Llama 3 70B Instruct vs Nemotron 4 340B
Llama 3 70B Instruct (2024) and Nemotron 4 340B (2025) are compact production models from AI at Meta and NVIDIA AI. Llama 3 70B Instruct ships a 8K-token context window, while Nemotron 4 340B ships a 4K-token context window. On pricing, Llama 3 70B Instruct costs $0.4/1M input tokens versus $4.2/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3 70B Instruct is ~950% cheaper at $0.4/1M; pay for Nemotron 4 340B only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3 70B Instruct | Nemotron 4 340B |
|---|---|---|
| Decision fit | Coding, Classification, and JSON / Tool use | Classification and JSON / Tool use |
| Context window | 8K | 4K |
| Cheapest output | $0.4/1M tokens | $4.2/1M tokens |
| Provider routes | 17 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3 70B Instruct has the lower cheapest tracked output price at $0.4/1M tokens.
- Llama 3 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3 70B Instruct for Coding, 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 prices on this page.
Llama 3 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Nemotron 4 340B
$4,410
Cheapest tracked route: DeepInfra
Estimated monthly gap: $3,990. Batch, cache, 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 $3.8/1M 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 3 70B Instruct is $3.8/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-18 | 2025-02-27 |
| Context window | 8K | 4K |
| Parameters | 70B | 340B |
| Architecture | decoder only | decoder only |
| License | Open Source | Unknown |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3 70B Instruct | Nemotron 4 340B |
|---|---|---|
| Input price | $0.4/1M tokens | $4.2/1M tokens |
| Output price | $0.4/1M tokens | $4.2/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 70B Instruct | 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 |
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 3 70B Instruct lists $0.4/1M input and $0.4/1M output tokens, while Nemotron 4 340B lists $4.2/1M input and $4.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 70B Instruct lower by about $3.8 per million blended tokens. Availability is 17 providers versus 2, so concentration risk also matters.
Choose Llama 3 70B Instruct when long-context analysis, larger context windows, and lower input-token cost 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 3 70B Instruct or Nemotron 4 340B?
Llama 3 70B Instruct supports 8K 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 3 70B Instruct or Nemotron 4 340B?
Llama 3 70B Instruct is cheaper on tracked token pricing. Llama 3 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Nemotron 4 340B costs $4.2/1M input and $4.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3 70B Instruct or Nemotron 4 340B open source?
Llama 3 70B Instruct is listed under Open Source. Nemotron 4 340B is listed under Unknown. 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 70B Instruct or Nemotron 4 340B?
Both Llama 3 70B Instruct 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 3 70B Instruct and Nemotron 4 340B?
Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. 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 70B Instruct over Nemotron 4 340B?
Llama 3 70B Instruct is ~950% cheaper at $0.4/1M; pay for Nemotron 4 340B only for provider fit. If your workload also depends on long-context analysis, start with Llama 3 70B Instruct; 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.