Gemma 3 vs Qwen2-72B
Gemma 3 (2025) and Qwen2-72B (2024) are compact production models from Google DeepMind and Alibaba. Gemma 3 ships a not-yet-sourced context window, while Qwen2-72B ships a 128K-token context window. On pricing, Gemma 3 costs $0.04/1M 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.
Gemma 3 is ~1025% cheaper at $0.04/1M; pay for Qwen2-72B only for provider fit.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-12 | 2024-06-05 |
| Context window | — | 128K |
| Parameters | — | 72.71B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 3 | Qwen2-72B |
|---|---|---|
| Input price | $0.04/1M tokens | $0.45/1M tokens |
| Output price | $0.08/1M tokens | $0.65/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3 | Qwen2-72B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
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, Gemma 3 lists $0.04/1M input and $0.08/1M output tokens, while Qwen2-72B lists $0.45/1M input and $0.65/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 3 lower by about $0.46 per million blended tokens. Availability is 3 providers versus 4, so concentration risk also matters.
Choose Gemma 3 when provider fit and lower input-token cost are central to the workload. Choose Qwen2-72B 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which is cheaper, Gemma 3 or Qwen2-72B?
Gemma 3 is cheaper on tracked token pricing. Gemma 3 costs $0.04/1M input and $0.08/1M output tokens. Qwen2-72B costs $0.45/1M input and $0.65/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemma 3 or Qwen2-72B open source?
Gemma 3 is listed under Open Source. Qwen2-72B 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, Gemma 3 or Qwen2-72B?
Both Gemma 3 and Qwen2-72B 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 Gemma 3 and Qwen2-72B?
Gemma 3 is available on OpenRouter, Google AI Studio, and GCP Vertex AI. Qwen2-72B is available on Fireworks AI, DeepInfra, Together AI, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3 over Qwen2-72B?
Gemma 3 is ~1025% cheaper at $0.04/1M; pay for Qwen2-72B only for provider fit. If your workload also depends on provider fit, start with Gemma 3; if it depends on provider fit, run the same evaluation with Qwen2-72B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.