Llama 3.1 Nemotron 70B Reward vs Nemotron 4 340B
Llama 3.1 Nemotron 70B Reward (2024) and Nemotron 4 340B (2025) are compact production models from NVIDIA AI. Llama 3.1 Nemotron 70B Reward ships a 4k-token context window, while Nemotron 4 340B 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. It focuses on practical selection signals rather than broad model-family marketing.
Nemotron 4 340B is safer overall; choose Llama 3.1 Nemotron 70B Reward when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Nemotron 70B Reward | Nemotron 4 340B |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | Classification | Classification and JSON / Tool use |
| Context window | 4k | 4k |
| Cheapest output | - | $4.20/1M tokens |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Local decision data tags Llama 3.1 Nemotron 70B Reward for Classification.
- Nemotron 4 340B has broader tracked provider coverage for fallback and procurement flexibility.
- Nemotron 4 340B uniquely exposes Structured outputs in local model data.
- Local decision data tags Nemotron 4 340B 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 3.1 Nemotron 70B Reward
Unavailable
No complete token price in local provider data
Nemotron 4 340B
$4,410
Cheapest tracked route/tier: DeepInfra
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.
- Nemotron 4 340B 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 | 2024-10-01 | 2025-02-27 |
| Context window | 4k | 4k |
| Parameters | 70B | 340B |
| Architecture | Decoder Only | Decoder Only |
| License | NVIDIA Open Model | NVIDIA Open Model |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use: permitted | Commercial use: permitted |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 Nemotron 70B Reward | Nemotron 4 340B |
|---|---|---|
| Input price | - | $4.20/1M tokens |
| Output price | - | $4.20/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 Nemotron 70B Reward | Nemotron 4 340B |
|---|---|---|
| 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 scores are currently available for this pair.
Deep dive
The capability footprint differs most on structured outputs: Nemotron 4 340B. 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 3.1 Nemotron 70B Reward has no token price sourced yet and Nemotron 4 340B has $4.20/1M input tokens. Provider availability is 1 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 Nemotron 70B Reward when provider fit are central to the workload. Choose Nemotron 4 340B 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. 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, Llama 3.1 Nemotron 70B Reward or Nemotron 4 340B?
Llama 3.1 Nemotron 70B Reward supports 4k tokens, while Nemotron 4 340B 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 3.1 Nemotron 70B Reward or Nemotron 4 340B open source?
Llama 3.1 Nemotron 70B Reward is listed under NVIDIA Open Model. Nemotron 4 340B 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.
Which is better for structured outputs, Llama 3.1 Nemotron 70B Reward or Nemotron 4 340B?
Nemotron 4 340B 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 3.1 Nemotron 70B Reward and Nemotron 4 340B?
Llama 3.1 Nemotron 70B Reward is available on NVIDIA NIM. Nemotron 4 340B is available on NVIDIA NIM and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 Nemotron 70B Reward over Nemotron 4 340B?
Nemotron 4 340B is safer overall; choose Llama 3.1 Nemotron 70B Reward when provider fit matters. If your workload also depends on provider fit, start with Llama 3.1 Nemotron 70B Reward; if it depends on provider fit, run the same evaluation with Nemotron 4 340B.
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Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.