GPT-4 Turbo vs Nemotron-Nano-12B-v2-VL
GPT-4 Turbo (2024) and Nemotron-Nano-12B-v2-VL (2025) are compact production models from OpenAI and NVIDIA AI. GPT-4 Turbo ships a 128k-token context window, while Nemotron-Nano-12B-v2-VL ships a not-yet-sourced context window. On pricing, Nemotron-Nano-12B-v2-VL costs $0.20/1M input tokens versus $5/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Nemotron-Nano-12B-v2-VL is ~2400% cheaper at $0.20/1M; pay for GPT-4 Turbo only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GPT-4 Turbo | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and provider-routed production | multimodal apps and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Vision and JSON / Tool use |
| Context window | 128k | — |
| Cheapest output | $15/1M tokens | $0.60/1M tokens |
| Provider routes | 6 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-4 Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-4 Turbo has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-4 Turbo uniquely exposes Function calling, Tool use, and Code execution in local model data.
- Local decision data tags GPT-4 Turbo for Coding, RAG, and Agents.
- Nemotron-Nano-12B-v2-VL has the lower cheapest tracked output price at $0.60/1M tokens.
- Local decision data tags Nemotron-Nano-12B-v2-VL for Vision 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-4 Turbo
$7,750
Cheapest tracked route/tier: Replicate API
Nemotron-Nano-12B-v2-VL
$310
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $7,440. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- Nemotron-Nano-12B-v2-VL is $14.40/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Function calling, Tool use, and Code execution before moving production traffic.
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- GPT-4 Turbo is $14.40/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-4 Turbo adds Function calling, Tool use, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-09 | 2025-10-28 |
| Context window | 128k | — |
| Parameters | 1.76T (8x222B MoE)* | 12B |
| Architecture | mixture of experts | decoder only |
| License | Proprietary | Llama 3 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | GPT-4 Turbo | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Input price | $5/1M tokens | $0.20/1M tokens |
| Output price | $15/1M tokens | $0.60/1M tokens |
| Providers |
Capabilities
| Capability | GPT-4 Turbo | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | 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 function calling: GPT-4 Turbo, tool use: GPT-4 Turbo, and code execution: GPT-4 Turbo. Both models share vision, multimodal input, and structured outputs, 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.
For cost, GPT-4 Turbo lists $5/1M input and $15/1M output tokens on the cheapest tracked provider, while Nemotron-Nano-12B-v2-VL lists $0.20/1M input and $0.60/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Nemotron-Nano-12B-v2-VL lower by about $7.68 per million blended tokens. Availability is 6 providers versus 3, so concentration risk also matters.
Choose GPT-4 Turbo when coding workflow support and broader provider choice are central to the workload. Choose Nemotron-Nano-12B-v2-VL when vision-heavy evaluation and lower input-token cost 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 is cheaper, GPT-4 Turbo or Nemotron-Nano-12B-v2-VL?
Nemotron-Nano-12B-v2-VL is cheaper on tracked token pricing. GPT-4 Turbo costs $5/1M input and $15/1M output tokens. Nemotron-Nano-12B-v2-VL costs $0.20/1M input and $0.60/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-4 Turbo or Nemotron-Nano-12B-v2-VL open source?
GPT-4 Turbo is listed under Proprietary. Nemotron-Nano-12B-v2-VL is listed under Llama 3 Community. 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 or Nemotron-Nano-12B-v2-VL?
Both GPT-4 Turbo and Nemotron-Nano-12B-v2-VL expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-4 Turbo or Nemotron-Nano-12B-v2-VL?
Both GPT-4 Turbo and Nemotron-Nano-12B-v2-VL expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for function calling, GPT-4 Turbo or Nemotron-Nano-12B-v2-VL?
GPT-4 Turbo has the clearer documented function calling signal in this comparison. If function calling 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 and Nemotron-Nano-12B-v2-VL?
GPT-4 Turbo is available on OpenAI API, Azure OpenAI, Salesforce Einstein Generative AI, OpenRouter, and Replicate API. Nemotron-Nano-12B-v2-VL is available on NVIDIA NIM, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.