Head-to-Head Model Comparison

Command A Vision vs Gemma 3n

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

Beliebige Modelle wechseln & vergleichen

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Command A VisionProfile

Gemma 3nProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command A Vision

Nativ Multimodale KI

Command A Vision von Cohere ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 128K Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 53/100 und $2.5/$10 pro 1M Token.

Intelligence Index53 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

45%

Google

Gemma 3n

Natives Sprach- & Audiomodell

Gemma 3n von Google ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 32K Kontextfenster, 150 Token/s Durchsatz, einem Intelligenz-Index von 52/100 und kostenlosem Open-Weights-Zugang.

Intelligence Index52 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

32K tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

Gemma 3n is 1.0x cheaper for API usage (input + output tokens combined).
Command A Vision has a larger context window (128K vs 32K tokens).
Gemma 3n is faster at 150 tok/s vs 65 tok/s.

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

Feature	Command A Vision	Gemma 3n
Intelligence Index Composite reasoning & knowledge score (0-100)	53	52
Provider	Cohere	Google
Input Price Cost per million input tokens	$2.50 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$10.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$2.02 / 1M	$0.00 / 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	16K	4K
Throughput Speed Tokens generated per second	65 tok/s	150 tok/s
SWE-bench Verified Real-world software engineering	45%	N/A
GPQA Diamond Graduate-level reasoning	62.5%	N/A
Cached Input Price Prompt caching discount per 1M tokens	$0.75 / 1M	$0.00 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Gemma 3n

1.0x cheaper monthly runtime

Speed

Gemma 3n

+131% faster generation

Context

Command A Vision

128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Command A VisionGemma 3n

SWE-bench Verified (Autonomous Coding)

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

45%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

62.5%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

81.5%vs64%

LMSYS Chatbot Arena Elo

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

1345ptsvs1220pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

41%vsN/A

Long-Context Reasoning

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

83%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs150 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 A Vision0.55s

Gemma 3n0.22s

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

Command A Vision8.24s

Gemma 3n3.55s

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 Vision Monthly API Bill

$13.88 / month

$2.5/1M in · $10/1M out

Gemma 3n Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Gemma 3n saves approximately $13.88 / month (100%) under this workload.1.0x Savings

Command A Vision · Strengths & Key Advantages

128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 45% · GPQA Diamond 62.5%)
Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 550 ms Erstlatenz (TTFT)
Offene Modellgewichte (Confidential (~110B+)) 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

Gemma 3n · Strengths & Key Advantages

32K Kontextfenster und bis zu 4K maximale Output-Token (MMLU-Pro 64% · Arena Elo 1220)
Hohe Generierungsgeschwindigkeit von 150 Token/Sekunde bei nur 220 ms Erstlatenz (TTFT)
Offene Modellgewichte (2B / 5B) 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 Vision	Gemma 3n
Cost per Task Estimated USD to finish one task	$0.80	$0.00
Output Tokens / Task Tokens generated per task	38K	3K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Command A Vision	Gemma 3n
Official API Model ID (SDK)	command-a-vision	models/gemma-3n
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2025-07	2025-06
Knowledge Cutoff	May 2025	March 2025
Architecture	Enterprise Multimodal Agent Transformer	Edge-optimized multimodal transformer with audio support
Parameters	Confidential (~110B+)	2B / 5B
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 128K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.75/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 68 GB VRAM (4-bit Quantized) · 237 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Google DeepMind Frontier Safety Framework Verified
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, vision, audio
Output Modalities	text	text

Command A Vision vs Gemma 3n: Schnelle Antworten (FAQ)

Which is more intelligent, Command A Vision or Gemma 3n?

Command A Vision is more intelligent. It scores 53 compared with 52 on the composite Intelligence Index.

Which model is faster?

Gemma 3n is faster, generating 150 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Gemma 3n is cheaper at $0.00 per 1M tokens blended, compared with $2.02 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemma 3n has lower latency, with a time to first token of 0.22s compared with 0.55s.

Which model has a larger context window?

Command A Vision has a larger context window, supporting 128K tokens compared with 32K tokens.

Fazit

Sowohl Command A Vision (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemma 3n (Optimal für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Command A Vision führt bei komplexem Reasoning und Coding, Gemma 3n liefert den höheren Token-Durchsatz und Gemma 3n ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Command A Vision oder Gemma 3n?+

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

Wie unterscheiden sich die API-Preise von Command A Vision und Gemma 3n?+

Command A Vision kostet $2.50 / $10.00 pro 1M Input-/Output-Tokens, Gemma 3n liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Gemma 3n günstiger.

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

Command A Vision bietet 128K Tokens Kontext gegenüber 32K Tokens bei Gemma 3n. Bei der Generierungsgeschwindigkeit liegt Gemma 3n 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 Vision vs Gemma 3n

Beliebige Modelle wechseln & vergleichen

Command A Vision

Gemma 3n

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Command A Vision · Strengths & Key Advantages

Considerations & Trade-offs

Gemma 3n · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Command A Vision vs Gemma 3n: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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