Llama 3.1 Nemotron Nano 4B v1.1 vs Tencent Hunyuan Turbo S
Llama 3.1 Nemotron Nano 4B v1.1 (2025) and Tencent Hunyuan Turbo S (2026) are compact production models from NVIDIA AI and Tencent AI Lab. Llama 3.1 Nemotron Nano 4B v1.1 ships a 4k-token context window, while Tencent Hunyuan Turbo S ships a 200k-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.
Tencent Hunyuan Turbo S fits 50x more tokens; pick it for long-context work and Llama 3.1 Nemotron Nano 4B v1.1 for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Nemotron Nano 4B v1.1 | Tencent Hunyuan Turbo S |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | Long context |
| Context window | 4k | 200k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 Nemotron Nano 4B v1.1 has broader tracked provider coverage for fallback and procurement flexibility.
- Tencent Hunyuan Turbo S has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Tencent Hunyuan Turbo S for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.1 Nemotron Nano 4B v1.1
Unavailable
No complete token price in local provider data
Tencent Hunyuan Turbo S
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 3.1 Nemotron Nano 4B v1.1 and Tencent Hunyuan Turbo S; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Tencent Hunyuan Turbo S and Llama 3.1 Nemotron Nano 4B v1.1; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-01 | 2026-01-10 |
| Context window | 4k | 200k |
| Parameters | 4B | — |
| Architecture | decoder only | - |
| License | Llama 3 Community | Tencent Hunyuan Community License |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 Nemotron Nano 4B v1.1 | Tencent Hunyuan Turbo S |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 Nemotron Nano 4B v1.1 | Tencent Hunyuan Turbo S |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| 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 is close: both models cover the core production surface. 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.
Pricing coverage is uneven: Llama 3.1 Nemotron Nano 4B v1.1 has no token price sourced yet and Tencent Hunyuan Turbo S 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 3.1 Nemotron Nano 4B v1.1 when provider fit and broader provider choice are central to the workload. Choose Tencent Hunyuan Turbo S when long-context analysis 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.
FAQ
Which has a larger context window, Llama 3.1 Nemotron Nano 4B v1.1 or Tencent Hunyuan Turbo S?
Tencent Hunyuan Turbo S supports 200k tokens, while Llama 3.1 Nemotron Nano 4B v1.1 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 Nano 4B v1.1 or Tencent Hunyuan Turbo S open source?
Llama 3.1 Nemotron Nano 4B v1.1 is listed under Llama 3 Community. Tencent Hunyuan Turbo S is listed under Tencent Hunyuan Community License. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Llama 3.1 Nemotron Nano 4B v1.1 and Tencent Hunyuan Turbo S?
Llama 3.1 Nemotron Nano 4B v1.1 is available on NVIDIA NIM. Tencent Hunyuan Turbo S 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 3.1 Nemotron Nano 4B v1.1 over Tencent Hunyuan Turbo S?
Tencent Hunyuan Turbo S fits 50x more tokens; pick it for long-context work and Llama 3.1 Nemotron Nano 4B v1.1 for tighter calls. If your workload also depends on provider fit, start with Llama 3.1 Nemotron Nano 4B v1.1; if it depends on long-context analysis, run the same evaluation with Tencent Hunyuan Turbo S.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.