Command R vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Command R by Cohere against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Command R
开源权重模型Cohere 推出的 Command R 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.15/百万 Token，输出价格为 $0.6/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.15 / 1M
$0.60 / 1M
128K tokens
32%
Claude Opus 5.5
深度推理模型Anthropic 推出的 Claude Opus 5.5 人工智能模型，具备 1M 上下文窗口，输入价格为 $4.0/百万 Token，输出价格为 $20.0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
核心差异一览
- Command R is 29.3x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- Command R is faster at 85 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 32.0%).
| Feature | Command R | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 94 |
Provider | Cohere | Anthropic |
Input Price Cost per million input tokens | $0.15 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.13 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 85 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 32% | 84.6% |
GPQA Diamond Graduate-level reasoning | 46% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.40 / 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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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 R · Strengths & Key Advantages
- 由 Cohere 研发的高精度 Retrieval-Augmented Dense Transformer 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.15/1M，输出 $0.6/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Claude Opus 5.5 · Strengths & Key Advantages
- 由 Anthropic 研发的高精度 Extended Autonomous Hybrid Reasoning Transformer 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $4.0/1M，输出 $20.0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command R | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.12 | $3.85 |
Output Tokens / Task Tokens generated per task | 25K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Command R | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | command-r | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-08 | 2026-09 |
| Knowledge Cutoff | June 2024 | June 2026 |
| Architecture | Retrieval-Augmented Dense Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 35B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.15/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| 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) · 22 GB VRAM (4-bit Quantized) · 75 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Command R vs Claude Opus 5.5: 常见问题解答 (FAQ)
Which is more intelligent, Command R or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 43 on the composite Intelligence Index.
Which model is faster?
Command R is faster, generating 85 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Command R is cheaper at $0.13 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command R has lower latency, with a time to first token of 0.42s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 128K tokens.
综合结论
Choose Command R for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 cohere 生态深度集成, or choose Claude Opus 5.5 for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 anthropic 生态深度集成.
常见问题 (FAQ)
Which is cheaper: Command R or Claude Opus 5.5?+
Command R costs $0.15/1M input and $0.6/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Command R or Claude Opus 5.5?+
Command R supports 128K tokens whereas Claude Opus 5.5 supports 1M tokens.
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 & Anthropic Official Documentation