Gemma 2: Google’s open model one year later

Actualizado: 2026-05-03

When Google released Gemma 2 in June 2024, the reception was polite but not enthusiastic. The first Gemma version had been received as a gesture to the open community that didn’t quite compete with Llama 3 or Mistral. Gemma 2 arrived with the promise of closing that gap, and a year later we have enough material to evaluate it without the initial uncertainty.

This post is a balance after a year of real-world use in different scenarios. Not an exhaustive benchmark study, but a practical read of where Gemma 2 has found its place and where it hasn’t.

Key takeaways

• The 9B is the most useful size for self-hosted applications: fits in a consumer GPU and competes favorably with Llama 3 8B on assistant tasks.
• Multilingual coverage is the clearest differentiator vs. Llama: in Spanish specifically, quality is good without fine-tuning.
• The 8K token window is the most visible limitation vs. Llama 3 (128K) or extended Mistral.
• Google’s license is permissive but not Apache 2.0/MIT; worth reading if your application needs maximum legal freedom.
• The community around Gemma 2 is smaller than Llama’s: fewer public fine-tunes, fewer variants.

The variants and their cases

Gemma 2 was released in three sizes, all with decoder-only transformer architecture and interleaved sliding-window attention:

• 2B: edge and very cheap workloads. Fits on mobile devices, runs on laptop CPU without drama, competes with Phi-3 Mini and Llama 3.2 in that range.
• 9B: fills the space Mistral 7B once reigned over: the general-purpose model that fits in a consumer GPU (16-24 GB VRAM with quantization). Probably the most useful size for most self-hosted applications.
• 27B: competes with Llama 3 70B at much lower inference cost. For serious deployments needing quality without 80 GB hardware, a very reasonable option.

Where Gemma 2 shines

Reasoning in non-English languages. Multilingual coverage is remarkable, and in Spanish specifically quality is good from the start, without fine-tuning. In comparisons with Llama 3 of similar size, Gemma 2 gives more consistent results in Spanish.

Conciseness in answers. Gemma 2 tends to answer directly, without the “sure, I’d be happy to explain…” that saturates some competitors’ responses. For integrating the model in applications where the answer is processed programmatically, this tendency is a relief.

Code for a non-specialized model. Gemma 2 27B is surprisingly competent for not being a specialized model.

Where it doesn’t fit

Context is the most visible limit. Gemma 2 ships with an 8K token window. For workloads requiring large document processing, this loses competitiveness against Llama 3 (128K) or extended Mistral.

Licensing is another thing worth understanding. Gemma is published under a Google-specific license that’s permissive but not Apache 2.0 or MIT. It has responsible-use clauses letting Google intervene if the model is used for prohibited purposes.

The ecosystem around Gemma 2 is smaller than Llama’s. Fewer public fine-tunes, fewer optimized variants for specific cases, fewer battle-tested integrations.

The choice between open models

Key questions when choosing between open models:

• Need long context? Llama 3 or Qwen 2.5 win comfortably.
• Need highly optimized performance on a specific GPU? Probably Mistral for inference-tooling maturity.
• Work mainly in Spanish or other European languages and value direct answers? Gemma 2 is a very strong option.
• Need high-quality code? Specific code models like DeepSeek or Qwen Coder.
• Need a very small model for the edge? Gemma 2 2B competes well with Phi-3 and Llama 3.2 1B/3B.

The place it has found

After a year, Gemma 2 has found a reasonable niche without massively stealing share from Llama or Mistral. Its adoption is solid among teams valuing multilingual quality, in deployments prioritizing concise answers, and in cases where Google tooling integration is a plus.

What hasn’t happened is Gemma 2 displacing Llama 3 as default for open models. Llama 3 remains the most frequent pick, and that’s more about ecosystem and documentation than fundamental technical differences.

If I’m starting a project today with no clear restrictions, I’d try Gemma 2 9B first, especially if the project has non-English workloads. In many cases I’d stay there. If the result didn’t convince me, I’d move to Llama 3 for ecosystem convenience. That order, a year ago, would have been reversed. The order change is probably the best summary of what Gemma 2 has achieved.