Qwen2.5 72B
Latest Other Qwen2.5 Generally Available Sep 2024
Alibaba's top open-weight model at 72B. Achieves 86.1 on MMLU and 83.1 on MATH, with 130K context and strong multilingual + code capabilities. 580K+ HuggingFace downloads.
Context
131K
tokens
Input
$0.40
per MTok
Output
$1.20
per MTok
Modalities
Input
Text Code
Output
Text Code
Code Examples
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-72B-Instruct",
"messages": [
{ "role": "user", "content": "Explain quantum entanglement in one sentence." }
]
}' API Parameters
| Name | Type | Description |
|---|---|---|
frequency_penalty | number | Penalize tokens by their frequency so far. Positive values reduce repetition. |
max_completion_tokens | integer | Maximum number of tokens the model may generate in the response. |
presence_penalty | number | Penalize tokens that have appeared at all so far. Positive values encourage new topics. |
response_format | one of | Constrain output to a JSON schema or an enum (structured outputs). |
seed | integer | Deterministic seed for sampling. Same seed + same prompt produces identical output. |
stop | array | Up to 4 sequences where the API will stop generating tokens. |
stream | boolean | Stream partial responses as Server-Sent Events. |
temperature | number | Sampling temperature; higher values produce more random output. 0 is deterministic. |
tool_choice | one of | Controls which (if any) tool is called: "none", "auto", "required", or a specific tool. |
tools | array | List of tools (functions) the model may call. |
top_p | number | Nucleus sampling: consider only tokens whose cumulative probability ≥ top_p. |
Standard OpenAI-compatible parameters. Consult the provider docs for model-specific behaviour.
Benchmark Scores
| Benchmark | Score |
|---|---|
| MMLU | 86.1% |
| MATH | 83.1% |
| HumanEval | 86.6% |
Performance
55
tok / sec
output speed
Source: Qwen2.5 technical report + llm-stats.com, April 2026
Strengths & Limitations
Best For
Best-in-class open-weight MMLU at 72B scale
Strong math and code
130K context
Large HuggingFace community
Limitations
No vision modality (base model)
Qwen licence (non-commercial restrictions for some uses)
Self-hosting requires ~4× A100 GPUs
Tags
Open WeightsCodingMathLong ContextMultilingual