Nemotron Nano 12B v2 VL vs GPT-OSS 20B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Nano 12B v2 VL dari NVIDIA dan GPT-OSS 20B dari OpenAI adalah dua model AI unggulan. Nemotron Nano 12B v2 VL menawarkan jendela konteks 128K token dengan harga $0.12/$0.30 per 1 juta token, sedangkan GPT-OSS 20B menyediakan konteks 131.1K token seharga $0.10/$0.50 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Nemotron Nano 12B v2 VL
AI Multimodal AsliNemotron Nano 12B v2 VL dari NVIDIA adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 128K, kecepatan 110 token/detik, indeks kecerdasan 43/100, sert
$0.12 / 1M
$0.30 / 1M
128K tokens
31%
GPT-OSS 20B
Penalaran Bobot TerbukaGPT-OSS 20B dari OpenAI adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 131K, kecepatan 160 token/detik, indeks kecerdasan 46/100, serta t
$0.10 / 1M
$0.50 / 1M
131.1K tokens
32.5%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Nano 12B v2 VL · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 31% · GPQA Diamond 47%)
- Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 350 ms
- Bobot terbuka (12B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GPT-OSS 20B · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 32.5% · GPQA Diamond 55%)
- Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 260 ms
- Bobot terbuka (21B (3.6B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik Nemotron Nano 12B v2 VL (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-OSS 20B (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-OSS 20B unggul dalam penalaran dan pemrograman, GPT-OSS 20B memberikan kecepatan generasi token lebih tinggi, dan Nemotron Nano 12B v2 VL lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Nemotron Nano 12B v2 VL atau GPT-OSS 20B?+
Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-OSS 20B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Nemotron Nano 12B v2 VL vs GPT-OSS 20B?+
Nemotron Nano 12B v2 VL mengenakan biaya $0.12 / $0.30 per 1M token input/output, sedangkan GPT-OSS 20B sebesar $0.10 / $0.50. Secara keseluruhan, Nemotron Nano 12B v2 VL lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Nemotron Nano 12B v2 VL mendukung konteks 128K token dibandingkan 131.1K token pada GPT-OSS 20B, sementara GPT-OSS 20B unggul dalam kecepatan output token per detik.
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.
Sumber resmi: Dokumentasi API resmi NVIDIA & OpenAI serta evaluasi benchmark independen (2026).