Gemma 2 27B vs Llama 3.1 405B
Gemma 2 27B (2024) and Llama 3.1 405B (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 2 27B ships a 8K-token context window, while Llama 3.1 405B ships a 128K-token context window. On Google-Proof Q&A, Gemma 2 27B leads by 5.2 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.
Llama 3.1 405B fits 16x more tokens; pick it for long-context work and Gemma 2 27B for tighter calls.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-06-27 | 2024-07-23 |
| Context window | 8K | 128K |
| Parameters | 27B | 405B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 2 27B | Llama 3.1 405B |
|---|---|---|
| Input price | $0.08/1M tokens | - |
| Output price | $0.24/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Gemma 2 27B | Llama 3.1 405B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
| Benchmark | Gemma 2 27B | Llama 3.1 405B |
|---|---|---|
| Google-Proof Q&A | 56.7 | 51.5 |
| HumanEval | 80.4 | 89.0 |
| Massive Multitask Language Understanding | 81.6 | 88.6 |
| HellaSwag | 92.6 | 95.8 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Gemma 2 27B at 56.7 and Llama 3.1 405B at 51.5, with Gemma 2 27B ahead by 5.2 points; HumanEval has Gemma 2 27B at 80.4 and Llama 3.1 405B at 89, with Llama 3.1 405B ahead by 8.6 points; Massive Multitask Language Understanding has Gemma 2 27B at 81.6 and Llama 3.1 405B at 88.6, with Llama 3.1 405B ahead by 7 points. The largest visible gap is 8.6 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 differs most on structured outputs: Gemma 2 27B. Both models share the core language-model surface, 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: Gemma 2 27B has $0.08/1M input tokens and Llama 3.1 405B has no token price sourced yet. Provider availability is 2 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 2 27B when provider fit and broader provider choice are central to the workload. Choose Llama 3.1 405B when long-context analysis and larger context windows 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, Gemma 2 27B or Llama 3.1 405B?
Llama 3.1 405B supports 128K tokens, while Gemma 2 27B supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 2 27B or Llama 3.1 405B open source?
Gemma 2 27B is listed under Open Source. Llama 3.1 405B 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 structured outputs, Gemma 2 27B or Llama 3.1 405B?
Gemma 2 27B has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Gemma 2 27B and Llama 3.1 405B?
Gemma 2 27B is available on GCP Vertex AI and Bitdeer AI. Llama 3.1 405B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 2 27B over Llama 3.1 405B?
Llama 3.1 405B fits 16x more tokens; pick it for long-context work and Gemma 2 27B for tighter calls. If your workload also depends on provider fit, start with Gemma 2 27B; if it depends on long-context analysis, run the same evaluation with Llama 3.1 405B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.