Marin 70B Base vs Nemotron 4 340B
Marin 70B Base (2025) and Nemotron 4 340B (2025) are compact production models from Marin and NVIDIA AI. Marin 70B Base ships a 131K-token context window, while Nemotron 4 340B ships a 4K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Marin 70B Base fits 33x more tokens; pick it for long-context work and Nemotron 4 340B for tighter calls.
Decision scorecard
Local evidence first| Signal | Marin 70B Base | Nemotron 4 340B |
|---|---|---|
| Decision fit | Long context | Classification and JSON / Tool use |
| Context window | 131K | 4K |
| Cheapest output | - | $4.2/1M tokens |
| Provider routes | 0 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Marin 70B Base has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Marin 70B Base for Long context.
- 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.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Marin 70B Base
Unavailable
No complete token price in local provider data
Nemotron 4 340B
$4,410
Cheapest tracked route: DeepInfra
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Marin 70B Base and Nemotron 4 340B; plan for SDK, billing, or endpoint changes.
- Nemotron 4 340B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Nemotron 4 340B and Marin 70B Base; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-05 | 2025-02-27 |
| Context window | 131K | 4K |
| Parameters | 70B | 340B |
| Architecture | - | decoder only |
| License | Open Source | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Marin 70B Base | Nemotron 4 340B |
|---|---|---|
| Input price | - | $4.2/1M tokens |
| Output price | - | $4.2/1M tokens |
| Providers | - |
Capabilities
| Capability | Marin 70B Base | Nemotron 4 340B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
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: Marin 70B Base has no token price sourced yet and Nemotron 4 340B has $4.2/1M input tokens. Provider availability is 0 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Marin 70B Base when long-context analysis and larger context windows are central to the workload. Choose Nemotron 4 340B when provider fit and broader provider choice 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, Marin 70B Base or Nemotron 4 340B?
Marin 70B Base supports 131K 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 Marin 70B Base or Nemotron 4 340B open source?
Marin 70B Base 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, Marin 70B Base or Nemotron 4 340B?
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 Marin 70B Base and Nemotron 4 340B?
Marin 70B Base is available on the tracked providers still being sourced. 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 Marin 70B Base over Nemotron 4 340B?
Marin 70B Base fits 33x more tokens; pick it for long-context work and Nemotron 4 340B for tighter calls. If your workload also depends on long-context analysis, start with Marin 70B Base; if it depends on provider fit, run the same evaluation with Nemotron 4 340B.
Continue comparing
Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.