Llama 3.3 Nemotron Super 49B v1 vs Llama 2 70B Chat
Llama 3.3 Nemotron Super 49B v1 (2025) and Llama 2 70B Chat (2023) are compact production models from NVIDIA AI and AI at Meta. Llama 3.3 Nemotron Super 49B v1 ships a 128k-token context window, while Llama 2 70B Chat ships a 4k-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 fits 32x more tokens; pick it for long-context work and Llama 2 70B Chat for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.3 Nemotron Super 49B v1 | Llama 2 70B Chat |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | Long context | Classification and JSON / Tool use |
| Context window | 128k | 4k |
| Cheapest output | - | $1.50/1M tokens |
| Provider routes | 1 tracked | 14 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.
- Llama 2 70B Chat has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 2 70B Chat uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 2 70B Chat for 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
Llama 2 70B Chat
$775
Cheapest tracked route/tier: Databricks Foundation Model Serving
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Llama 2 70B Chat adds Structured outputs in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-01 | 2023-07-18 |
| Context window | 128k | 4k |
| Parameters | 49B | 70B |
| Architecture | decoder only | decoder only |
| License | 1 | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.3 Nemotron Super 49B v1 | Llama 2 70B Chat |
|---|---|---|
| Input price | - | $0.50/1M tokens |
| Output price | - | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.3 Nemotron Super 49B v1 | Llama 2 70B Chat |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| 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 structured outputs: Llama 2 70B Chat. 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 Llama 2 70B Chat has $0.50/1M input tokens. Provider availability is 1 tracked routes versus 14. 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 long-context analysis and larger context windows are central to the workload. Choose Llama 2 70B Chat when provider fit and broader provider choice 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, Llama 3.3 Nemotron Super 49B v1 or Llama 2 70B Chat?
Llama 3.3 Nemotron Super 49B v1 supports 128k tokens, while Llama 2 70B Chat supports 4k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.3 Nemotron Super 49B v1 or Llama 2 70B Chat open source?
Llama 3.3 Nemotron Super 49B v1 is listed under 1. Llama 2 70B Chat 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, Llama 3.3 Nemotron Super 49B v1 or Llama 2 70B Chat?
Llama 2 70B Chat 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 Llama 2 70B Chat?
Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Llama 2 70B Chat is available on Databricks Foundation Model Serving, Microsoft Foundry, GCP Vertex AI, Alibaba Cloud PAI-EAS, and AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.3 Nemotron Super 49B v1 over Llama 2 70B Chat?
Llama 3.3 Nemotron Super 49B v1 fits 32x more tokens; pick it for long-context work and Llama 2 70B Chat for tighter calls. If your workload also depends on long-context analysis, start with Llama 3.3 Nemotron Super 49B v1; if it depends on provider fit, run the same evaluation with Llama 2 70B Chat.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.