LLM Reference

GPT-4 Vision Preview vs Llama 3.2 NV EmbedQA 1B v2

GPT-4 Vision Preview (2023) and Llama 3.2 NV EmbedQA 1B v2 (2025) are compact production models from OpenAI and NVIDIA AI. GPT-4 Vision Preview ships a 128K-token context window, while Llama 3.2 NV EmbedQA 1B v2 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.

GPT-4 Vision Preview fits 32x more tokens; pick it for long-context work and Llama 3.2 NV EmbedQA 1B v2 for tighter calls.

Decision scorecard

Local evidence first
SignalGPT-4 Vision PreviewLlama 3.2 NV EmbedQA 1B v2
Best formultimodal appsgeneral production evaluation
Decision fitCoding, Agents, and Long contextGeneral
Context window128K4K
Cheapest output$40/1M tokens-
Provider routes1 tracked1 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-4 Vision Preview when...
  • GPT-4 Vision Preview has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-4 Vision Preview uniquely exposes Vision, Multimodal, and Code execution in local model data.
  • Local decision data tags GPT-4 Vision Preview for Coding, Agents, and Long context.
Choose Llama 3.2 NV EmbedQA 1B v2 when...
  • Use Llama 3.2 NV EmbedQA 1B v2 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

GPT-4 Vision Preview

$18,000

Cheapest tracked route/tier: Azure OpenAI

Llama 3.2 NV EmbedQA 1B v2

Unavailable

No complete token price in local provider data

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

GPT-4 Vision Preview -> Llama 3.2 NV EmbedQA 1B v2
  • No overlapping tracked provider route is sourced for GPT-4 Vision Preview and Llama 3.2 NV EmbedQA 1B v2; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision, Multimodal, and Code execution before moving production traffic.
Llama 3.2 NV EmbedQA 1B v2 -> GPT-4 Vision Preview
  • No overlapping tracked provider route is sourced for Llama 3.2 NV EmbedQA 1B v2 and GPT-4 Vision Preview; plan for SDK, billing, or endpoint changes.
  • GPT-4 Vision Preview adds Vision, Multimodal, and Code execution in local capability data.

Specs

Specification
Released2023-11-062025-03-01
Context window128K4K
Parameters1.76T (8x222B MoE)*1B
Architecturemixture of expertsencoder
LicenseProprietary1
Knowledge cutoff2023-04-

Pricing and availability

Pricing attributeGPT-4 Vision PreviewLlama 3.2 NV EmbedQA 1B v2
Input price$10/1M tokens-
Output price$40/1M tokens-
Providers

Capabilities

CapabilityGPT-4 Vision PreviewLlama 3.2 NV EmbedQA 1B v2
VisionYesNo
MultimodalYesNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoNo
Code executionYesNo
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 vision: GPT-4 Vision Preview, multimodal input: GPT-4 Vision Preview, and code execution: GPT-4 Vision 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 Vision Preview has $10/1M input tokens and Llama 3.2 NV EmbedQA 1B v2 has no token price sourced yet. Provider availability is 1 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 Vision Preview when coding workflow support and larger context windows are central to the workload. Choose Llama 3.2 NV EmbedQA 1B v2 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 Vision Preview or Llama 3.2 NV EmbedQA 1B v2?

GPT-4 Vision Preview supports 128K tokens, while Llama 3.2 NV EmbedQA 1B v2 supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is GPT-4 Vision Preview or Llama 3.2 NV EmbedQA 1B v2 open source?

GPT-4 Vision Preview is listed under Proprietary. Llama 3.2 NV EmbedQA 1B v2 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 Vision Preview or Llama 3.2 NV EmbedQA 1B v2?

GPT-4 Vision 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 multimodal input, GPT-4 Vision Preview or Llama 3.2 NV EmbedQA 1B v2?

GPT-4 Vision Preview has the clearer documented multimodal input signal in this comparison. If multimodal input 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 Vision Preview or Llama 3.2 NV EmbedQA 1B v2?

GPT-4 Vision 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 Vision Preview and Llama 3.2 NV EmbedQA 1B v2?

GPT-4 Vision Preview is available on Azure OpenAI. Llama 3.2 NV EmbedQA 1B v2 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.