Command A Plus vs Command A
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command A Plus by Cohere and Command A by Cohere 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 A holds a notable pricing advantage for token ingestion. On output generation, Command A is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Command A provides an expanded memory horizon with 256K tokens. In latency-sensitive production environments, Command A 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 A Plus
Open-Weights-ModellCommand A Plus von Cohere ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 128K Token zu API-Preisen von $3/1M Input- und $12/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$3.00 / 1M
$12.00 / 1M
128K tokens
60.5%
Command A
Open-Weights-ModellCommand A von Cohere ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 256K Token zu API-Preisen von $2.5/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.50 / 1M
$10.00 / 1M
256K tokens
46%
Wichtigste Unterschiede auf einen Blick
- 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 |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command A Plus · Strengths & Key Advantages
- Fortschrittliche Flagship Multimodal Agentic MoE-Architektur von Cohere mit 128K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($3/1M Input, $12/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Command A · Strengths & Key Advantages
- Fortschrittliche Agentic Enterprise Transformer-Architektur von Cohere mit 256K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2.5/1M Input, $10/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
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 |
Technische Spezifikationen
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: Schnelle Antworten (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.
Fazit
Verdict & Model Selection Recommendation: Choose Command A Plus if your priority is Enterprise intelligence platforms, sovereign government automation, and mission-critical agent workflows. Its performance in demanding tasks justifies its operational token cost. Choose Command A if your workflow prioritizes Enterprise autonomous agents, multi-step ERP workflows, and secure corporate automation. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does Command A Plus compare to Command A in API token pricing and cost per token?+
Command A Plus costs $3.00 per 1M input tokens and $12.00 per 1M output tokens, whereas Command A costs $2.50 input and $10.00 output. Overall, Command A 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 A Plus or Command A?+
Command A Plus supports a context window of 128K tokens (~96k words), while Command A supports 256K tokens (~192k words). Therefore, Command A can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Command A Plus or Command A?+
Command A Plus scores 60.5% on SWE-bench Verified while Command A scores 46%. For autonomous software engineering and multi-file debugging, Command A Plus holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Command A Plus and Command A?+
Command A Plus features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Command A focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command A Plus and Command A?+
Command A Plus generates approximately 68 tokens per second, while Command A delivers 70 tokens per second. In latency-sensitive production environments, Command A provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Command A Plus and Command A to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Command A to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Command A Plus. 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.
Related reading
- Command A Plus vs Claude Opus 5.5: Benchmarks, Pricing & Speed ComparisonVergleich
- Command A Plus vs Command A Reasoning: Benchmarks, Pricing & Speed ComparisonVergleich
- Command A Plus vs Gemini 4 Argon: Benchmarks, Pricing & Speed ComparisonVergleich
- Command A Plus vs GPT-6 Astra: Benchmarks, Pricing & Speed ComparisonVergleich
- Command A Plus vs Nemotron 3 Ultra 550B A55B: Benchmarks, Pricing & Speed ComparisonVergleich
- Command A Vision vs Command A: Benchmarks, Pricing & Speed ComparisonVergleich
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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs