Nemotron 3 Ultra 550B A55B vs Qwen3.8 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3 Ultra 550B A55B by NVIDIA and Qwen3.8 Max Preview by Alibaba 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, Qwen3.8 Max Preview holds a notable pricing advantage for token ingestion. On output generation, Qwen3.8 Max Preview is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,000K tokens. In latency-sensitive production environments, Nemotron 3 Ultra 550B A55B 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.
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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%
Qwen3.8 Max Preview
Modello a Pesi ApertiQwen3.8 Max Preview di Alibaba è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $2.0/1M token in input e $6.0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
Differenze Chiave a Colpo d’Occhio
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- Nemotron 3 Ultra 550B A55B is faster at 65 tok/s vs 60 tok/s.
- Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 64.5%).
| Feature | Nemotron 3 Ultra 550B A55B | Qwen3.8 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 93 |
Provider | NVIDIA | Alibaba |
Input Price Cost per million input tokens | $1.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $3.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.68 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 32K |
Throughput Speed Tokens generated per second | 65 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 64.5% | 75.2% |
GPQA Diamond Graduate-level reasoning | 76% | 79.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.00 / 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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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
Qwen3.8 Max Preview · Strengths & Key Advantages
- Architettura Colossus-Scale Open Sparse Mixture-of-Experts avanzata di Alibaba con supporto fino a 1M token di contesto
- Ottimo rapporto qualità-prezzo ($2.0/1M input, $6.0/1M 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 | Qwen3.8 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.85 | $0.00 |
Output Tokens / Task Tokens generated per task | 48K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 24K | 15K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3 Ultra 550B A55B | Qwen3.8 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-ultra-550b-a55b | qwen3-8-max-preview-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-06 | 2026-07 |
| Knowledge Cutoff | February 2026 | April 2026 |
| Architecture | Colossal Sparse MoE (550B Total / 55B Active) | Colossus-Scale Open Sparse Mixture-of-Experts |
| Parameters | 550B (55B active MoE) | 2.4T total (95B 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 | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| 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) · Vision (~258–765 tokens per image tile) |
| 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) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| 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, vision |
| Output Modalities | text | text |
Nemotron 3 Ultra 550B A55B vs Qwen3.8 Max Preview: Risposte Rapide (FAQ)
Which is more intelligent, Nemotron 3 Ultra 550B A55B or Qwen3.8 Max Preview?
Qwen3.8 Max Preview is more intelligent. It scores 93 compared with 60 on the composite Intelligence Index.
Which model is faster?
Nemotron 3 Ultra 550B A55B is faster, generating 65 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $0.68 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3 Ultra 550B A55B has lower latency, with a time to first token of 0.62s compared with 0.68s.
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 Qwen3.8 Max Preview if your workflow prioritizes National research centers, sovereign enterprise clouds, and frontier AI research. 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 Qwen3.8 Max Preview 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 Qwen3.8 Max Preview costs $0.00 input and $0.00 output. Overall, Qwen3.8 Max Preview 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 Qwen3.8 Max Preview?+
Both Nemotron 3 Ultra 550B A55B and Qwen3.8 Max Preview offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Nemotron 3 Ultra 550B A55B or Qwen3.8 Max Preview?+
Nemotron 3 Ultra 550B A55B scores 64.5% on SWE-bench Verified while Qwen3.8 Max Preview scores 75.2%. For autonomous software engineering and multi-file debugging, Qwen3.8 Max Preview holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron 3 Ultra 550B A55B and Qwen3.8 Max Preview?+
Nemotron 3 Ultra 550B A55B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.8 Max Preview 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 Qwen3.8 Max Preview?+
Nemotron 3 Ultra 550B A55B generates approximately 65 tokens per second, while Qwen3.8 Max Preview delivers 60 tokens per second. In latency-sensitive production environments, Nemotron 3 Ultra 550B A55B 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 Qwen3.8 Max Preview to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.8 Max Preview 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.
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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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs