Command A vs Aya Vision 32B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Cohere 的 Command A 与 Cohere 的 Aya Vision 32B 是当前备受关注的两款主力 AI 模型。Command A 提供 256K Token 上下文窗口，API 定价为每百万 Token 输入 $2.50 / 输出 $10.00；而 Aya Vision 32B 提供 16K Token 上下文窗口，定价为每百万 Token 输入 $0.35 / 输出 $0.80。以下为两者的基准测试、延迟与成本全面对比。
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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）。
$2.50 / 1M
$10.00 / 1M
256K tokens
46%
Aya Vision 32B
多语言全球化模型Cohere 推出的 Aya Vision 32B 是一款针对全球多语言理解、跨语种精准翻译与国际化业务深度优化的大模型，具备 16K 上下文窗口、75 Token/秒生成速度与 45/100 综合智能指数（每百万 Token $0.35 输入 / $0.8 输出）。
$0.35 / 1M
$0.80 / 1M
16K tokens
32%
核心差异一览
- Aya Vision 32B is 10.2x cheaper for API usage (input + output tokens combined).
- Command A has a larger context window (256K vs 16K tokens).
- Aya Vision 32B is faster at 75 tok/s vs 70 tok/s.
- Command A leads on SWE-bench coding (46.0% vs 32.0%).
| Feature | Command A | Aya Vision 32B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 51 | 45 |
Provider | Cohere | Cohere |
Input Price Cost per million input tokens | $2.50 / 1M | $0.35 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.80 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.02 / 1M | $0.21 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 16K tokens~24 A4 pages of text |
Max Output Tokens Per single request | 16K | 4K |
Throughput Speed Tokens generated per second | 70 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 46% | 32% |
GPQA Diamond Graduate-level reasoning | 61% | 51.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $0.08 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
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.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
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
Aya Vision 32B · Strengths & Key Advantages
- 提供 16K 上下文窗口与最高 4K 输出容量，核心基准表现优异（SWE-bench Verified 32% · GPQA Diamond 51.5%）
- 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 480 毫秒
- 开放模型权重 (32B)，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
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command A | Aya Vision 32B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.75 | $0.22 |
Output Tokens / Task Tokens generated per task | 38K | 25K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Command A | Aya Vision 32B |
|---|---|---|
| Official API Model ID (SDK) | command-a | aya-vision-32b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-03 | 2025-03 |
| Knowledge Cutoff | January 2025 | January 2025 |
| Architecture | Agentic Enterprise Transformer | High-Capacity Multilingual VLM |
| Parameters | Confidential (~100B+) | 32B |
| 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 256K window | Flat rate ($0.35/1M in) across full 16K window |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Cached input at $0.08/1M 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 62 GB VRAM (4-bit Quantized) · 215 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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 | text, vision |
| Output Modalities | text | text |
Command A vs Aya Vision 32B: 常见问题解答 (FAQ)
Which is more intelligent, Command A or Aya Vision 32B?
Command A is more intelligent. It scores 51 compared with 45 on the composite Intelligence Index.
Which model is faster?
Aya Vision 32B is faster, generating 75 tokens per second compared with 70 tokens per second.
Which model is cheaper to run?
Aya Vision 32B is cheaper at $0.21 per 1M tokens blended, compared with $2.02 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Vision 32B has lower latency, with a time to first token of 0.48s compared with 0.52s.
Which model has a larger context window?
Command A has a larger context window, supporting 256K tokens compared with 16K tokens.
综合结论
综合来看，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。）与 Aya Vision 32B（适合：针对全球多语言理解、跨语种精准翻译与国际化业务深度优化的任务、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 在综合智能与编程基准测试中表现更强，Aya Vision 32B 拥有更快的 Token 生成速度，而 Aya Vision 32B 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 和 Aya Vision 32B 哪个更强？+
在综合智能指数与编程基准评测中，Command A 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 与 Aya Vision 32B 的 API 价格哪个更便宜？+
Command A 的 API 价格为输入 $2.50 / 输出 $10.00（每百万 Token），Aya Vision 32B 为输入 $0.35 / 输出 $0.80。综合混合成本来看，Aya Vision 32B 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 支持 256K Token 上下文窗口，而 Aya Vision 32B 支持 16K Token。在流式生成速度方面，Aya Vision 32B 的吞吐表现更快。
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.
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)。