Head-to-Head Model Comparison

Command R7B vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策

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Command R7BProfile

Llama 3.3 70BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command R7B

开源权重基础大模型

Cohere 推出的 Command R7B 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 128K 上下文窗口、140 Token/秒生成速度与 38/100 综合智能指数（每百万 Token $0.07 输入 / $0.2 输出）。

Intelligence Index38 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

27%

Llama 3.3 70B

开源权重基础大模型

Meta 推出的 Llama 3.3 70B 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 128K 上下文窗口、280 Token/秒生成速度与 38/100 综合智能指数（免费开源权重）。

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

核心差异一览

Llama 3.3 70B is 2.1x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 140 tok/s.
Llama 3.3 70B leads on SWE-bench coding (39.5% vs 27.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Command R7B	Llama 3.3 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	38	38
Provider	Cohere	Meta
Input Price Cost per million input tokens	$0.07 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.20 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.05 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	4K
Throughput Speed Tokens generated per second	140 tok/s	280 tok/s
SWE-bench Verified Real-world software engineering	27%	39.5%
GPQA Diamond Graduate-level reasoning	42.5%	48.9%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.10 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework

Coding

Llama 3.3 70B

+12.5% SWE-bench lead

Cost

Llama 3.3 70B

2.1x cheaper monthly runtime

Speed

Llama 3.3 70B

+100% faster generation

Context

Tied

128K tokens window

权威基准测试得分对比

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

Command R7BLlama 3.3 70B

SWE-bench Verified (Autonomous Coding)

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

27%vs39.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

42.5%vs48.9%

MMLU-Pro (Multi-task Language Understanding)

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

66%vs71%

LMSYS Chatbot Arena Elo

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

1215ptsvs1285pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs7.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs26.1%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs23.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.5%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs56.2%

Long-Context Reasoning

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

68%vs63.9%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs280 tok/s

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.

Time to First Token (TTFT)Lower is better

Command R7B0.29s

Llama 3.3 70B0.25s

Est. End-to-End Response (500 tokens)TTFT + generation time

Command R7B3.86s

Llama 3.3 70B2.04s

实时 Token 成本模拟器

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Command R7B Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

Llama 3.3 70B saves approximately $0.17 / month (52%) under this workload.2.1x Savings

Command R7B · Strengths & Key Advantages

提供 128K 上下文窗口与最高 4K 输出容量，核心基准表现优异（SWE-bench Verified 27% · GPQA Diamond 42.5%）
高达 140 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 290 毫秒
开放模型权重 (7B)，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

Llama 3.3 70B · Strengths & Key Advantages

提供 128K 上下文窗口与最高 4K 输出容量，核心基准表现优异（SWE-bench Verified 39.5% · GPQA Diamond 48.9%）
高达 280 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 250 毫秒
开放模型权重 (70B)，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 R7B	Llama 3.3 70B
Cost per Task Estimated USD to finish one task	$0.05	$0.21
Output Tokens / Task Tokens generated per task	20K	45K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

Feature	Command R7B	Llama 3.3 70B
Official API Model ID (SDK)	command-r7b	meta-llama/Llama-3.3-70B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-12	2024-12
Knowledge Cutoff	October 2024	December 2023
Architecture	Efficient Compact RAG Transformer	Dense Autoregressive Transformer
Parameters	7B	70B
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 ($0.07/1M in) across full 128K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

Command R7B vs Llama 3.3 70B: 常见问题解答 (FAQ)

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

Command R7B is cheaper at $0.05 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.29s.

综合结论

综合来看，Command R7B（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、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 Llama 3.3 70B（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、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 R7B 在综合智能与编程基准测试中表现更强，Llama 3.3 70B 拥有更快的 Token 生成速度，而 Command R7B 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 R7B 和 Llama 3.3 70B 哪个更强？+

在综合智能指数与编程基准评测中，Command R7B 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 R7B 与 Llama 3.3 70B 的 API 价格哪个更便宜？+

Command R7B 的 API 价格为输入 $0.07 / 输出 $0.20（每百万 Token），Llama 3.3 70B 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Command R7B 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 R7B 支持 128K Token 上下文窗口，而 Llama 3.3 70B 支持 128K Token。在流式生成速度方面，Llama 3.3 70B 的吞吐表现更快。

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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Command R7B vs Llama 3.3 70B

切换并对比任意模型

Command R7B

Llama 3.3 70B

各项指标谁更胜一筹？

权威基准测试得分对比

延迟与响应速度

实时 Token 成本模拟器

Command R7B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

技术规格参数

Command R7B vs Llama 3.3 70B: 常见问题解答 (FAQ)

综合结论

常见问题 (FAQ)

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