Gemma 3 12B (free) vs Llama 3.2 NV RerankQA 1B v2
Gemma 3 12B (free) (2026) and Llama 3.2 NV RerankQA 1B v2 (2025) are compact production models from Google DeepMind and NVIDIA AI. Gemma 3 12B (free) ships a 33K-token context window, while Llama 3.2 NV RerankQA 1B v2 ships a 4K-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.
Gemma 3 12B (free) fits 8x more tokens; pick it for long-context work and Llama 3.2 NV RerankQA 1B v2 for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3 12B (free) | Llama 3.2 NV RerankQA 1B v2 |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | General |
| Context window | 33K | 4K |
| Cheapest output | $0.13/1M tokens | - |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 3 12B (free) has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemma 3 12B (free) has broader tracked provider coverage for fallback and procurement flexibility.
- Gemma 3 12B (free) uniquely exposes Structured outputs in local model data.
- Local decision data tags Gemma 3 12B (free) for Classification and JSON / Tool use.
- Use Llama 3.2 NV RerankQA 1B v2 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 3 12B (free)
$64.50
Cheapest tracked route: OpenRouter
Llama 3.2 NV RerankQA 1B v2
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 (free) and Llama 3.2 NV RerankQA 1B v2; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for Llama 3.2 NV RerankQA 1B v2 and Gemma 3 12B (free); plan for SDK, billing, or endpoint changes.
- Gemma 3 12B (free) adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-01-01 | 2025-03-01 |
| Context window | 33K | 4K |
| Parameters | — | 1B |
| Architecture | decoder only | encoder |
| License | Open Source | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 3 12B (free) | Llama 3.2 NV RerankQA 1B v2 |
|---|---|---|
| Input price | $0.04/1M tokens | - |
| Output price | $0.13/1M tokens | - |
| Providers |
Capabilities
| Capability | Gemma 3 12B (free) | Llama 3.2 NV RerankQA 1B v2 |
|---|---|---|
| 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
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Gemma 3 12B (free). 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 3 12B (free) has $0.04/1M input tokens and Llama 3.2 NV RerankQA 1B v2 has no token price sourced yet. Provider availability is 3 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 (free) when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Llama 3.2 NV RerankQA 1B v2 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.
FAQ
Which has a larger context window, Gemma 3 12B (free) or Llama 3.2 NV RerankQA 1B v2?
Gemma 3 12B (free) supports 33K tokens, while Llama 3.2 NV RerankQA 1B v2 supports 4K 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 (free) or Llama 3.2 NV RerankQA 1B v2 open source?
Gemma 3 12B (free) is listed under Open Source. Llama 3.2 NV RerankQA 1B v2 is listed under 1. 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 12B (free) or Llama 3.2 NV RerankQA 1B v2?
Gemma 3 12B (free) 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 3 12B (free) and Llama 3.2 NV RerankQA 1B v2?
Gemma 3 12B (free) is available on AWS Bedrock, OpenRouter, and GCP Vertex AI. Llama 3.2 NV RerankQA 1B v2 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3 12B (free) over Llama 3.2 NV RerankQA 1B v2?
Gemma 3 12B (free) fits 8x more tokens; pick it for long-context work and Llama 3.2 NV RerankQA 1B v2 for tighter calls. If your workload also depends on long-context analysis, start with Gemma 3 12B (free); if it depends on provider fit, run the same evaluation with Llama 3.2 NV RerankQA 1B v2.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.