.5 72B features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Qwen 2.5 72B locally on my own GPU (Open Weights)?+
Yes, Qwen 2.5 72B (72B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Alibaba train on data sent to the Qwen 2.5 72B API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Qwen 2.5 72B perform on coding and reasoning benchmarks?+
Qwen 2.5 72B achieves a composite Intelligence Index of 44/100, scoring 45.2% on SWE-bench Verified, 52.8% on GPQA Diamond, 71.4% on MMLU-Pro, and 1282 pts on Chatbot Arena Elo.
What is Qwen 2.5 72B best used for?+
Qwen 2.5 72B is best suited for: Self-hosted enterprise agents, mathematical computing, and private coding assistants. Assessment: The reigning open-source dense model of late 2024, competing directly with GPT-4o on technical workloads.
Alibaba Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Alibaba's flagship reasoning tier:
Qwen3.8 Max Preview
Qwen3.7 Plus
Qwen3.7 Max
Qwen3.6 Max Preview
Qwen3.6 Plus
Qwen3.6 Flash
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Qwen 2.5 72B:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.