LLM Reference

Llama 2 7B Chat vs Nemotron 4 340B

Llama 2 7B Chat (2023) and Nemotron 4 340B (2025) are compact production models from AI at Meta and NVIDIA AI. Llama 2 7B Chat ships a 4k-token context window, while Nemotron 4 340B ships a 4k-token context window. On pricing, Llama 2 7B Chat costs $0.05/1M 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 7B Chat is ~8300% cheaper at $0.05/1M; pay for Nemotron 4 340B only for provider fit.

Decision scorecard

Local evidence first
SignalLlama 2 7B ChatNemotron 4 340B
Best forprovider-routed productionprovider-routed production
Decision fitClassification and JSON / Tool useClassification and JSON / Tool use
Context window4k4k
Cheapest output$0.25/1M tokens$4.20/1M tokens
Provider routes10 tracked2 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Llama 2 7B Chat when...
  • Llama 2 7B Chat has the lower cheapest tracked output price at $0.25/1M tokens.
  • Llama 2 7B Chat has broader tracked provider coverage for fallback and procurement flexibility.
  • Local decision data tags Llama 2 7B Chat for Classification and JSON / Tool use.
Choose Nemotron 4 340B when...
  • 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.

Lower estimate Llama 2 7B Chat

Llama 2 7B Chat

$103

Cheapest tracked route/tier: Replicate API

Nemotron 4 340B

$4,410

Cheapest tracked route/tier: DeepInfra

Estimated monthly gap: $4,308. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

Llama 2 7B Chat -> Nemotron 4 340B
  • Provider overlap exists on DeepInfra; start route-level A/B tests there.
  • Nemotron 4 340B is $3.95/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Nemotron 4 340B -> Llama 2 7B Chat
  • Provider overlap exists on DeepInfra; start route-level A/B tests there.
  • Llama 2 7B Chat is $3.95/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.

Specs

Specification
Released2023-07-182025-02-27
Context window4k4k
Parameters7B340B
Architecturedecoder onlydecoder only
LicenseLlama 2 CommunityNVIDIA Open Model
OpennessOpen weightsOpen weights
Commercial useCommercial use with conditionsCommercial use allowed
Knowledge cutoff2022-09-

Pricing and availability

Pricing attributeLlama 2 7B ChatNemotron 4 340B
Input price$0.05/1M tokens$4.20/1M tokens
Output price$0.25/1M tokens$4.20/1M tokens
Providers

Capabilities

CapabilityLlama 2 7B ChatNemotron 4 340B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsYesYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

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 7B Chat lists $0.05/1M input and $0.25/1M 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 7B Chat lower by about $4.09 per million blended tokens. Availability is 10 providers versus 2, so concentration risk also matters.

Choose Llama 2 7B 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 7B Chat or Nemotron 4 340B?

Llama 2 7B 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 7B Chat or Nemotron 4 340B?

Llama 2 7B Chat is cheaper on tracked token pricing. Llama 2 7B Chat costs $0.05/1M input and $0.25/1M 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 7B Chat or Nemotron 4 340B open source?

Llama 2 7B 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 7B Chat or Nemotron 4 340B?

Both Llama 2 7B 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 7B Chat and Nemotron 4 340B?

Llama 2 7B Chat is available on Alibaba Cloud PAI-EAS, Baseten API, Fireworks AI, Microsoft Foundry, and GCP Vertex AI. 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 7B Chat over Nemotron 4 340B?

Llama 2 7B Chat is ~8300% cheaper at $0.05/1M; pay for Nemotron 4 340B only for provider fit. If your workload also depends on provider fit, start with Llama 2 7B 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.