Llama 2 70B Chat vs Nemotron 4 340B
Llama 2 70B Chat (2023) and Nemotron 4 340B (2025) are compact production models from AI at Meta and NVIDIA AI. Llama 2 70B Chat ships a 4k-token context window, while Nemotron 4 340B ships a 4k-token context window. On pricing, Llama 2 70B Chat costs $0.50/1M input tokens versus $4.20/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 2 70B Chat is ~740% cheaper at $0.50/1M; pay for Nemotron 4 340B only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 2 70B Chat | Nemotron 4 340B |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Classification and JSON / Tool use | Classification and JSON / Tool use |
| Context window | 4k | 4k |
| Cheapest output | $1.50/1M tokens | $4.20/1M tokens |
| Provider routes | 14 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 2 70B Chat has the lower cheapest tracked output price at $1.50/1M tokens.
- Llama 2 70B Chat has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 2 70B Chat for Classification and JSON / Tool use.
- 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 2 70B Chat
$775
Cheapest tracked route/tier: Databricks Foundation Model Serving
Nemotron 4 340B
$4,410
Cheapest tracked route/tier: DeepInfra
Estimated monthly gap: $3,635. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on NVIDIA NIM and DeepInfra; start route-level A/B tests there.
- Nemotron 4 340B is $2.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on NVIDIA NIM and DeepInfra; start route-level A/B tests there.
- Llama 2 70B Chat is $2.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-07-18 | 2025-02-27 |
| Context window | 4k | 4k |
| Parameters | 70B | 340B |
| Architecture | decoder only | decoder only |
| License | Llama 2 Community | NVIDIA Open Model |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 2 70B Chat | Nemotron 4 340B |
|---|---|---|
| Input price | $0.50/1M tokens | $4.20/1M tokens |
| Output price | $1.50/1M tokens | $4.20/1M tokens |
| Providers |
Capabilities
| Capability | Llama 2 70B Chat | Nemotron 4 340B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | 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 is close: both models cover structured outputs. 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.
For cost, Llama 2 70B Chat lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider, while Nemotron 4 340B lists $4.20/1M input and $4.20/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 2 70B Chat lower by about $3.40 per million blended tokens. Availability is 14 providers versus 2, so concentration risk also matters.
Choose Llama 2 70B Chat when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Nemotron 4 340B 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, Llama 2 70B Chat or Nemotron 4 340B?
Llama 2 70B Chat 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.
Which is cheaper, Llama 2 70B Chat or Nemotron 4 340B?
Llama 2 70B Chat is cheaper on tracked token pricing. Llama 2 70B Chat costs $0.50/1M input and $1.50/1M output tokens. Nemotron 4 340B costs $4.20/1M input and $4.20/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 2 70B Chat or Nemotron 4 340B open source?
Llama 2 70B Chat is listed under Llama 2 Community. 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 2 70B Chat or Nemotron 4 340B?
Both Llama 2 70B Chat and Nemotron 4 340B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama 2 70B Chat and Nemotron 4 340B?
Llama 2 70B Chat is available on Databricks Foundation Model Serving, Microsoft Foundry, GCP Vertex AI, Alibaba Cloud PAI-EAS, and AWS Bedrock. 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 2 70B Chat over Nemotron 4 340B?
Llama 2 70B Chat is ~740% cheaper at $0.50/1M; pay for Nemotron 4 340B only for provider fit. If your workload also depends on provider fit, start with Llama 2 70B Chat; if it depends on provider fit, run the same evaluation with Nemotron 4 340B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.