Llama 3 Taiwan 70B Instruct vs Nemotron 3 Ultra
Llama 3 Taiwan 70B Instruct (2024) and Nemotron 3 Ultra (2026) are frontier reasoning models from AI at Meta and NVIDIA AI. Llama 3 Taiwan 70B Instruct ships a 8k-token context window, while Nemotron 3 Ultra ships a 1m-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.
Nemotron 3 Ultra fits 125x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3 Taiwan 70B Instruct | Nemotron 3 Ultra |
|---|---|---|
| Best for | general production evaluation | reasoning-heavy apps and long-context analysis |
| Decision fit | General | Long context |
| Context window | 8k | 1m |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3 Taiwan 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Nemotron 3 Ultra has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Nemotron 3 Ultra uniquely exposes Reasoning in local model data.
- Local decision data tags Nemotron 3 Ultra for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3 Taiwan 70B Instruct
Unavailable
No complete token price in local provider data
Nemotron 3 Ultra
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3 Taiwan 70B Instruct and Nemotron 3 Ultra; plan for SDK, billing, or endpoint changes.
- Nemotron 3 Ultra adds Reasoning in local capability data.
- No overlapping tracked provider route is sourced for Nemotron 3 Ultra and Llama 3 Taiwan 70B Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-01 | 2026-06-04 |
| Context window | 8k | 1m |
| Parameters | 70B | 550B |
| Architecture | decoder only | moe |
| License | Llama 3 Community | NVIDIA Open Model |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3 Taiwan 70B Instruct | Nemotron 3 Ultra |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3 Taiwan 70B Instruct | Nemotron 3 Ultra |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| 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 reasoning mode: Nemotron 3 Ultra. 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 Taiwan 70B Instruct has no token price sourced yet and Nemotron 3 Ultra has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3 Taiwan 70B Instruct when provider fit and broader provider choice are central to the workload. Choose Nemotron 3 Ultra when reasoning depth and larger context windows 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 Taiwan 70B Instruct or Nemotron 3 Ultra?
Nemotron 3 Ultra supports 1m tokens, while Llama 3 Taiwan 70B Instruct supports 8k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3 Taiwan 70B Instruct or Nemotron 3 Ultra open source?
Llama 3 Taiwan 70B Instruct is listed under Llama 3 Community. Nemotron 3 Ultra 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 reasoning mode, Llama 3 Taiwan 70B Instruct or Nemotron 3 Ultra?
Nemotron 3 Ultra has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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 Taiwan 70B Instruct and Nemotron 3 Ultra?
Llama 3 Taiwan 70B Instruct is available on NVIDIA NIM. Nemotron 3 Ultra is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 Taiwan 70B Instruct over Nemotron 3 Ultra?
Nemotron 3 Ultra fits 125x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls. If your workload also depends on provider fit, start with Llama 3 Taiwan 70B Instruct; if it depends on reasoning depth, run the same evaluation with Nemotron 3 Ultra.
Continue comparing
Last reviewed: 2026-06-09. Data sourced from public model cards and provider documentation.