Llama 3.3 Nemotron Super 49B v1 vs TxGemma
Llama 3.3 Nemotron Super 49B v1 (2025) and TxGemma (2024) are compact production models from NVIDIA AI and Google DeepMind. Llama 3.3 Nemotron Super 49B v1 ships a 128k-token context window, while TxGemma ships a not-yet-sourced 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 TxGemma when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.3 Nemotron Super 49B v1 | TxGemma |
|---|---|---|
| Best for | general production evaluation | tool-calling agents |
| Decision fit | Long context | Agents, Classification, and JSON / Tool use |
| Context window | 128k | — |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.3 Nemotron Super 49B v1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
- TxGemma uniquely exposes Function calling, Tool use, and Structured outputs in local model data.
- Local decision data tags TxGemma for Agents, Classification, and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.3 Nemotron Super 49B v1
Unavailable
No complete token price in local provider data
TxGemma
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 Llama 3.3 Nemotron Super 49B v1 and TxGemma; plan for SDK, billing, or endpoint changes.
- TxGemma adds Function calling, Tool use, and Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for TxGemma and Llama 3.3 Nemotron Super 49B v1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling, Tool use, and Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-01 | 2024-06-01 |
| Context window | 128k | — |
| Parameters | 49B | 2B |
| Architecture | decoder only | decoder only |
| License | NVIDIA Open Model | Proprietary |
| Openness | Open weights | Proprietary |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.3 Nemotron Super 49B v1 | TxGemma |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.3 Nemotron Super 49B v1 | TxGemma |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| 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 differs most on function calling: TxGemma, tool use: TxGemma, and structured outputs: TxGemma. 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: Llama 3.3 Nemotron Super 49B v1 has no token price sourced yet and TxGemma 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 Llama 3.3 Nemotron Super 49B v1 when provider fit are central to the workload. Choose TxGemma 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
Is Llama 3.3 Nemotron Super 49B v1 or TxGemma open source?
Llama 3.3 Nemotron Super 49B v1 is listed under NVIDIA Open Model. TxGemma is listed under Proprietary. 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 function calling, Llama 3.3 Nemotron Super 49B v1 or TxGemma?
TxGemma has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, Llama 3.3 Nemotron Super 49B v1 or TxGemma?
TxGemma has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Llama 3.3 Nemotron Super 49B v1 or TxGemma?
TxGemma 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 Llama 3.3 Nemotron Super 49B v1 and TxGemma?
Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. TxGemma is available on GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.3 Nemotron Super 49B v1 over TxGemma?
Llama 3.3 Nemotron Super 49B v1 is safer overall; choose TxGemma when provider fit matters. If your workload also depends on provider fit, start with Llama 3.3 Nemotron Super 49B v1; if it depends on provider fit, run the same evaluation with TxGemma.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.