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Head-to-Head Model Comparison

Command A Vision vs Command A

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command A Vision vs Command A: Benchmarks, Pricing & Speed Comparison

Command A Vision di Cohere e Command A di Cohere sono due modelli di IA di riferimento. Command A Vision offre una finestra di contesto di 128K token a $2.50/$10.00 per milione di token, mentre Command A mette a disposizione 256K token di contesto a $2.50/$10.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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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%

Cohere

Command A

IA Agentica e Strumenti Autonomi

Command A di Cohere è un modello di IA progettato per i workflow agentici autonomi, il tool calling e l’automazione multi-step, con finestra di contesto da 256K, velocità di 70 token/s, indice di intelligenza di 51/100 e tariffa di $2.5/$10

Intelligence Index51 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

46%

Differenze Chiave a Colpo d’Occhio

  • Both models cost the same under this workload — choose on capability, not price.
  • Command A has a larger context window (256K vs 128K tokens).
  • Command A is faster at 70 tok/s vs 65 tok/s.
  • Command A leads on SWE-bench coding (46.0% vs 45.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand A VisionCommand A
Intelligence Index
Composite reasoning & knowledge score (0-100)
5351
Provider
CohereCohere
Input Price
Cost per million input tokens
$2.50 / 1M$2.50 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.02 / 1M$2.02 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text256K tokens~384 A4 pages of text
Max Output Tokens
Per single request
16K16K
Throughput Speed
Tokens generated per second
65 tok/s70 tok/s
SWE-bench Verified
Real-world software engineering
45%46%
GPQA Diamond
Graduate-level reasoning
62.5%61%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.75 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
Command A
+1.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Command A
+8% faster generation
Context
Command A
256K tokens window

Punteggi Benchmark Verificati

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

Command A VisionCommand A
SWE-bench Verified (Autonomous Coding)

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

45%vs46%
GPQA Diamond (Doctorate-Level Science & Math)

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

62.5%vs61%
MMLU-Pro (Multi-task Language Understanding)

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

81.5%vs81%
LMSYS Chatbot Arena Elo

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

1345ptsvs1335pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

41%vs42%
Long-Context Reasoning

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

83%vs84%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs70 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
Command A0.52s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command A Vision8.24s
Command A7.66s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Command A Vision Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
Command A Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
These models cost the same under this workload — no savings either way.Break-even

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

Command A · Strengths & Key Advantages

  • Finestra di contesto da 256K con capacità massima di output fino a 16K token (SWE-bench Verified 46% · GPQA Diamond 61%)
  • Throughput elevato di 70 token/secondo con latenza al primo token (TTFT) di soli 520 ms
  • Pesi aperti (Confidential (~100B+)) 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

Token Use & Task Economics

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

MetricCommand A VisionCommand A
Cost per Task
Estimated USD to finish one task
$0.80$0.75
Output Tokens / Task
Tokens generated per task
38K38K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A VisionCommand A
Official API Model ID (SDK)command-a-visioncommand-a
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-072025-03
Knowledge CutoffMay 2025January 2025
ArchitectureEnterprise Multimodal Agent TransformerAgentic Enterprise Transformer
ParametersConfidential (~110B+)Confidential (~100B+)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 windowFlat rate ($2.5/1M in) across full 256K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensCached input at $0.75/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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) · 62 GB VRAM (4-bit Quantized) · 215 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Command A Vision vs Command A: Risposte Rapide (FAQ)

Which is more intelligent, Command A Vision or Command A?

Command A Vision is more intelligent. It scores 53 compared with 51 on the composite Intelligence Index.

Which model is faster?

Command A is faster, generating 70 tokens per second compared with 65 tokens per second.

Which model has lower latency?

Command A has lower latency, with a time to first token of 0.52s compared with 0.55s.

Which model has a larger context window?

Command A has a larger context window, supporting 256K tokens compared with 128K 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 Command A (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Command A Vision guida nei benchmark di ragionamento e programmazione, Command A garantisce una generazione più rapida e Command A Vision 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 Command A?+

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 Command A?+

Command A Vision costa $2.50 / $10.00 per milione di token di input/output, mentre Command A costa $2.50 / $10.00. Nel costo combinato, Command A Vision 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 256K token di Command A, mentre Command A 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Fonti ufficiali: Documentazione API ufficiale di Cohere e Cohere, schede di sistema e benchmark indipendenti (2026).