Qwen2-72B vs Qwen2.5-72B-Instruct
Qwen2-72B (2024) and Qwen2.5-72B-Instruct (2024) are compact production models from Alibaba. Qwen2-72B ships a 128K-token context window, while Qwen2.5-72B-Instruct ships a 128K-token context window. On HumanEval, Qwen2.5-72B-Instruct leads by 25.6 pts. On pricing, Qwen2.5-72B-Instruct costs $0.12/1M input tokens versus $0.45/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen2.5-72B-Instruct is ~275% cheaper at $0.12/1M; pay for Qwen2-72B only for provider fit.
Specs
| Released | 2024-06-05 | 2024-06-07 |
| Context window | 128K | 128K |
| Parameters | 72.71B | 72.7B |
| Architecture | decoder only | decoder only |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Qwen2-72B | Qwen2.5-72B-Instruct | |
|---|---|---|
| Input price | $0.45/1M tokens | $0.12/1M tokens |
| Output price | $0.65/1M tokens | $0.39/1M tokens |
| Providers |
Capabilities
| Qwen2-72B | Qwen2.5-72B-Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Qwen2-72B | Qwen2.5-72B-Instruct |
|---|---|---|
| HumanEval | 67.1 | 92.7 |
| Massive Multitask Language Understanding | 84.2 | 88.2 |
Deep dive
On shared benchmark coverage, HumanEval has Qwen2-72B at 67.1 and Qwen2.5-72B-Instruct at 92.7, with Qwen2.5-72B-Instruct ahead by 25.6 points; Massive Multitask Language Understanding has Qwen2-72B at 84.2 and Qwen2.5-72B-Instruct at 88.2, with Qwen2.5-72B-Instruct ahead by 4 points. The largest visible gap is 25.6 points on HumanEval, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Qwen2-72B lists $0.45/1M input and $0.65/1M output tokens, while Qwen2.5-72B-Instruct lists $0.12/1M input and $0.39/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $0.31 per million blended tokens. Availability is 4 providers versus 7, so concentration risk also matters.
Choose Qwen2-72B when provider fit are central to the workload. Choose Qwen2.5-72B-Instruct when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Qwen2-72B or Qwen2.5-72B-Instruct?
Qwen2-72B supports 128K tokens, while Qwen2.5-72B-Instruct supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, Qwen2-72B or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. Qwen2-72B costs $0.45/1M input and $0.65/1M output tokens. Qwen2.5-72B-Instruct costs $0.12/1M input and $0.39/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Qwen2-72B or Qwen2.5-72B-Instruct open source?
Qwen2-72B is listed under Apache 2.0. Qwen2.5-72B-Instruct is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for structured outputs, Qwen2-72B or Qwen2.5-72B-Instruct?
Both Qwen2-72B and Qwen2.5-72B-Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run Qwen2-72B and Qwen2.5-72B-Instruct?
Qwen2-72B is available on Fireworks AI, DeepInfra, Together AI, and Microsoft Foundry. Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Qwen2-72B over Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is ~275% cheaper at $0.12/1M; pay for Qwen2-72B only for provider fit. If your workload also depends on provider fit, start with Qwen2-72B; if it depends on provider fit, run the same evaluation with Qwen2.5-72B-Instruct.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.