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Head-to-Head Model Comparison

Command A Vision vs Command A

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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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 Vision vs Command A: Benchmarks, Pricing & Speed Comparison

Cohere의 Command A Vision와 Cohere의 Command A는 현재 가장 주목받는 AI 모델입니다. Command A Vision는 128K 토큰 컨텍스트 윈도우와 100만 토큰당 $2.50/$10.00(입력/출력)의 요금을 제공하며, Command A는 256K 토큰 컨텍스트와 $2.50/$10.00의 요금을 제공합니다. 아래에서 벤치마크, 지연 시간 및 비용을 상세히 비교해 보세요.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

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Popular Matchups:
Cohere

Command A Vision

네이티브 멀티모달 AI

Cohere의 Command A Vision 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 65 토큰의 생성 속도, 종합 지능 지수 53/100점(1M 토큰당 입력 $2.5 / 출력 $10)을 제공합니다.

Intelligence Index53 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

45%

Cohere

Command A

자율 에이전트 및 도구 호출 특화

Cohere의 Command A 모델은 자율 에이전트 워크플로우, 도구 호출(Tool Calling) 및 다단계 업무 자동화에 최적화된 AI 모델로, 256K 컨텍스트 윈도우, 초당 70 토큰의 생성 속도, 종합 지능 지수 51/100점(1M 토큰당 입력 $2.5 / 출력 $10)을 제공합니다.

Intelligence Index51 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

46%

한눈에 보는 핵심 차이점

  • Both models cost the same under this workload — choose on capability, not price.
  • Command A has a larger context window (256K vs 128K tokens).
  • Command A is faster at 70 tok/s vs 65 tok/s.
  • Command A leads on SWE-bench coding (46.0% vs 45.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand A VisionCommand A
Intelligence Index
Composite reasoning & knowledge score (0-100)
5351
Provider
CohereCohere
Input Price
Cost per million input tokens
$2.50 / 1M$2.50 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.02 / 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
16K16K
Throughput Speed
Tokens generated per second
65 tok/s70 tok/s
SWE-bench Verified
Real-world software engineering
45%46%
GPQA Diamond
Graduate-level reasoning
62.5%61%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.75 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Command A
+1.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Command A
+8% faster generation
Context
Command A
256K tokens window

검증된 벤치마크 점수 비교

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

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

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

45%vs46%
GPQA Diamond (Doctorate-Level Science & Math)

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

62.5%vs61%
MMLU-Pro (Multi-task Language Understanding)

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

81.5%vs81%
LMSYS Chatbot Arena Elo

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

1345ptsvs1335pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

41%vs42%
Long-Context Reasoning

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

83%vs84%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs70 tok/s

지연 시간(Latency) 및 응답 속도

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 Vision0.55s
Command A0.52s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command A Vision8.24s
Command A7.66s

실시간 API 토큰 비용 시뮬레이터

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 Vision Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
Command A Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
These models cost the same under this workload — no savings either way.Break-even

Command A Vision · Strengths & Key Advantages

  • 128K 컨텍스트 윈도우 및 최대 16K 출력 토큰 생성 지원 (SWE-bench Verified 45% · GPQA Diamond 62.5%)
  • 초당 65 토큰의 빠른 처리 속도와 550ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (Confidential (~110B+))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Command A · Strengths & Key Advantages

  • 256K 컨텍스트 윈도우 및 최대 16K 출력 토큰 생성 지원 (SWE-bench Verified 46% · GPQA Diamond 61%)
  • 초당 70 토큰의 빠른 처리 속도와 520ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (Confidential (~100B+))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Token Use & Task Economics

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

MetricCommand A VisionCommand A
Cost per Task
Estimated USD to finish one task
$0.80$0.75
Output Tokens / Task
Tokens generated per task
38K38K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A VisionCommand A
Official API Model ID (SDK)command-a-visioncommand-a
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-072025-03
Knowledge CutoffMay 2025January 2025
ArchitectureEnterprise Multimodal Agent TransformerAgentic Enterprise Transformer
ParametersConfidential (~110B+)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 ($2.5/1M 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) · 68 GB VRAM (4-bit Quantized) · 237 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 Vision vs Command A: 핵심 요약 문답 (FAQ)

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

Command A Vision is more intelligent. It scores 53 compared with 51 on the composite Intelligence Index.

Which model is faster?

Command A is faster, generating 70 tokens per second compared with 65 tokens per second.

Which model has lower latency?

Command A has lower latency, with a time to first token of 0.52s compared with 0.55s.

Which model has a larger context window?

Command A has a larger context window, supporting 256K tokens compared with 128K tokens.

종합 평가

Command A Vision(텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Command A(자율 에이전트 워크플로우, 도구 호출(Tool Calling) 및 다단계 업무 자동화, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Command A Vision가 앞서며, 토큰 생성 속도에서는 Command A가 빠르고, 대규모 API 운영 비용 면에서는 Command A Vision가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Command A Vision와 Command A 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Command A Vision가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Command A Vision와 Command A의 API 요금 차이는 얼마인가요?+

Command A Vision는 100만 토큰당 입력 $2.50 / 출력 $10.00이며, Command A는 입력 $2.50 / 출력 $10.00입니다. 혼합 비용 기준으로는 Command A Vision가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Command A Vision가 128K 토큰, Command A가 256K 토큰를 지원하며, 출력 속도 면에서는 Command A가 더 빠릅니다.

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.

공식 출처: Cohere 및 Cohere 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).