Command A Vision vs Command A
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command A Vision 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, both models offer identical input token rates. On output generation, costs are matched. 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.
任意のAIモデルを切り替えて比較
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Command A Vision
オープンウェイトモデルCohere が提供する Command A Vision は、最大 128K トークンのコンテキストウィンドウを備え、入力 $2.5/100万トークン・出力 $10/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.50 / 1M
$10.00 / 1M
128K tokens
45%
Command A
オープンウェイトモデルCohere が提供する Command A は、最大 256K トークンのコンテキストウィンドウを備え、入力 $2.5/100万トークン・出力 $10/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.50 / 1M
$10.00 / 1M
256K tokens
46%
ひと目でわかる主な違い
- 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%).
| Feature | Command A Vision | Command A |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 53 | 51 |
Provider | Cohere | Cohere |
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 text | 256K tokens~384 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 65 tok/s | 70 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 |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command A Vision · Strengths & Key Advantages
- Cohere の高度な Enterprise Multimodal Agent Transformer アーキテクチャと 128K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2.5/1M、出力 $10/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Command A · Strengths & Key Advantages
- Cohere の高度な Agentic Enterprise Transformer アーキテクチャと 256K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2.5/1M、出力 $10/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command A Vision | Command A |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.80 | $0.75 |
Output Tokens / Task Tokens generated per task | 38K | 38K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Command A Vision | Command A |
|---|---|---|
| Official API Model ID (SDK) | command-a-vision | command-a |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-07 | 2025-03 |
| Knowledge Cutoff | May 2025 | January 2025 |
| Architecture | Enterprise Multimodal Agent Transformer | Agentic Enterprise Transformer |
| Parameters | Confidential (~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 Cloud | Unlimited (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 window | Flat rate ($2.5/1M in) across full 256K window |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Cached input at $0.75/1M tokens |
| Computer Use / GUI Agent | API / 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 Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full 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 Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / 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 Classification | Open-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 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Command A Vision vs Command A: よくある質問 (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.
総合評価
Verdict & Model Selection Recommendation: Choose Command A Vision if your priority is Auditing complex financial reports, corporate document intelligence, and visual enterprise 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.
よくある質問 (FAQ)
How does Command A Vision compare to Command A in API token pricing and cost per token?+
Both Command A Vision and Command A share matched API token pricing at $2.50 per 1M input tokens and $10.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Command A Vision or Command A?+
Command A Vision 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 Vision or Command A?+
Command A Vision scores 45% on SWE-bench Verified while Command A scores 46%. For autonomous software engineering and multi-file debugging, Command A holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Command A Vision and Command A?+
Command A Vision is optimized for low-latency direct generation. In contrast, Command A focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command A Vision and Command A?+
Command A Vision generates approximately 65 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 Vision and Command A to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Command A, and route deep architectural or complex reasoning queries to Command A Vision. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs