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Gemini 2.5 Pro vs Qwen2.5-72B

Gemini 2.5 Pro (2025) and Qwen2.5-72B (2025) are compact production models from Google DeepMind and Alibaba. Gemini 2.5 Pro ships a 1M-token context window, while Qwen2.5-72B ships a 128k-token context window. On MMLU PRO, Gemini 2.5 Pro leads by 14.2 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

Gemini 2.5 Pro fits 8x more tokens; pick it for long-context work and Qwen2.5-72B for tighter calls.

Specs

Released2025-06-172025-10-10
Context window1M128k
Parameters72B
Architecturedecoder only-
LicenseProprietaryOpen Source
Knowledge cutoff2025-012024-09

Pricing and availability

Gemini 2.5 ProQwen2.5-72B
Input price$1.25/1M tokens-
Output price$10/1M tokens-
Providers-

Capabilities

Gemini 2.5 ProQwen2.5-72B
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

BenchmarkGemini 2.5 ProQwen2.5-72B
MMLU PRO86.272.0

Deep dive

On shared benchmark coverage, MMLU PRO has Gemini 2.5 Pro at 86.2 and Qwen2.5-72B at 72, with Gemini 2.5 Pro ahead by 14.2 points. The largest visible gap is 14.2 points on MMLU PRO, 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 differs most on vision: Gemini 2.5 Pro, multimodal input: Gemini 2.5 Pro, structured outputs: Gemini 2.5 Pro, and code execution: Gemini 2.5 Pro. Both models share function calling and tool use, 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: Gemini 2.5 Pro has $1.25/1M input tokens and Qwen2.5-72B has no token price sourced yet. Provider availability is 3 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Gemini 2.5 Pro when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Qwen2.5-72B when provider fit 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, Gemini 2.5 Pro or Qwen2.5-72B?

Gemini 2.5 Pro supports 1M tokens, while Qwen2.5-72B supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Gemini 2.5 Pro or Qwen2.5-72B open source?

Gemini 2.5 Pro is listed under Proprietary. Qwen2.5-72B is listed under Open Source. 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, Gemini 2.5 Pro or Qwen2.5-72B?

Gemini 2.5 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.

Which is better for multimodal input, Gemini 2.5 Pro or Qwen2.5-72B?

Gemini 2.5 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, Gemini 2.5 Pro or Qwen2.5-72B?

Both Gemini 2.5 Pro and Qwen2.5-72B expose function calling. 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 Gemini 2.5 Pro and Qwen2.5-72B?

Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, and OpenRouter. Qwen2.5-72B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.