Nemotron Nano 12B v2 VL vs GPT-OSS 20B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
NVIDIA 的 Nemotron Nano 12B v2 VL 与 OpenAI 的 GPT-OSS 20B 是当前备受关注的两款主力 AI 模型。Nemotron Nano 12B v2 VL 提供 128K Token 上下文窗口，API 定价为每百万 Token 输入 $0.12 / 输出 $0.30；而 GPT-OSS 20B 提供 131.1K Token 上下文窗口，定价为每百万 Token 输入 $0.10 / 输出 $0.50。以下为两者的基准测试、延迟与成本全面对比。
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Nemotron Nano 12B v2 VL
原生多模态视觉模型NVIDIA 推出的 Nemotron Nano 12B v2 VL 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 128K 上下文窗口、110 Token/秒生成速度与 43/100 综合智能指数（每百万 Token $0.12 输入 / $0.3 输出）。
$0.12 / 1M
$0.30 / 1M
128K tokens
31%
GPT-OSS 20B
开源权重推理大模型OpenAI 推出的 GPT-OSS 20B 是一款通过大规模强化学习训练、支持私有化部署的开源权重推理大模型，具备 131K 上下文窗口、160 Token/秒生成速度与 46/100 综合智能指数（每百万 Token $0.1 输入 / $0.5 输出）。
$0.10 / 1M
$0.50 / 1M
131.1K tokens
32.5%
核心差异一览
- Nemotron Nano 12B v2 VL is 1.2x cheaper for API usage (input + output tokens combined).
- GPT-OSS 20B has a larger context window (131K vs 128K tokens).
- GPT-OSS 20B is faster at 160 tok/s vs 110 tok/s.
- GPT-OSS 20B leads on SWE-bench coding (32.5% vs 31.0%).
| Feature | Nemotron Nano 12B v2 VL | GPT-OSS 20B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 46 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.12 / 1M | $0.10 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $0.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 33K |
Throughput Speed Tokens generated per second | 110 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 31% | 32.5% |
GPQA Diamond Graduate-level reasoning | 47% | 55% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.01 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Nano 12B v2 VL · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 31% · GPQA Diamond 47%）
- 高达 110 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 350 毫秒
- 开放模型权重 (12B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
GPT-OSS 20B · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 33K 输出容量，核心基准表现优异（SWE-bench Verified 32.5% · GPQA Diamond 55%）
- 高达 160 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 260 毫秒
- 开放模型权重 (21B (3.6B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron Nano 12B v2 VL | GPT-OSS 20B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.09 |
Output Tokens / Task Tokens generated per task | 24K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 18K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron Nano 12B v2 VL | GPT-OSS 20B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-nano-12b-v2-vl | gpt-oss-20b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-10 | 2025-08 |
| Knowledge Cutoff | August 2025 | June 2024 |
| Architecture | Compact Vision-Language Transformer | Sparse MoE transformer with reasoning |
| Parameters | 12B | 21B (3.6B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.12/1M in) across full 128K window | Flat rate ($0.1/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Automatic prefix caching at $0.01/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 13 GB VRAM (4-bit Quantized) · 45 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Nemotron Nano 12B v2 VL vs GPT-OSS 20B: 常见问题解答 (FAQ)
Which is more intelligent, Nemotron Nano 12B v2 VL or GPT-OSS 20B?
GPT-OSS 20B is more intelligent. It scores 46 compared with 43 on the composite Intelligence Index.
Which model is faster?
GPT-OSS 20B is faster, generating 160 tokens per second compared with 110 tokens per second.
Which model has lower latency?
GPT-OSS 20B has lower latency, with a time to first token of 0.26s compared with 0.35s.
Which model has a larger context window?
GPT-OSS 20B has a larger context window, supporting 131.1K tokens compared with 128K tokens.
综合结论
综合来看，Nemotron Nano 12B v2 VL（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 GPT-OSS 20B（适合：通过大规模强化学习训练、支持私有化部署的开源权重推理任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：GPT-OSS 20B 在综合智能与编程基准测试中表现更强，GPT-OSS 20B 拥有更快的 Token 生成速度，而 Nemotron Nano 12B v2 VL 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Nemotron Nano 12B v2 VL 和 GPT-OSS 20B 哪个更强？+
在综合智能指数与编程基准评测中，GPT-OSS 20B 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Nemotron Nano 12B v2 VL 与 GPT-OSS 20B 的 API 价格哪个更便宜？+
Nemotron Nano 12B v2 VL 的 API 价格为输入 $0.12 / 输出 $0.30（每百万 Token），GPT-OSS 20B 为输入 $0.10 / 输出 $0.50。综合混合成本来看，Nemotron Nano 12B v2 VL 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Nemotron Nano 12B v2 VL 支持 128K Token 上下文窗口，而 GPT-OSS 20B 支持 131.1K Token。在流式生成速度方面，GPT-OSS 20B 的吞吐表现更快。
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: NVIDIA 与 OpenAI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。