Head-to-Head Model Comparison

Command A Plus vs Command A

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Проверено исследовательской группой GetInfoUsПоследняя проверка: October 2026Редакционная политика
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Related topics (12)
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

Command A Plus by Cohere and Command A by Cohere 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, Command A holds a notable pricing advantage for token ingestion. On output generation, Command A is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Command A provides an expanded memory horizon with 256K tokens. In latency-sensitive production environments, Command A 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.

Прямое сравнение (VS)

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Command A Plus

Открытые веса (Open Weights)

Command A Plus от Cohere — передовая модель искусственного интеллекта с контекстным окном 128K токенов и стоимостью API $3/1M входящих и $12/1M исходящих токенов для программирования, аналитики и автоматизации.

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

Открытые веса (Open Weights)

Command A от Cohere — передовая модель искусственного интеллекта с контекстным окном 256K токенов и стоимостью API $2.5/1M входящих и $10/1M исходящих токенов для программирования, аналитики и автоматизации.

Intelligence Index51 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

46%

Ключевые отличия кратко

  • 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

Кто лидирует в каждой категории?

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

Результаты проверенных бенчмарков

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

Задержка и скорость ответа

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

Интерактивный калькулятор стоимости токенов

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

  • Современная архитектура Flagship Multimodal Agentic MoE от Cohere с поддержкой до 128K токенов контекста
  • Оптимальная стоимость использования ($3/1M вход, $12/1M выход) и высокая скорость генерации
  • Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
  • При работе с максимальным контекстом требуется контроль расхода токенов
  • Для узкоспециализированных задач рекомендуется точная настройка системных промптов

Command A · Strengths & Key Advantages

  • Современная архитектура Agentic Enterprise Transformer от Cohere с поддержкой до 256K токенов контекста
  • Оптимальная стоимость использования ($2.5/1M вход, $10/1M выход) и высокая скорость генерации
  • Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
  • При работе с максимальным контекстом требуется контроль расхода токенов
  • Для узкоспециализированных задач рекомендуется точная настройка системных промптов

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

Технические характеристики

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: Быстрые ответы (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.

Вердикт

Verdict & Model Selection Recommendation: Choose Command A Plus if your priority is Enterprise intelligence platforms, sovereign government automation, and mission-critical agent workflows. Its performance in demanding tasks justifies its operational token cost. Choose Command A if your workflow prioritizes Enterprise autonomous agents, multi-step ERP workflows, and secure corporate automation. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

Часто задаваемые вопросы (FAQ)

How does Command A Plus compare to Command A in API token pricing and cost per token?+

Command A Plus costs $3.00 per 1M input tokens and $12.00 per 1M output tokens, whereas Command A costs $2.50 input and $10.00 output. Overall, Command A 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: Command A Plus or Command A?+

Command A Plus supports a context window of 128K tokens (~96k words), while Command A supports 256K tokens (~192k words). Therefore, Command A can process more textual data and document context in a single prompt.

Which AI model is better for software engineering and coding: Command A Plus or Command A?+

Command A Plus scores 60.5% on SWE-bench Verified while Command A scores 46%. For autonomous software engineering and multi-file debugging, Command A Plus holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between Command A Plus and Command A?+

Command A Plus features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Command A focuses on standard fast inference.

How does inference speed (tokens per second) compare between Command A Plus and Command A?+

Command A Plus generates approximately 68 tokens per second, while Command A delivers 70 tokens per second. In latency-sensitive production environments, Command A provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Command A Plus and Command A to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Command A to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Command A Plus. This tiered routing pattern cuts overall API bills by up to 70%.

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.

Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs