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Head-to-Head Model Comparison

Command A Plus vs Command A

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 Command A: Benchmarks, Pricing & Speed Comparison

Cohere 的 Command A Plus 与 Cohere 的 Command A 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 Command A 提供 256K Token 上下文窗口，定价为每百万 Token 输入 $2.50 / 输出 $10.00。以下为两者的基准测试、延迟与成本全面对比。

模型直接对比 (VS)

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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%

Cohere

Command A

自主智能体与工具专家

Cohere 推出的 Command A 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型，具备 256K 上下文窗口、70 Token/秒生成速度与 51/100 综合智能指数（每百万 Token $2.5 输入 / $10 输出）。

Intelligence Index51 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

46%

核心差异一览

  • Command A is 1.2x cheaper for API usage (input + output tokens combined).
  • Command A has a larger context window (256K vs 128K tokens).
  • Command A is faster at 70 tok/s vs 68 tok/s.
  • Command A Plus leads on SWE-bench coding (60.5% vs 46.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand A PlusCommand A
Intelligence Index
Composite reasoning & knowledge score (0-100)
5851
Provider
CohereCohere
Input Price
Cost per million input tokens
$3.00 / 1M$2.50 / 1M
Output Price
Cost per million output tokens
$12.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.33 / 1M$2.02 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text256K tokens~384 A4 pages of text
Max Output Tokens
Per single request
33K16K
Throughput Speed
Tokens generated per second
68 tok/s70 tok/s
SWE-bench Verified
Real-world software engineering
60.5%46%
GPQA Diamond
Graduate-level reasoning
74%61%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.75 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework
Coding
Command A Plus
+14.5% SWE-bench lead
Cost
Command A
1.2x cheaper monthly runtime
Speed
Command A
+3% faster generation
Context
Command A
256K tokens window

权威基准测试得分对比

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

Command A PlusCommand A
SWE-bench Verified (Autonomous Coding)

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

60.5%vs46%
GPQA Diamond (Doctorate-Level Science & Math)

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

74%vs61%
MMLU-Pro (Multi-task Language Understanding)

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

86.8%vs81%
LMSYS Chatbot Arena Elo

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

1410ptsvs1335pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

50%vs42%
AIME (Advanced Math Competition)

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

82.5%vsN/A
Long-Context Reasoning

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

88%vs84%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs70 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
Command A0.52s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command A Plus7.91s
Command A7.66s

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
Command A Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
Command A saves approximately $2.55 / month (16%) under this workload.1.2x 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

Command A · Strengths & Key Advantages

  • 提供 256K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 46% · GPQA Diamond 61%）
  • 高达 70 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 520 毫秒
  • 开放模型权重 (Confidential (~100B+))，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

Token Use & Task Economics

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

MetricCommand A PlusCommand A
Cost per Task
Estimated USD to finish one task
$0.85$0.75
Output Tokens / Task
Tokens generated per task
45K38K
Reasoning Tokens / Task
Thinking tokens burned per task
22K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A PlusCommand A
Official API Model ID (SDK)command-a-pluscommand-a
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-052025-03
Knowledge CutoffMarch 2026January 2025
ArchitectureFlagship Multimodal Agentic MoEAgentic Enterprise Transformer
ParametersConfidential (~180B+ MoE)Confidential (~100B+)
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 ($2.5/1M in) across full 256K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensCached input at $0.75/1M 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) · 62 GB VRAM (4-bit Quantized) · 215 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 Command A: 常见问题解答 (FAQ)

Which is more intelligent, Command A Plus or Command A?

Command A Plus is more intelligent. It scores 58 compared with 51 on the composite Intelligence Index.

Which model is faster?

Command A is faster, generating 70 tokens per second compared with 68 tokens per second.

Which model is cheaper to run?

Command A is cheaper at $2.02 per 1M tokens blended, compared with $2.33 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command A has lower latency, with a time to first token of 0.52s compared with 0.56s.

Which model has a larger context window?

Command A has a larger context window, supporting 256K 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 Command A（适合：专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的任务、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：Command A Plus 在综合智能与编程基准测试中表现更强，Command A 拥有更快的 Token 生成速度，而 Command A 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 Command A 哪个更强？+

在综合智能指数与编程基准评测中，Command A Plus 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 Command A 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），Command A 为输入 $2.50 / 输出 $10.00。综合混合成本来看，Command A 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 Command A 支持 256K Token。在流式生成速度方面，Command A 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.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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