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GPT-5 Pro vs Qwen2.5-72B-Instruct

GPT-5 Pro (2025) and Qwen2.5-72B-Instruct (2024) are compact production models from OpenAI and Alibaba. GPT-5 Pro ships a 400K-token context window, while Qwen2.5-72B-Instruct ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

GPT-5 Pro is safer overall; choose Qwen2.5-72B-Instruct when provider fit matters.

Decision scorecard

Local evidence first
SignalGPT-5 ProQwen2.5-72B-Instruct
Decision fitCoding, RAG, and AgentsCoding, RAG, and Long context
Context window400K128K
Cheapest output-$0.54/1M tokens
Provider routes0 tracked7 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-5 Pro when...
  • GPT-5 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-5 Pro uniquely exposes Vision, Multimodal, and Function calling in local model data.
  • Local decision data tags GPT-5 Pro for Coding, RAG, and Agents.
Choose Qwen2.5-72B-Instruct when...
  • Qwen2.5-72B-Instruct has broader tracked provider coverage for fallback and procurement flexibility.
  • Local decision data tags Qwen2.5-72B-Instruct for Coding, RAG, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

GPT-5 Pro

Unavailable

No complete token price in local provider data

Qwen2.5-72B-Instruct

$279

Cheapest tracked route: Chutes AI

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

GPT-5 Pro -> Qwen2.5-72B-Instruct
  • No overlapping tracked provider route is sourced for GPT-5 Pro and Qwen2.5-72B-Instruct; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Qwen2.5-72B-Instruct -> GPT-5 Pro
  • No overlapping tracked provider route is sourced for Qwen2.5-72B-Instruct and GPT-5 Pro; plan for SDK, billing, or endpoint changes.
  • GPT-5 Pro adds Vision, Multimodal, and Function calling in local capability data.

Specs

Specification
Released2025-10-012024-06-07
Context window400K128K
Parameters72.7B
Architecturedecoder onlydecoder only
LicenseProprietaryApache 2.0
Knowledge cutoff2024-09-

Pricing and availability

Pricing attributeGPT-5 ProQwen2.5-72B-Instruct
Input price-$0.18/1M tokens
Output price-$0.54/1M tokens
Providers-

Capabilities

CapabilityGPT-5 ProQwen2.5-72B-Instruct
VisionYesNo
MultimodalYesNo
ReasoningNoNo
Function callingYesNo
Tool useYesNo
Structured outputsYesYes
Code executionYesNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GPT-5 Pro, multimodal input: GPT-5 Pro, function calling: GPT-5 Pro, tool use: GPT-5 Pro, and code execution: GPT-5 Pro. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

Pricing coverage is uneven: GPT-5 Pro has no token price sourced yet and Qwen2.5-72B-Instruct has $0.18/1M input tokens. Provider availability is 0 tracked routes versus 7. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose GPT-5 Pro when coding workflow support and larger context windows are central to the workload. Choose Qwen2.5-72B-Instruct when provider fit 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, GPT-5 Pro or Qwen2.5-72B-Instruct?

GPT-5 Pro supports 400K 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.

Is GPT-5 Pro or Qwen2.5-72B-Instruct open source?

GPT-5 Pro is listed under Proprietary. 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 vision, GPT-5 Pro or Qwen2.5-72B-Instruct?

GPT-5 Pro has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, GPT-5 Pro or Qwen2.5-72B-Instruct?

GPT-5 Pro has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for function calling, GPT-5 Pro or Qwen2.5-72B-Instruct?

GPT-5 Pro has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GPT-5 Pro and Qwen2.5-72B-Instruct?

GPT-5 Pro is available on the tracked providers still being sourced. 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.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.