Head-to-Head Model Comparison

Command A Vision vs Gemma 3n

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Command A VisionProfile

Gemma 3nProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command A Vision

IA Multimodale Nativa

Command A Vision di Cohere è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 128K, velocità di 65 token/s, indice di intelligenza di 53/100

Intelligence Index53 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

45%

Google

Gemma 3n

Modello Vocale e Audio Nativo

Gemma 3n di Google è un modello di IA progettato per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, con finestra di contesto da 32K, velocità di 150 token/s, indice di intelligenza di 52/100 e tariffa di

Intelligence Index52 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

32K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 128K con capacità massima di output fino a 16K token (SWE-bench Verified 45% · GPQA Diamond 62.5%)
Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 550 ms
Pesi aperti (Confidential (~110B+)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Gemma 3n · Strengths & Key Advantages

Finestra di contesto da 32K con capacità massima di output fino a 4K token (MMLU-Pro 64% · Arena Elo 1220)
Throughput elevato di 150 token/secondo con latenza al primo token (TTFT) di soli 220 ms
Pesi aperti (2B / 5B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia Command A Vision (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) sia Gemma 3n (Ideale per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Command A Vision guida nei benchmark di ragionamento e programmazione, Gemma 3n garantisce una generazione più rapida e Gemma 3n offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Command A Vision o Gemma 3n?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Command A Vision. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Command A Vision e Gemma 3n?+

Command A Vision costa $2.50 / $10.00 per milione di token di input/output, mentre Gemma 3n costa $0.00 / $0.00. Nel costo combinato, Gemma 3n risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Command A Vision supporta 128K token di contesto contro 32K token di Gemma 3n, mentre Gemma 3n offre una velocità di generazione superiore.

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

Cambia e Confronta Qualsiasi Modello

Command A Vision

Gemma 3n

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Command A Vision · Strengths & Key Advantages

Considerations & Trade-offs

Gemma 3n · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Command A Vision vs Gemma 3n: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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