Gemma 2 9B SahabatAI Instruct vs Llama 3.1 405B
Gemma 2 9B SahabatAI Instruct (2025) and Llama 3.1 405B (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 2 9B SahabatAI Instruct ships a 8K-token context window, while Llama 3.1 405B 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.
Llama 3.1 405B fits 16x more tokens; pick it for long-context work and Gemma 2 9B SahabatAI Instruct for tighter calls.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2024-07-23 |
| Context window | 8K | 128K |
| Parameters | 9B | 405B |
| Architecture | decoder only | decoder only |
| License | 1 | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 2 9B SahabatAI Instruct | Llama 3.1 405B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Gemma 2 9B SahabatAI Instruct | Llama 3.1 405B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| 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 the core production surface. 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.
Pricing coverage is uneven: Gemma 2 9B SahabatAI Instruct has no token price sourced yet and Llama 3.1 405B has no token price sourced yet. Provider availability is 1 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 9B SahabatAI Instruct 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. 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, Gemma 2 9B SahabatAI Instruct or Llama 3.1 405B?
Llama 3.1 405B supports 128K tokens, while Gemma 2 9B SahabatAI Instruct 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 9B SahabatAI Instruct or Llama 3.1 405B open source?
Gemma 2 9B SahabatAI Instruct is listed under 1. 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.
Where can I run Gemma 2 9B SahabatAI Instruct and Llama 3.1 405B?
Gemma 2 9B SahabatAI Instruct is available on NVIDIA NIM. 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 9B SahabatAI Instruct over Llama 3.1 405B?
Llama 3.1 405B fits 16x more tokens; pick it for long-context work and Gemma 2 9B SahabatAI Instruct for tighter calls. If your workload also depends on provider fit, start with Gemma 2 9B SahabatAI Instruct; if it depends on long-context analysis, run the same evaluation with Llama 3.1 405B.
Continue comparing
Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.