Nemotron 3 Ultra 550B A55B vs GPT-OSS 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3 Ultra 550B A55B by NVIDIA and GPT-OSS 120B by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GPT-OSS 120B holds a notable pricing advantage for token ingestion. On output generation, GPT-OSS 120B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nemotron 3 Ultra 550B A55B provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, GPT-OSS 120B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
Cambia e Confronta Qualsiasi Modello
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Nemotron 3 Ultra 550B A55B
Modello a Pesi ApertiNemotron 3 Ultra 550B A55B di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $1/1M token in input e $3/1M in output, ideale per coding, ragionamento e automazione aziendale.
$1.00 / 1M
$3.00 / 1M
1M tokens
64.5%
GPT-OSS 120B
Modello a Pesi ApertiGPT-OSS 120B di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 131K token e tariffazione API di $0.15/1M token in input e $0.6/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.15 / 1M
$0.60 / 1M
131.1K tokens
45.5%
Differenze Chiave a Colpo d’Occhio
- GPT-OSS 120B is 5.8x cheaper for API usage (input + output tokens combined).
- Nemotron 3 Ultra 550B A55B has a larger context window (1M vs 131K tokens).
- GPT-OSS 120B is faster at 95 tok/s vs 65 tok/s.
- Nemotron 3 Ultra 550B A55B leads on SWE-bench coding (64.5% vs 45.5%).
| Feature | Nemotron 3 Ultra 550B A55B | GPT-OSS 120B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 56 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $1.00 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $3.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.68 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 66K | 33K |
Throughput Speed Tokens generated per second | 65 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | 64.5% | 45.5% |
GPQA Diamond Graduate-level reasoning | 76% | 68.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.01 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages
- Architettura Colossal Sparse MoE (550B Total / 55B Active) avanzata di NVIDIA con supporto fino a 1M token di contesto
- Ottimo rapporto qualità-prezzo ($1/1M input, $3/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
GPT-OSS 120B · Strengths & Key Advantages
- Architettura Sparse MoE transformer with reasoning avanzata di OpenAI con supporto fino a 131K token di contesto
- Ottimo rapporto qualità-prezzo ($0.15/1M input, $0.6/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron 3 Ultra 550B A55B | GPT-OSS 120B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.85 | $0.14 |
Output Tokens / Task Tokens generated per task | 48K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 24K | 22K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3 Ultra 550B A55B | GPT-OSS 120B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-ultra-550b-a55b | gpt-oss-120b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-06 | 2025-08 |
| Knowledge Cutoff | February 2026 | June 2024 |
| Architecture | Colossal Sparse MoE (550B Total / 55B Active) | Sparse MoE transformer with reasoning |
| Parameters | 550B (55B active MoE) | 117B (5.1B 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 ($1/1M in) across full 1000K window | Flat rate ($0.15/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Automatic prefix caching at $0.015/1M (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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 341 GB VRAM (4-bit Quantized) · 1183 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 73 GB VRAM (4-bit Quantized) · 252 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 | text |
| Output Modalities | text | text |
Nemotron 3 Ultra 550B A55B vs GPT-OSS 120B: Risposte Rapide (FAQ)
Which is more intelligent, Nemotron 3 Ultra 550B A55B or GPT-OSS 120B?
Nemotron 3 Ultra 550B A55B is more intelligent. It scores 60 compared with 56 on the composite Intelligence Index.
Which model is faster?
GPT-OSS 120B is faster, generating 95 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
GPT-OSS 120B is cheaper at $0.10 per 1M tokens blended, compared with $0.68 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-OSS 120B has lower latency, with a time to first token of 0.48s compared with 0.62s.
Which model has a larger context window?
Nemotron 3 Ultra 550B A55B has a larger context window, supporting 1M tokens compared with 131.1K tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose Nemotron 3 Ultra 550B A55B if your priority is National AI initiatives, sovereign defense intelligence, and enterprise-wide autonomous agent swarms. Its performance in demanding tasks justifies its operational token cost. Choose GPT-OSS 120B if your workflow prioritizes Self-hosted agents that need near-frontier quality on private data. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Nemotron 3 Ultra 550B A55B compare to GPT-OSS 120B in API token pricing and cost per token?+
Nemotron 3 Ultra 550B A55B costs $1.00 per 1M input tokens and $3.00 per 1M output tokens, whereas GPT-OSS 120B costs $0.15 input and $0.60 output. Overall, GPT-OSS 120B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Nemotron 3 Ultra 550B A55B or GPT-OSS 120B?+
Nemotron 3 Ultra 550B A55B supports a context window of 1,000K tokens (~750k words), while GPT-OSS 120B supports 131K tokens (~98k words). Therefore, Nemotron 3 Ultra 550B A55B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nemotron 3 Ultra 550B A55B or GPT-OSS 120B?+
Nemotron 3 Ultra 550B A55B scores 64.5% on SWE-bench Verified while GPT-OSS 120B scores 45.5%. For autonomous software engineering and multi-file debugging, Nemotron 3 Ultra 550B A55B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron 3 Ultra 550B A55B and GPT-OSS 120B?+
Nemotron 3 Ultra 550B A55B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-OSS 120B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron 3 Ultra 550B A55B and GPT-OSS 120B?+
Nemotron 3 Ultra 550B A55B generates approximately 65 tokens per second, while GPT-OSS 120B delivers 95 tokens per second. In latency-sensitive production environments, GPT-OSS 120B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3 Ultra 550B A55B and GPT-OSS 120B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-OSS 120B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron 3 Ultra 550B A55B. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Command A Plus vs Nemotron 3 Ultra 550B A55B: Benchmarks, Pricing & Speed ComparisonConfronta
- GPT-OSS 120B vs DeepSeek V4 Pro: Benchmarks, Pricing & Speed ComparisonConfronta
- GPT-OSS 120B vs Llama 5: Benchmarks, Pricing & Speed ComparisonConfronta
- Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B: Benchmarks, Pricing & Speed ComparisonConfronta
- Nemotron 3 Super 120B A12B vs GPT-OSS 120B: Benchmarks, Pricing & Speed ComparisonConfronta
- Nemotron 3 Ultra 550B A55B vs Gemini 4 Argon: Benchmarks, Pricing & Speed ComparisonConfronta
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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs