GPT-4 Turbo Preview vs Llama 3.3 Nemotron Super 49B v1
GPT-4 Turbo Preview (2023) and Llama 3.3 Nemotron Super 49B v1 (2025) are compact production models from OpenAI and NVIDIA AI. GPT-4 Turbo Preview ships a 128K-token context window, while Llama 3.3 Nemotron Super 49B v1 ships a 128K-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.
Llama 3.3 Nemotron Super 49B v1 is safer overall; choose GPT-4 Turbo Preview when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | GPT-4 Turbo Preview | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Long context |
| Context window | 128K | 128K |
| Cheapest output | $30/1M tokens | - |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-4 Turbo Preview has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-4 Turbo Preview uniquely exposes Vision, Structured outputs, and Code execution in local model data.
- Local decision data tags GPT-4 Turbo Preview for Coding, RAG, and Agents.
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GPT-4 Turbo Preview
$15,500
Cheapest tracked route: OpenAI API
Llama 3.3 Nemotron Super 49B v1
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 GPT-4 Turbo Preview and Llama 3.3 Nemotron Super 49B v1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Structured outputs, and Code execution before moving production traffic.
- No overlapping tracked provider route is sourced for Llama 3.3 Nemotron Super 49B v1 and GPT-4 Turbo Preview; plan for SDK, billing, or endpoint changes.
- GPT-4 Turbo Preview adds Vision, Structured outputs, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-11-06 | 2025-06-01 |
| Context window | 128K | 128K |
| Parameters | 1.76T (8x222B MoE)* | 49B |
| Architecture | mixture of experts | decoder only |
| License | Proprietary | 1 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | GPT-4 Turbo Preview | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Input price | $10/1M tokens | - |
| Output price | $30/1M tokens | - |
| Providers |
Capabilities
| Capability | GPT-4 Turbo Preview | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Vision | Yes | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-4 Turbo Preview, structured outputs: GPT-4 Turbo Preview, and code execution: GPT-4 Turbo Preview. 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-4 Turbo Preview has $10/1M input tokens and Llama 3.3 Nemotron Super 49B v1 has no token price sourced yet. Provider availability is 3 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GPT-4 Turbo Preview when coding workflow support and broader provider choice are central to the workload. Choose Llama 3.3 Nemotron Super 49B v1 when provider fit 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, GPT-4 Turbo Preview or Llama 3.3 Nemotron Super 49B v1?
GPT-4 Turbo Preview supports 128K tokens, while Llama 3.3 Nemotron Super 49B v1 supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GPT-4 Turbo Preview or Llama 3.3 Nemotron Super 49B v1 open source?
GPT-4 Turbo Preview is listed under Proprietary. Llama 3.3 Nemotron Super 49B v1 is listed under 1. 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 vision, GPT-4 Turbo Preview or Llama 3.3 Nemotron Super 49B v1?
GPT-4 Turbo Preview has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, GPT-4 Turbo Preview or Llama 3.3 Nemotron Super 49B v1?
GPT-4 Turbo Preview 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.
Which is better for code execution, GPT-4 Turbo Preview or Llama 3.3 Nemotron Super 49B v1?
GPT-4 Turbo Preview has the clearer documented code execution signal in this comparison. If code execution is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GPT-4 Turbo Preview and Llama 3.3 Nemotron Super 49B v1?
GPT-4 Turbo Preview is available on OpenAI API, Azure OpenAI, and OpenRouter. Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-14. Data sourced from public model cards and provider documentation.