Qwen 2.5 72B Instruct
Apache 2.0Developed by Alibaba Cloud · Released 2025-01-01
What are the token costs and operational benchmarks for Qwen 2.5 72B Instruct?
Architectural Overview
Frontier foundation model developed by Alibaba Cloud featuring Dense Transformer architecture.
Optimal Production Use Cases
Open-source dense flagship model offering state-of-the-art multi-language understanding, mathematical reasoning, and JSON generation.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.35/1M in · $0.4/1M out
$0.18/1M in · $0.72/1M out
Head-to-Head Comparisons Involving Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct vs Claude 3.5 Haiku
Qwen 2.5 72B Instruct is 56.2% cheaper for input tokens ($0.35 vs. $0.80 per 1M tokens) and $0.40 vs. $4.00 for output tokens (2.3x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (280ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Qwen 2.5 72B Instruct vs Claude 3.5 Sonnet
Qwen 2.5 72B Instruct is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (8.6x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (100ms faster than Qwen 2.5 72B Instruct). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Claude 3.7 Sonnet
Qwen 2.5 72B Instruct is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (8.6x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Claude Opus 5
Qwen 2.5 72B Instruct is 93.0% cheaper for input tokens ($0.35 vs. $5.00 per 1M tokens) and $0.40 vs. $25.00 for output tokens (14.3x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (80ms faster than Qwen 2.5 72B Instruct). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Codestral 25.01
Codestral 25.01 is 14.3% cheaper for input tokens ($0.30 vs. $0.35 per 1M tokens) and $0.90 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than Qwen 2.5 72B Instruct). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Composer 2.5
Qwen 2.5 72B Instruct is 80.6% cheaper for input tokens ($0.35 vs. $1.80 per 1M tokens) and $0.40 vs. $7.20 for output tokens (5.1x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (240ms faster than Qwen 2.5 72B Instruct). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs DeepSeek-R1
Qwen 2.5 72B Instruct is 36.4% cheaper for input tokens ($0.35 vs. $0.55 per 1M tokens) and $0.40 vs. $2.19 for output tokens (1.6x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (1380ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs DeepSeek-V3
DeepSeek-V3 is 60.0% cheaper for input tokens ($0.14 vs. $0.35 per 1M tokens) and $0.28 vs. $0.40 for output tokens (2.5x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (80ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 42.0% for DeepSeek-V3.
Qwen 2.5 72B Instruct vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 60.0% cheaper for input tokens ($0.14 vs. $0.35 per 1M tokens) and $0.56 vs. $0.40 for output tokens (2.5x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (270ms faster than Qwen 2.5 72B Instruct). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Fable 5
Qwen 2.5 72B Instruct is 82.5% cheaper for input tokens ($0.35 vs. $2.00 per 1M tokens) and $0.40 vs. $8.00 for output tokens (5.7x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (180ms faster than Qwen 2.5 72B Instruct). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Gemini 2.0 Flash
Gemini 2.0 Flash is 71.4% cheaper for input tokens ($0.10 vs. $0.35 per 1M tokens) and $0.40 vs. $0.40 for output tokens (3.5x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (40ms faster than Qwen 2.5 72B Instruct). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Gemini 3.7 Flash
Gemini 3.7 Flash is 77.1% cheaper for input tokens ($0.08 vs. $0.35 per 1M tokens) and $0.32 vs. $0.40 for output tokens (4.4x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (345ms faster than Qwen 2.5 72B Instruct). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs GLM 5.3 Flash
GLM 5.3 Flash is 57.1% cheaper for input tokens ($0.15 vs. $0.35 per 1M tokens) and $0.50 vs. $0.40 for output tokens (2.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (290ms faster than Qwen 2.5 72B Instruct). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs OpenAI GPT-4o
Qwen 2.5 72B Instruct is 86.0% cheaper for input tokens ($0.35 vs. $2.50 per 1M tokens) and $0.40 vs. $10.00 for output tokens (7.1x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (140ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Qwen 2.5 72B Instruct vs GPT-5.6 Luna
GPT-5.6 Luna is 48.6% cheaper for input tokens ($0.18 vs. $0.35 per 1M tokens) and $0.72 vs. $0.40 for output tokens (1.9x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than Qwen 2.5 72B Instruct). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs GPT-5.6 Sol
Qwen 2.5 72B Instruct is 95.6% cheaper for input tokens ($0.35 vs. $8.00 per 1M tokens) and $0.40 vs. $32.00 for output tokens (22.9x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (0ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs GPT-5.6 Terra
Qwen 2.5 72B Instruct is 76.7% cheaper for input tokens ($0.35 vs. $1.50 per 1M tokens) and $0.40 vs. $6.00 for output tokens (4.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (210ms faster than Qwen 2.5 72B Instruct). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Grok 3
Qwen 2.5 72B Instruct is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (8.6x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (430ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs xAI Grok 4.6
Qwen 2.5 72B Instruct is 82.5% cheaper for input tokens ($0.35 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (5.7x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (140ms faster than Qwen 2.5 72B Instruct). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 48.6% cheaper for input tokens ($0.18 vs. $0.35 per 1M tokens) and $0.40 vs. $0.40 for output tokens (1.9x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (0ms faster than Llama 3.3 70B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Qwen 2.5 72B Instruct vs Mistral Large 2
Qwen 2.5 72B Instruct is 82.5% cheaper for input tokens ($0.35 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (5.7x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (130ms faster than Mistral Large 2). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 39.0% for Mistral Large 2.
Qwen 2.5 72B Instruct vs OpenAI o1
Qwen 2.5 72B Instruct is 97.7% cheaper for input tokens ($0.35 vs. $15.00 per 1M tokens) and $0.40 vs. $60.00 for output tokens (42.9x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (430ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs o3-mini
Qwen 2.5 72B Instruct is 68.2% cheaper for input tokens ($0.35 vs. $1.10 per 1M tokens) and $0.40 vs. $4.40 for output tokens (3.1x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (780ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 65.7% cheaper for input tokens ($0.12 vs. $0.35 per 1M tokens) and $0.36 vs. $0.40 for output tokens (2.9x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Qwen 2.5 72B Instruct vs Qwen 2.5 Max
Qwen 2.5 Max is 20.0% cheaper for input tokens ($0.28 vs. $0.35 per 1M tokens) and $0.84 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (60ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 65.7% cheaper for input tokens ($0.12 vs. $0.35 per 1M tokens) and $0.48 vs. $0.40 for output tokens (2.9x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (300ms faster than Qwen 2.5 72B Instruct). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Frequently Asked Questions & Query Fan-Out
How much does Qwen 2.5 72B Instruct cost per 1M tokens?
Qwen 2.5 72B Instruct costs $0.35 per million prompt (input) tokens and $0.40 per million completion (output) tokens.
What is the context window for Qwen 2.5 72B Instruct?
Qwen 2.5 72B Instruct supports a maximum context window of 134,144 tokens, with a maximum single-generation output of 8,192 tokens.
What are the primary use cases for Qwen 2.5 72B Instruct?
Open-source dense flagship model offering state-of-the-art multi-language understanding, mathematical reasoning, and JSON generation.