Command A Vision vs Gemma 3n
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command A Vision by Cohere and Gemma 3n by Google 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, Gemma 3n holds a notable pricing advantage for token ingestion. On output generation, Gemma 3n is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Command A Vision provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, Gemma 3n 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 Vision
VLM empresarialmultimodal empresarial agente capaz de comprensión complejo PDFs, financial graphs, y system architectures un través de 128K contexto.
$2.50 / 1M
$10.00 / 1M
128K tokens
45%
Gemma 3n
Abierto en dispositivocompacto en-device abierto modelo optimizado para phones, perimetral devices, y offline voz/visión asistentes.
$0.00 / 1M
$0.00 / 1M
32K tokens
N/A
Diferencias Clave de un Vistazo
- 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.
| Característica | Command A Vision | Gemma 3n |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 53 | 52 |
Provider | Cohere | |
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 |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones 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.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command A Vision · Strengths & Key Advantages
- nativo multimodal visión integrated con Cohere verificable citas fundamentación
- Flawless extracción y analytical razonamiento sobre multi-page PDF documentos y financial filings
- empresarial-grade uso de herramientas connecting visual datos un back-office APIs
Considerations & Trade-offs
- mayor inferencia costo para alto-resolución imagen batches
- video y continuous audio streams no supported
Gemma 3n · Strengths & Key Advantages
- se ejecuta en moderno smartphones y Apple Silicon con minimal power
- Handles en vivo audio y visión inputs locally
- de pesos abiertos
Considerations & Trade-offs
- Smaller 32K contexto horizon
- menor razonamiento profundidad en nuanced programación
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
Verdict & Model Selection Recommendation: Choose Command A Vision if your priority is Auditing complex financial reports, corporate document intelligence, and visual enterprise workflows. Its performance in demanding tasks justifies its operational token cost. Choose Gemma 3n if your workflow prioritizes Embedded hardware, mobile edge intelligence, and offline voice interfaces. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does Command A Vision compare to Gemma 3n in API token pricing and cost per token?+
Command A Vision costs $2.50 per 1M input tokens and $10.00 per 1M output tokens, whereas Gemma 3n costs $0.00 input and $0.00 output. Overall, Gemma 3n 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 Vision or Gemma 3n?+
Command A Vision supports a context window of 128K tokens (~96k words), while Gemma 3n supports 32K tokens (~24k words). Therefore, Command A Vision can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Command A Vision or Gemma 3n?+
Command A Vision scores 45% on SWE-bench Verified, while Gemma 3n scores competitively for software generation. For complex autonomous debugging and agentic engineering, Command A Vision provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Command A Vision and Gemma 3n?+
Command A Vision is optimized for low-latency direct generation. In contrast, Gemma 3n focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command A Vision and Gemma 3n?+
Command A Vision generates approximately 65 tokens per second, while Gemma 3n delivers 150 tokens per second. In latency-sensitive production environments, Gemma 3n provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Command A Vision and Gemma 3n to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemma 3n to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Command A Vision. 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.
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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs