LLM Reference

Nemotron 4 340B vs Tencent Hunyuan Turbo S

Nemotron 4 340B (2025) and Tencent Hunyuan Turbo S (2026) are compact production models from NVIDIA AI and Tencent AI Lab. Nemotron 4 340B 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 Nemotron 4 340B for tighter calls.

Decision scorecard

Local evidence first
SignalNemotron 4 340BTencent Hunyuan Turbo S
Best forprovider-routed productiongeneral production evaluation
Decision fitClassification and JSON / Tool useLong context
Context window4k200k
Cheapest output$4.20/1M tokens-
Provider routes2 tracked0 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Nemotron 4 340B when...
  • Nemotron 4 340B has broader tracked provider coverage for fallback and procurement flexibility.
  • Nemotron 4 340B uniquely exposes Structured outputs in local model data.
  • Local decision data tags Nemotron 4 340B for Classification and JSON / Tool use.
Choose Tencent Hunyuan Turbo S when...
  • 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.

Nemotron 4 340B

$4,410

Cheapest tracked route/tier: DeepInfra

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

Nemotron 4 340B -> Tencent Hunyuan Turbo S
  • No overlapping tracked provider route is sourced for Nemotron 4 340B and Tencent Hunyuan Turbo S; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Structured outputs before moving production traffic.
Tencent Hunyuan Turbo S -> Nemotron 4 340B
  • No overlapping tracked provider route is sourced for Tencent Hunyuan Turbo S and Nemotron 4 340B; plan for SDK, billing, or endpoint changes.
  • Nemotron 4 340B adds Structured outputs in local capability data.

Specs

Specification
Released2025-02-272026-01-10
Context window4k200k
Parameters340B
Architecturedecoder only-
LicenseNVIDIA Open ModelTencent Hunyuan Community License
OpennessOpen weightsOpen weights
Commercial useCommercial use allowedCommercial use with conditions
Knowledge cutoff--

Pricing and availability

Pricing attributeNemotron 4 340BTencent Hunyuan Turbo S
Input price$4.20/1M tokens-
Output price$4.20/1M tokens-
Providers-

Capabilities

CapabilityNemotron 4 340BTencent Hunyuan Turbo S
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsYesNo
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on structured outputs: Nemotron 4 340B. 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: Nemotron 4 340B has $4.20/1M input tokens and Tencent Hunyuan Turbo S has no token price sourced yet. Provider availability is 2 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Nemotron 4 340B 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. 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, Nemotron 4 340B or Tencent Hunyuan Turbo S?

Tencent Hunyuan Turbo S supports 200k 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.

Is Nemotron 4 340B or Tencent Hunyuan Turbo S open source?

Nemotron 4 340B is listed under NVIDIA Open Model. 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.

Which is better for structured outputs, Nemotron 4 340B or Tencent Hunyuan Turbo S?

Nemotron 4 340B has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Nemotron 4 340B and Tencent Hunyuan Turbo S?

Nemotron 4 340B is available on NVIDIA NIM and DeepInfra. 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 Nemotron 4 340B over Tencent Hunyuan Turbo S?

Tencent Hunyuan Turbo S fits 50x more tokens; pick it for long-context work and Nemotron 4 340B for tighter calls. If your workload also depends on provider fit, start with Nemotron 4 340B; 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.