Command R7B Arabic vs Mistral Small 3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command R7B Arabic by Cohere and Mistral Small 3 by Mistral AI 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, Command R7B Arabic holds a notable pricing advantage for token ingestion. On output generation, Command R7B Arabic is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Command R7B Arabic provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, Command R7B Arabic 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.
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Command R7B Arabic
开源权重模型Cohere 推出的 Command R7B Arabic 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.07/百万 Token，输出价格为 $0.2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.07 / 1M
$0.20 / 1M
128K tokens
26%
Mistral Small 3
开源权重模型Mistral AI 推出的 Mistral Small 3 人工智能模型，具备 32K 上下文窗口，输入价格为 $0.1/百万 Token，输出价格为 $0.3/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.10 / 1M
$0.30 / 1M
32K tokens
31.5%
核心差异一览
- Command R7B Arabic is 1.6x cheaper for API usage (input + output tokens combined).
- Command R7B Arabic has a larger context window (128K vs 32K tokens).
- Command R7B Arabic is faster at 135 tok/s vs 105 tok/s.
- Mistral Small 3 leads on SWE-bench coding (31.5% vs 26.0%).
| Feature | Command R7B Arabic | Mistral Small 3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 41 |
Provider | Cohere | Mistral AI |
Input Price Cost per million input tokens | $0.07 / 1M | $0.10 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $0.30 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.09 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 32K tokens~48 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 135 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 26% | 31.5% |
GPQA Diamond Graduate-level reasoning | 43% | 46.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.05 / 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.
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 R7B Arabic · Strengths & Key Advantages
- 由 Cohere 研发的高精度 Arabic-Specialized RAG Transformer 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.07/1M，输出 $0.2/1M），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
Mistral Small 3 · Strengths & Key Advantages
- 由 Mistral AI 研发的高精度 Compact Dense Transformer 架构，支持 32K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.1/1M，输出 $0.3/1M），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 R7B Arabic | Mistral Small 3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $0.08 |
Output Tokens / Task Tokens generated per task | 20K | 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 R7B Arabic | Mistral Small 3 |
|---|---|---|
| Official API Model ID (SDK) | command-r7b-arabic | mistral-small-3-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-02 | 2025-01 |
| Knowledge Cutoff | December 2024 | December 2024 |
| Architecture | Arabic-Specialized RAG Transformer | Compact Dense Transformer |
| Parameters | 7B | 24B |
| 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 | Flat rate ($0.1/1M in) across full 32K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Cached input at $0.05/1M 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) |
| 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) · 15 GB VRAM (4-bit Quantized) · 52 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 Arabic vs Mistral Small 3: 常见问题解答 (FAQ)
Which is more intelligent, Command R7B Arabic or Mistral Small 3?
Mistral Small 3 is more intelligent. It scores 41 compared with 39 on the composite Intelligence Index.
Which model is faster?
Command R7B Arabic is faster, generating 135 tokens per second compared with 105 tokens per second.
Which model is cheaper to run?
Command R7B Arabic is cheaper at $0.05 per 1M tokens blended, compared with $0.09 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command R7B Arabic has lower latency, with a time to first token of 0.31s compared with 0.38s.
Which model has a larger context window?
Command R7B Arabic has a larger context window, supporting 128K tokens compared with 32K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Command R7B Arabic if your priority is Middle Eastern government digitization, Arabic enterprise search, and regional customer support. Its performance in demanding tasks justifies its operational token cost. Choose Mistral Small 3 if your workflow prioritizes High-volume customer support, classification pipelines, and low-latency chatbots. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Command R7B Arabic compare to Mistral Small 3 in API token pricing and cost per token?+
Command R7B Arabic costs $0.07 per 1M input tokens and $0.20 per 1M output tokens, whereas Mistral Small 3 costs $0.10 input and $0.30 output. Overall, Command R7B Arabic is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Command R7B Arabic or Mistral Small 3?+
Command R7B Arabic supports a context window of 128K tokens (~96k words), while Mistral Small 3 supports 32K tokens (~24k words). Therefore, Command R7B Arabic can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Command R7B Arabic or Mistral Small 3?+
Command R7B Arabic scores 26% on SWE-bench Verified while Mistral Small 3 scores 31.5%. For autonomous software engineering and multi-file debugging, Mistral Small 3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Command R7B Arabic and Mistral Small 3?+
Command R7B Arabic is optimized for low-latency direct generation. In contrast, Mistral Small 3 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command R7B Arabic and Mistral Small 3?+
Command R7B Arabic generates approximately 135 tokens per second, while Mistral Small 3 delivers 105 tokens per second. In latency-sensitive production environments, Command R7B Arabic provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Command R7B Arabic and Mistral Small 3 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Command R7B Arabic to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mistral Small 3. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs