Gemma 3 12B Instruct vs Llama 3.3 Nemotron Super 49B v1
Gemma 3 12B Instruct (2025) and Llama 3.3 Nemotron Super 49B v1 (2025) are compact production models from Google DeepMind and NVIDIA AI. Gemma 3 12B Instruct ships a 128k-token context window, while Llama 3.3 Nemotron Super 49B v1 ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.3 Nemotron Super 49B v1 is safer overall; choose Gemma 3 12B Instruct when provider fit matters.
Decision scorecard
Local evidence first| Signal | Gemma 3 12B Instruct | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | Long context | Long context |
| Context window | 128k | 128k |
| Cheapest output | $0.20/1M tokens | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Gemma 3 12B Instruct for Long context.
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemma 3 12B Instruct
$210
Cheapest tracked route/tier: Fireworks AI
Llama 3.3 Nemotron Super 49B v1
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 3 12B Instruct and Llama 3.3 Nemotron Super 49B v1; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Llama 3.3 Nemotron Super 49B v1 and Gemma 3 12B Instruct; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2025-06-01 |
| Context window | 128k | 128k |
| Parameters | 12B | 49B |
| Architecture | Decoder Only | Decoder Only |
| License | Gemma | NVIDIA Open Model |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Gemma 3 12B Instruct | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Input price | $0.20/1M tokens | - |
| Output price | $0.20/1M tokens | - |
| Providers |
Capabilities
| Capability | Gemma 3 12B Instruct | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| 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 |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | 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 3 12B Instruct has $0.20/1M input tokens and Llama 3.3 Nemotron Super 49B v1 has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 3 12B Instruct when provider fit are central to the workload. Choose Llama 3.3 Nemotron Super 49B v1 when provider fit 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 has a larger context window, Gemma 3 12B Instruct or Llama 3.3 Nemotron Super 49B v1?
Gemma 3 12B Instruct supports 128k tokens, while Llama 3.3 Nemotron Super 49B v1 supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 3 12B Instruct or Llama 3.3 Nemotron Super 49B v1 open source?
Gemma 3 12B Instruct is listed under Gemma. Llama 3.3 Nemotron Super 49B v1 is listed under NVIDIA Open Model. 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 3 12B Instruct and Llama 3.3 Nemotron Super 49B v1?
Gemma 3 12B Instruct is available on Fireworks AI. Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3 12B Instruct over Llama 3.3 Nemotron Super 49B v1?
Llama 3.3 Nemotron Super 49B v1 is safer overall; choose Gemma 3 12B Instruct when provider fit matters. If your workload also depends on provider fit, start with Gemma 3 12B Instruct; if it depends on provider fit, run the same evaluation with Llama 3.3 Nemotron Super 49B v1.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.