GPT-2 Large vs Nemotron 4 340B
GPT-2 Large (2019) and Nemotron 4 340B (2025) are compact production models from OpenAI and NVIDIA AI. GPT-2 Large ships a 1k-token context window, while Nemotron 4 340B ships a 4k-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. It focuses on practical selection signals rather than broad model-family marketing.
Nemotron 4 340B fits 4x more tokens; pick it for long-context work and GPT-2 Large for tighter calls.
Decision scorecard
Local evidence first| Signal | GPT-2 Large | Nemotron 4 340B |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | General | Classification and JSON / Tool use |
| Context window | 1k | 4k |
| Cheapest output | - | $4.20/1M tokens |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use GPT-2 Large when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Nemotron 4 340B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- 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 route or tier on this page.
GPT-2 Large
Unavailable
No complete token price in local provider data
Nemotron 4 340B
$4,410
Cheapest tracked route/tier: DeepInfra
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for GPT-2 Large 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 GPT-2 Large; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2019-08-20 | 2025-02-27 |
| Context window | 1k | 4k |
| Parameters | 774M | 340B |
| Architecture | decoder only | decoder only |
| License | MIT(OSI) | NVIDIA Open Model |
| Openness | Open source | Open weights |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2017-12 | - |
Pricing and availability
| Pricing attribute | GPT-2 Large | Nemotron 4 340B |
|---|---|---|
| Input price | - | $4.20/1M tokens |
| Output price | - | $4.20/1M tokens |
| Providers |
Capabilities
| Capability | GPT-2 Large | 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 |
| 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 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: GPT-2 Large has no token price sourced yet and Nemotron 4 340B has $4.20/1M input tokens. Provider availability is 1 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GPT-2 Large when provider fit are central to the workload. Choose Nemotron 4 340B when long-context analysis, larger context windows, 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, GPT-2 Large or Nemotron 4 340B?
Nemotron 4 340B supports 4k tokens, while GPT-2 Large supports 1k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GPT-2 Large or Nemotron 4 340B open source?
GPT-2 Large is listed under MIT. Nemotron 4 340B 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 structured outputs, GPT-2 Large 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 GPT-2 Large and Nemotron 4 340B?
GPT-2 Large is available on Azure OpenAI. Nemotron 4 340B is available on NVIDIA NIM and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick GPT-2 Large over Nemotron 4 340B?
Nemotron 4 340B fits 4x more tokens; pick it for long-context work and GPT-2 Large for tighter calls. If your workload also depends on provider fit, start with GPT-2 Large; if it depends on long-context analysis, run the same evaluation with Nemotron 4 340B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.