Command R7B Arabic vs Mistral Small 3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command R7B Arabic da Cohere e Mistral Small 3 da Mistral AI são dois modelos de IA de destaque. O Command R7B Arabic oferece uma janela de contexto de 128K tokens a $0.07/$0.20 por milhão de tokens, enquanto o Mistral Small 3 oferece 32K tokens de contexto a $0.10/$0.30 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
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Command R7B Arabic
Modelo Base de Pesos AbertosO Command R7B Arabic da Cohere é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 135 tokens/s, índice de inteligência de 39/100
$0.07 / 1M
$0.20 / 1M
128K tokens
26%
Mistral Small 3
Modelo Base de Pesos AbertosO Mistral Small 3 da Mistral AI é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 32K, velocidade de 105 tokens/s, índice de inteligência de 41/100
$0.10 / 1M
$0.30 / 1M
32K tokens
31.5%
Principais Diferenças em Resumo
- 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 |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command R7B Arabic · Strengths & Key Advantages
- Janela de contexto de 128K com limite máximo de saída de 4K tokens (SWE-bench Verified 26% · GPQA Diamond 43%)
- Taxa de transferência rápida de 135 tokens/segundo com latência inicial (TTFT) de 310 ms
- Pesos abertos (7B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Mistral Small 3 · Strengths & Key Advantages
- Janela de contexto de 32K com limite máximo de saída de 8K tokens (SWE-bench Verified 31.5% · GPQA Diamond 46.5%)
- Taxa de transferência rápida de 105 tokens/segundo com latência inicial (TTFT) de 380 ms
- Pesos abertos (24B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
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 |
Especificações Técnicas
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: Respostas Rápidas (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.
Veredito
Tanto Command R7B Arabic (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Mistral Small 3 (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Mistral Small 3 lidera em inteligência geral e programação, Command R7B Arabic oferece geração mais rápida e Command R7B Arabic apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Command R7B Arabic ou Mistral Small 3?+
Nas avaliações de inteligência e código, Mistral Small 3 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Command R7B Arabic ou Mistral Small 3?+
O Command R7B Arabic custa $0.07 / $0.20 por 1M de tokens (entrada/saída) e o Mistral Small 3 custa $0.10 / $0.30. No custo combinado, Command R7B Arabic é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Command R7B Arabic suporta 128K tokens de contexto contra 32K tokens do Mistral Small 3, enquanto Command R7B Arabic entrega maior velocidade de geração.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Fontes oficiais: Documentação oficial de API da Cohere e Mistral AI e benchmarks independentes (2026).