Head-to-Head Model Comparison

Command A vs Aya Vision 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Command AProfile

Aya Vision 32BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command A

Autonomes Agenten-Modell

Command A von Cohere ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 256K Kontextfenster, 70 Token/s Durchsatz, einem Intelligenz-Index von 51/100 und $2.5/$10 pro 1M Token.

Intelligence Index51 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

46%

Cohere

Aya Vision 32B

Mehrsprachiges KI-Modell

Aya Vision 32B von Cohere ist ein KI-Modell für präzise mehrsprachige Übersetzung, globale Kommunikation und sprachübergreifende Analysen mit 16K Kontextfenster, 75 Token/s Durchsatz, einem Intelligenz-Index von 45/100 und $0.35/$0.8 pro 1M

Intelligence Index45 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.80 / 1M

Context Window:

16K tokens

SWE-bench Verified:

32%

Wichtigste Unterschiede auf einen Blick

Aya Vision 32B is 10.2x cheaper for API usage (input + output tokens combined).
Command A has a larger context window (256K vs 16K tokens).
Aya Vision 32B is faster at 75 tok/s vs 70 tok/s.
Command A leads on SWE-bench coding (46.0% vs 32.0%).

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

Feature	Command A	Aya Vision 32B
Intelligence Index Composite reasoning & knowledge score (0-100)	51	45
Provider	Cohere	Cohere
Input Price Cost per million input tokens	$2.50 / 1M	$0.35 / 1M
Output Price Cost per million output tokens	$10.00 / 1M	$0.80 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$2.02 / 1M	$0.21 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	16K tokens~24 A4 pages of text
Max Output Tokens Per single request	16K	4K
Throughput Speed Tokens generated per second	70 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	46%	32%
GPQA Diamond Graduate-level reasoning	61%	51.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.75 / 1M	$0.08 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Command A

+14.0% SWE-bench lead

Cost

Aya Vision 32B

10.2x cheaper monthly runtime

Speed

Aya Vision 32B

+7% faster generation

Context

Command A

256K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Command AAya Vision 32B

SWE-bench Verified (Autonomous Coding)

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

46%vs32%

GPQA Diamond (Doctorate-Level Science & Math)

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

61%vs51.5%

MMLU-Pro (Multi-task Language Understanding)

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

81%vs74%

LMSYS Chatbot Arena Elo

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

1335ptsvs1280pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

42%vsN/A

Long-Context Reasoning

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

84%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 tok/s vs75 tok/s

Latenz & Antwortzeit

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 A0.52s

Aya Vision 32B0.48s

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

Command A7.66s

Aya Vision 32B7.15s

Interaktiver Token-Kosten-Simulator

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 A Monthly API Bill

$13.88 / month

$2.5/1M in · $10/1M out

Aya Vision 32B Monthly API Bill

$1.36 / month

$0.35/1M in · $0.8/1M out

Aya Vision 32B saves approximately $12.51 / month (90%) under this workload.10.2x Savings

Command A · Strengths & Key Advantages

256K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 46% · GPQA Diamond 61%)
Hohe Generierungsgeschwindigkeit von 70 Token/Sekunde bei nur 520 ms Erstlatenz (TTFT)
Offene Modellgewichte (Confidential (~100B+)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Aya Vision 32B · Strengths & Key Advantages

16K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 32% · GPQA Diamond 51.5%)
Hohe Generierungsgeschwindigkeit von 75 Token/Sekunde bei nur 480 ms Erstlatenz (TTFT)
Offene Modellgewichte (32B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Command A	Aya Vision 32B
Cost per Task Estimated USD to finish one task	$0.75	$0.22
Output Tokens / Task Tokens generated per task	38K	25K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Command A	Aya Vision 32B
Official API Model ID (SDK)	command-a	aya-vision-32b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-03	2025-03
Knowledge Cutoff	January 2025	January 2025
Architecture	Agentic Enterprise Transformer	High-Capacity Multilingual VLM
Parameters	Confidential (~100B+)	32B
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 ($2.5/1M in) across full 256K window	Flat rate ($0.35/1M in) across full 16K window
Prompt Cache Policy & TTL	Cached input at $0.75/1M tokens	Cached input at $0.08/1M 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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 62 GB VRAM (4-bit Quantized) · 215 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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, vision
Output Modalities	text	text

Command A vs Aya Vision 32B: Schnelle Antworten (FAQ)

Which is more intelligent, Command A or Aya Vision 32B?

Command A is more intelligent. It scores 51 compared with 45 on the composite Intelligence Index.

Which model is faster?

Aya Vision 32B is faster, generating 75 tokens per second compared with 70 tokens per second.

Which model is cheaper to run?

Aya Vision 32B is cheaper at $0.21 per 1M tokens blended, compared with $2.02 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Aya Vision 32B has lower latency, with a time to first token of 0.48s compared with 0.52s.

Which model has a larger context window?

Command A has a larger context window, supporting 256K tokens compared with 16K tokens.

Fazit

Sowohl Command A (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Aya Vision 32B (Optimal für präzise mehrsprachige Übersetzung, globale Kommunikation und sprachübergreifende Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Command A führt bei komplexem Reasoning und Coding, Aya Vision 32B liefert den höheren Token-Durchsatz und Aya Vision 32B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Command A oder Aya Vision 32B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Command A vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Command A und Aya Vision 32B?+

Command A kostet $2.50 / $10.00 pro 1M Input-/Output-Tokens, Aya Vision 32B liegt bei $0.35 / $0.80. Beim gewichteten Gesamtpreis ist Aya Vision 32B günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Command A bietet 256K Tokens Kontext gegenüber 16K Tokens bei Aya Vision 32B. Bei der Generierungsgeschwindigkeit liegt Aya Vision 32B vorn.

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 A vs Aya Vision 32B

Beliebige Modelle wechseln & vergleichen

Command A

Aya Vision 32B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Command A · Strengths & Key Advantages

Considerations & Trade-offs

Aya Vision 32B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Command A vs Aya Vision 32B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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