Head-to-Head Model Comparison

Command A Plus vs Nemotron 3 Ultra 550B A55B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期:October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command A Plus vs Nemotron 3 Ultra 550B A55B: Benchmarks, Pricing & Speed Comparison

Cohere 的 Command A Plus 与 NVIDIA 的 Nemotron 3 Ultra 550B A55B 是当前备受关注的两款主力 AI 模型。Command A Plus 提供 128K Token 上下文窗口,API 定价为每百万 Token 输入 $3.00 / 输出 $12.00;而 Nemotron 3 Ultra 550B A55B 提供 1M Token 上下文窗口,定价为每百万 Token 输入 $1.00 / 输出 $3.00。以下为两者的基准测试、延迟与成本全面对比。

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Cohere

Command A Plus

开源权重基础大模型

Cohere 推出的 Command A Plus 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型,具备 128K 上下文窗口、68 Token/秒生成速度与 58/100 综合智能指数(每百万 Token $3 输入 / $12 输出)。

Intelligence Index58 / 100
Input Token:

$3.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

60.5%

NVIDIA

Nemotron 3 Ultra 550B A55B

开源权重基础大模型

NVIDIA 推出的 Nemotron 3 Ultra 550B A55B 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型,具备 1M 上下文窗口、65 Token/秒生成速度与 60/100 综合智能指数(每百万 Token $1 输入 / $3 输出)。

Intelligence Index60 / 100
Input Token:

$1.00 / 1M

Output Token:

$3.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

64.5%

核心差异一览

  • Nemotron 3 Ultra 550B A55B is 3.6x cheaper for API usage (input + output tokens combined).
  • Nemotron 3 Ultra 550B A55B has a larger context window (1M vs 128K tokens).
  • Command A Plus is faster at 68 tok/s vs 65 tok/s.
  • Nemotron 3 Ultra 550B A55B leads on SWE-bench coding (64.5% vs 60.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand A PlusNemotron 3 Ultra 550B A55B
Intelligence Index
Composite reasoning & knowledge score (0-100)
5860
Provider
CohereNVIDIA
Input Price
Cost per million input tokens
$3.00 / 1M$1.00 / 1M
Output Price
Cost per million output tokens
$12.00 / 1M$3.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.33 / 1M$0.68 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K66K
Throughput Speed
Tokens generated per second
68 tok/s65 tok/s
SWE-bench Verified
Real-world software engineering
60.5%64.5%
GPQA Diamond
Graduate-level reasoning
74%76%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.25 / 1M

各项指标谁更胜一筹?

At-a-glance decision framework
Coding
Nemotron 3 Ultra 550B A55B
+4.0% SWE-bench lead
Cost
Nemotron 3 Ultra 550B A55B
3.6x cheaper monthly runtime
Speed
Command A Plus
+5% faster generation
Context
Nemotron 3 Ultra 550B A55B
1M tokens window

权威基准测试得分对比

Official evaluation metrics verified by independent third-party research labs.

Command A PlusNemotron 3 Ultra 550B A55B
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

60.5%vs64.5%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

74%vs76%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

86.8%vs87.5%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1410ptsvs1425pts
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

50%vs53%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

82.5%vs86%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

88%vs89%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs65 tok/s

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.

Time to First Token (TTFT)Lower is better
Command A Plus0.56s
Nemotron 3 Ultra 550B A55B0.62s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command A Plus7.91s
Nemotron 3 Ultra 550B A55B8.31s

实时 Token 成本模拟器

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Command A Plus Monthly API Bill
$16.43 / month
$3/1M in · $12/1M out
Nemotron 3 Ultra 550B A55B Monthly API Bill
$4.58 / month
$1/1M in · $3/1M out
Nemotron 3 Ultra 550B A55B saves approximately $11.85 / month (72%) under this workload.3.6x Savings

Command A Plus · Strengths & Key Advantages

  • 提供 128K 上下文窗口与最高 33K 输出容量,核心基准表现优异(SWE-bench Verified 60.5% · GPQA Diamond 74%)
  • 高达 68 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 560 毫秒
  • 开放模型权重 (Confidential (~180B+ 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
  • 在大规模高频调用场景下,建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本

Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages

  • 提供 1M 上下文窗口与最高 66K 输出容量,核心基准表现优异(SWE-bench Verified 64.5% · GPQA Diamond 76%)
  • 高达 65 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 620 毫秒
  • 开放模型权重 (550B (55B 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.

MetricCommand A PlusNemotron 3 Ultra 550B A55B
Cost per Task
Estimated USD to finish one task
$0.85$0.85
Output Tokens / Task
Tokens generated per task
45K48K
Reasoning Tokens / Task
Thinking tokens burned per task
22K24K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A PlusNemotron 3 Ultra 550B A55B
Official API Model ID (SDK)command-a-plusnemotron-3-ultra-550b-a55b
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-052026-06
Knowledge CutoffMarch 2026February 2026
ArchitectureFlagship Multimodal Agentic MoEColossal Sparse MoE (550B Total / 55B Active)
ParametersConfidential (~180B+ MoE)550B (55B active MoE)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($3/1M in) across full 128K windowFlat rate ($1/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensCached input at $0.25/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 112 GB VRAM (4-bit Quantized) · 387 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 341 GB VRAM (4-bit Quantized) · 1183 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Command A Plus vs Nemotron 3 Ultra 550B A55B: 常见问题解答 (FAQ)

Which is more intelligent, Command A Plus or Nemotron 3 Ultra 550B A55B?

Nemotron 3 Ultra 550B A55B is more intelligent. It scores 60 compared with 58 on the composite Intelligence Index.

Which model is faster?

Command A Plus is faster, generating 68 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Nemotron 3 Ultra 550B A55B is cheaper at $0.68 per 1M tokens blended, compared with $2.33 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command A Plus has lower latency, with a time to first token of 0.56s 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 128K tokens.

综合结论

综合来看,Command A Plus(适合:支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、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 Nemotron 3 Ultra 550B A55B(适合:支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、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:Nemotron 3 Ultra 550B A55B 在综合智能与编程基准测试中表现更强,Command A Plus 拥有更快的 Token 生成速度,而 Nemotron 3 Ultra 550B A55B 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,Command A Plus 和 Nemotron 3 Ultra 550B A55B 哪个更强?+

在综合智能指数与编程基准评测中,Nemotron 3 Ultra 550B A55B 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。

Command A Plus 与 Nemotron 3 Ultra 550B A55B 的 API 价格哪个更便宜?+

Command A Plus 的 API 价格为输入 $3.00 / 输出 $12.00(每百万 Token),Nemotron 3 Ultra 550B A55B 为输入 $1.00 / 输出 $3.00。综合混合成本来看,Nemotron 3 Ultra 550B A55B 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?+

Command A Plus 支持 128K Token 上下文窗口,而 Nemotron 3 Ultra 550B A55B 支持 1M Token。在流式生成速度方面,Command A Plus 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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: Cohere 与 NVIDIA 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。