Head-to-Head Model Comparison

Command A Plus vs Claude Opus 5.5

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 Claude Opus 5.5: Benchmarks, Pricing & Speed Comparison

Cohere 的 Command A Plus 与 Anthropic 的 Claude Opus 5.5 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 Claude Opus 5.5 提供 1M Token 上下文窗口,定价为每百万 Token 输入 $4.00 / 输出 $20.00。以下为两者的基准测试、延迟与成本全面对比。

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Popular Matchups:
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%

Anthropic

Claude Opus 5.5

旗舰前沿大模型

Anthropic 推出的 Claude Opus 5.5 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型,具备 1M 上下文窗口、78 Token/秒生成速度与 94/100 综合智能指数(每百万 Token $4 输入 / $20 输出)。

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

核心差异一览

  • Command A Plus is 1.5x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
  • Claude Opus 5.5 is faster at 78 tok/s vs 68 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 60.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand A PlusClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5894
Provider
CohereAnthropic
Input Price
Cost per million input tokens
$3.00 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$12.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.33 / 1M$3.08 / 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
33K128K
Throughput Speed
Tokens generated per second
68 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
60.5%84.6%
GPQA Diamond
Graduate-level reasoning
74%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.40 / 1M

各项指标谁更胜一筹?

At-a-glance decision framework
Coding
Claude Opus 5.5
+24.1% SWE-bench lead
Cost
Command A Plus
1.5x cheaper monthly runtime
Speed
Claude Opus 5.5
+15% faster generation
Context
Claude Opus 5.5
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 PlusClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

60.5%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

74%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

86.8%vs91.5%
LMSYS Chatbot Arena Elo

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

1410ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

50%vs66.4%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs83.1%
AIME (Advanced Math Competition)

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

82.5%vs95.4%
Long-Context Reasoning

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

88%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs78 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
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command A Plus7.91s
Claude Opus 5.57.81s

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
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Command A Plus saves approximately $8.18 / month (33%) under this workload.1.5x 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

Claude Opus 5.5 · Strengths & Key Advantages

  • 提供 1M 上下文窗口与最高 128K 输出容量,核心基准表现优异(SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
  • 高达 78 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 1400 毫秒
  • 极具竞争力的 API 定价(每百万 Token $4 输入 / $20 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
  • 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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricCommand A PlusClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.85$3.85
Output Tokens / Task
Tokens generated per task
45K68K
Reasoning Tokens / Task
Thinking tokens burned per task
22K42K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A PlusClaude Opus 5.5
Official API Model ID (SDK)command-a-plusclaude-opus-5-5-20260901
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-052026-09
Knowledge CutoffMarch 2026June 2026
ArchitectureFlagship Multimodal Agentic MoEExtended Autonomous Hybrid Reasoning Transformer
ParametersConfidential (~180B+ MoE)Confidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($3/1M in) across full 128K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.75/1M tokens$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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 toolsNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
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)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Command A Plus vs Claude Opus 5.5: 常见问题解答 (FAQ)

Which is more intelligent, Command A Plus or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 58 on the composite Intelligence Index.

Which model is faster?

Claude Opus 5.5 is faster, generating 78 tokens per second compared with 68 tokens per second.

Which model is cheaper to run?

Command A Plus is cheaper at $2.33 per 1M tokens blended, compared with $3.08 (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 1.40s.

Which model has a larger context window?

Claude Opus 5.5 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 Claude Opus 5.5(适合:面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、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:Claude Opus 5.5 在综合智能与编程基准测试中表现更强,Claude Opus 5.5 拥有更快的 Token 生成速度,而 Command A Plus 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 Claude Opus 5.5 哪个更强?+

在综合智能指数与编程基准评测中,Claude Opus 5.5 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 Claude Opus 5.5 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),Claude Opus 5.5 为输入 $4.00 / 输出 $20.00。综合混合成本来看,Command A Plus 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 Claude Opus 5.5 支持 1M Token。在流式生成速度方面,Claude Opus 5.5 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 Anthropic 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。