Command A Plus vs Command A
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command A Plus da Cohere e Command A da Cohere são dois modelos de IA de destaque. O Command A Plus oferece uma janela de contexto de 128K tokens a $3.00/$12.00 por milhão de tokens, enquanto o Command A oferece 256K tokens de contexto a $2.50/$10.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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Command A Plus
Modelo Base de Pesos AbertosO Command A Plus 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 68 tokens/s, índice de inteligência de 58/100 e ta
$3.00 / 1M
$12.00 / 1M
128K tokens
60.5%
Command A
IA Agêntica e Uso de FerramentasO Command A da Cohere é um modelo de IA projetado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, oferecendo janela de contexto de 256K, velocidade de 70 tokens/s, índice de inteligência de 51/100 e tarifa d
$2.50 / 1M
$10.00 / 1M
256K tokens
46%
Principais Diferenças em Resumo
- Command A is 1.2x cheaper for API usage (input + output tokens combined).
- Command A has a larger context window (256K vs 128K tokens).
- Command A is faster at 70 tok/s vs 68 tok/s.
- Command A Plus leads on SWE-bench coding (60.5% vs 46.0%).
| Feature | Command A Plus | Command A |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 51 |
Provider | Cohere | Cohere |
Input Price Cost per million input tokens | $3.00 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $12.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.33 / 1M | $2.02 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 256K tokens~384 A4 pages of text |
Max Output Tokens Per single request | 33K | 16K |
Throughput Speed Tokens generated per second | 68 tok/s | 70 tok/s |
SWE-bench Verified Real-world software engineering | 60.5% | 46% |
GPQA Diamond Graduate-level reasoning | 74% | 61% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $0.75 / 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 A Plus · Strengths & Key Advantages
- Janela de contexto de 128K com limite máximo de saída de 33K tokens (SWE-bench Verified 60.5% · GPQA Diamond 74%)
- Taxa de transferência rápida de 68 tokens/segundo com latência inicial (TTFT) de 560 ms
- Pesos abertos (Confidential (~180B+ MoE)) 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
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Command A · Strengths & Key Advantages
- Janela de contexto de 256K com limite máximo de saída de 16K tokens (SWE-bench Verified 46% · GPQA Diamond 61%)
- Taxa de transferência rápida de 70 tokens/segundo com latência inicial (TTFT) de 520 ms
- Pesos abertos (Confidential (~100B+)) 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
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command A Plus | Command A |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.85 | $0.75 |
Output Tokens / Task Tokens generated per task | 45K | 38K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Command A Plus | Command A |
|---|---|---|
| Official API Model ID (SDK) | command-a-plus | command-a |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-05 | 2025-03 |
| Knowledge Cutoff | March 2026 | January 2025 |
| Architecture | Flagship Multimodal Agentic MoE | Agentic Enterprise Transformer |
| Parameters | Confidential (~180B+ MoE) | Confidential (~100B+) |
| 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 ($3/1M in) across full 128K window | Flat rate ($2.5/1M in) across full 256K window |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Cached input at $0.75/1M 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 112 GB VRAM (4-bit Quantized) · 387 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 62 GB VRAM (4-bit Quantized) · 215 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, vision | text |
| Output Modalities | text | text |
Command A Plus vs Command A: Respostas Rápidas (FAQ)
Which is more intelligent, Command A Plus or Command A?
Command A Plus is more intelligent. It scores 58 compared with 51 on the composite Intelligence Index.
Which model is faster?
Command A is faster, generating 70 tokens per second compared with 68 tokens per second.
Which model is cheaper to run?
Command A is cheaper at $2.02 per 1M tokens blended, compared with $2.33 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command A has lower latency, with a time to first token of 0.52s compared with 0.56s.
Which model has a larger context window?
Command A has a larger context window, supporting 256K tokens compared with 128K tokens.
Veredito
Tanto Command A Plus (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Command A (Indicado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Command A Plus lidera em inteligência geral e programação, Command A oferece geração mais rápida e Command A apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Command A Plus ou Command A?+
Nas avaliações de inteligência e código, Command A Plus leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Command A Plus ou Command A?+
O Command A Plus custa $3.00 / $12.00 por 1M de tokens (entrada/saída) e o Command A custa $2.50 / $10.00. No custo combinado, Command A é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Command A Plus suporta 128K tokens de contexto contra 256K tokens do Command A, enquanto Command A 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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- Command A Plus vs. Command A Reasoning: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Command A Plus vs. Gemini 4 Argon: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Command A Plus vs. GPT-6 Astra: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Command A Plus vs. Nemotron 3 Ultra 550B A55B: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Command A Vision vs. Command A: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
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 Cohere e benchmarks independentes (2026).