Head-to-Head Model Comparison

Command A Translate vs Mistral Small 3.2

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 TranslateProfile

Mistral Small 3.2Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command A Translate

Modello Leggero ed Economico

Command A Translate di Cohere è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 8K, velocità di 130 token/s, indice di intelligenza di 40/100 e tariff

Intelligence Index40 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.40 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

N/A

Mistral AI

Mistral Small 3.2

Modello Base a Pesi Aperti

Mistral Small 3.2 di Mistral AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 100 token/s, indice di intelligenza di

Intelligence Index43 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

34%

Differenze Chiave a Colpo d’Occhio

Mistral Small 3.2 is 1.5x cheaper for API usage (input + output tokens combined).
Mistral Small 3.2 has a larger context window (128K vs 8K tokens).
Command A Translate is faster at 130 tok/s vs 100 tok/s.

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

Feature	Command A Translate	Mistral Small 3.2
Intelligence Index Composite reasoning & knowledge score (0-100)	40	43
Provider	Cohere	Mistral AI
Input Price Cost per million input tokens	$0.20 / 1M	$0.10 / 1M
Output Price Cost per million output tokens	$0.40 / 1M	$0.30 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.11 / 1M	$0.09 / 1M
Context Window Maximum input + output tokens	8.2K tokens~12 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	130 tok/s	100 tok/s
SWE-bench Verified Real-world software engineering	N/A	34%
GPQA Diamond Graduate-level reasoning	N/A	48.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.05 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Mistral Small 3.2

1.5x cheaper monthly runtime

Speed

Command A Translate

+30% faster generation

Context

Mistral Small 3.2

128K tokens window

Punteggi Benchmark Verificati

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

Command A TranslateMistral Small 3.2

SWE-bench Verified (Autonomous Coding)

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

N/Avs34%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs48.5%

MMLU-Pro (Multi-task Language Understanding)

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

68%vs72.5%

LMSYS Chatbot Arena Elo

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

1235ptsvs1255pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs29%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs55%

Long-Context Reasoning

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

N/Avs68%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs100 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 Translate0.32s

Mistral Small 3.20.40s

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

Command A Translate4.17s

Mistral Small 3.25.40s

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 Translate Monthly API Bill

$0.74 / month

$0.2/1M in · $0.4/1M out

Mistral Small 3.2 Monthly API Bill

$0.50 / month

$0.1/1M in · $0.3/1M out

Mistral Small 3.2 saves approximately $0.24 / month (33%) under this workload.1.5x Savings

Command A Translate · Strengths & Key Advantages

Finestra di contesto da 8K con capacità massima di output fino a 4K token (MMLU-Pro 68% · Arena Elo 1235)
Throughput elevato di 130 token/secondo con latenza al primo token (TTFT) di soli 320 ms
Pesi aperti (Confidential (~14B)) 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

Mistral Small 3.2 · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 34% · GPQA Diamond 48.5%)
Throughput elevato di 100 token/secondo con latenza al primo token (TTFT) di soli 400 ms
Pesi aperti (24B) 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 Translate	Mistral Small 3.2
Cost per Task Estimated USD to finish one task	$0.04	$0.09
Output Tokens / Task Tokens generated per task	15K	25K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Command A Translate	Mistral Small 3.2
Official API Model ID (SDK)	command-a-translate	mistral-small-3-2-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2025-06
Knowledge Cutoff	June 2025	April 2025
Architecture	Specialized Enterprise Neural Translation Transformer	Compact Vision-Language Transformer
Parameters	Confidential (~14B)	24B
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 ($0.2/1M in) across full 8K window	Flat rate ($0.1/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Cached input at $0.05/1M 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) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 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 Translate vs Mistral Small 3.2: Risposte Rapide (FAQ)

Which is more intelligent, Command A Translate or Mistral Small 3.2?

Mistral Small 3.2 is more intelligent. It scores 43 compared with 40 on the composite Intelligence Index.

Which model is faster?

Command A Translate is faster, generating 130 tokens per second compared with 100 tokens per second.

Which model is cheaper to run?

Mistral Small 3.2 is cheaper at $0.09 per 1M tokens blended, compared with $0.11 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command A Translate has lower latency, with a time to first token of 0.32s compared with 0.40s.

Which model has a larger context window?

Mistral Small 3.2 has a larger context window, supporting 128K tokens compared with 8.2K tokens.

Verdetto

Sia Command A Translate (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) sia Mistral Small 3.2 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mistral Small 3.2 guida nei benchmark di ragionamento e programmazione, Command A Translate garantisce una generazione più rapida e Mistral Small 3.2 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Command A Translate o Mistral Small 3.2?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mistral Small 3.2. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Command A Translate e Mistral Small 3.2?+

Command A Translate costa $0.20 / $0.40 per milione di token di input/output, mentre Mistral Small 3.2 costa $0.10 / $0.30. Nel costo combinato, Mistral Small 3.2 risulta più economico.

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

Command A Translate supporta 8.2K token di contesto contro 128K token di Mistral Small 3.2, mentre Command A Translate 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 Translate vs Mistral Small 3.2

Cambia e Confronta Qualsiasi Modello

Command A Translate

Mistral Small 3.2

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Command A Translate · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Small 3.2 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Command A Translate vs Mistral Small 3.2: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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