Head-to-Head Model Comparison

Command A Reasoning vs o3

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 ReasoningProfile

o3Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command A Reasoning

Ragionamento a Pesi Aperti

Command A Reasoning di Cohere è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 256K, velocità di 55 token/s, indice di intelligenza di 55/100 e tari

Intelligence Index55 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

53%

OpenAI

o3

Motore di Ragionamento Profondo

o3 di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 200K, velocità di 75 token/s, indice di intelligenza di 56/100 e tariffa di $2/$8 pe

Intelligence Index56 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

69.1%

Differenze Chiave a Colpo d’Occhio

o3 is 1.3x cheaper for API usage (input + output tokens combined).
Command A Reasoning has a larger context window (256K vs 200K tokens).
o3 is faster at 75 tok/s vs 55 tok/s.
o3 leads on SWE-bench coding (69.1% vs 53.0%).

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

Feature	Command A Reasoning	o3
Intelligence Index Composite reasoning & knowledge score (0-100)	55	56
Provider	Cohere	OpenAI
Input Price Cost per million input tokens	$2.50 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$10.00 / 1M	$8.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$2.02 / 1M	$1.55 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	200K tokens~300 A4 pages of text
Max Output Tokens Per single request	33K	100K
Throughput Speed Tokens generated per second	55 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	53%	69.1%
GPQA Diamond Graduate-level reasoning	70%	76.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.75 / 1M	$0.50 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

+16.1% SWE-bench lead

Cost

1.3x cheaper monthly runtime

Speed

+36% faster generation

Context

Command A Reasoning

256K tokens window

Punteggi Benchmark Verificati

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

Command A Reasoningo3

SWE-bench Verified (Autonomous Coding)

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

53%vs69.1%

GPQA Diamond (Doctorate-Level Science & Math)

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

70%vs76.5%

MMLU-Pro (Multi-task Language Understanding)

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

84.5%vs87%

LMSYS Chatbot Arena Elo

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

1370ptsvs1345pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs12.9%

Terminal-Bench (Agentic Coding & Terminal Use)

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

47%vs42%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs42.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs70.5%

AIME (Advanced Math Competition)

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

78%vs91.6%

Long-Context Reasoning

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

86%vs78.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

55 tok/s vs75 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 Reasoning0.75s

o32.80s

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

Command A Reasoning9.84s

o39.47s

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 Reasoning Monthly API Bill

$13.88 / month

$2.5/1M in · $10/1M out

o3 Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

o3 saves approximately $2.93 / month (21%) under this workload.1.3x Savings

Command A Reasoning · Strengths & Key Advantages

Finestra di contesto da 256K con capacità massima di output fino a 33K token (SWE-bench Verified 53% · GPQA Diamond 70%)
Throughput elevato di 55 token/secondo con latenza al primo token (TTFT) di soli 750 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

o3 · Strengths & Key Advantages

Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 69.1% · GPQA Diamond 76.5%)
Throughput elevato di 75 token/secondo con latenza al primo token (TTFT) di soli 2800 ms
Economia API competitiva ($2/$8 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
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.

Metric	Command A Reasoning	o3
Cost per Task Estimated USD to finish one task	$0.95	$1.35
Output Tokens / Task Tokens generated per task	45K	48K
Reasoning Tokens / Task Thinking tokens burned per task	22K	26K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Command A Reasoning	o3
Official API Model ID (SDK)	command-a-reasoning	o3
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2025-08	2025-04
Knowledge Cutoff	June 2025	June 2024
Architecture	Enterprise Chain-of-Thought Reasoning Transformer	Reasoning transformer with adjustable thinking effort
Parameters	Confidential (~100B+)	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($2.5/1M in) across full 256K window	Flat rate ($2/1M in) across full 200K window
Prompt Cache Policy & TTL	Cached input at $0.75/1M tokens	Automatic prefix caching at $0.5/1M (no extra cache-write fee)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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) · 62 GB VRAM (4-bit Quantized) · 215 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision
Output Modalities	text	text

Command A Reasoning vs o3: Risposte Rapide (FAQ)

Which is more intelligent, Command A Reasoning or o3?

o3 is more intelligent. It scores 56 compared with 55 on the composite Intelligence Index.

Which model is faster?

o3 is faster, generating 75 tokens per second compared with 55 tokens per second.

Which model is cheaper to run?

o3 is cheaper at $1.55 per 1M tokens blended, compared with $2.02 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command A Reasoning has lower latency, with a time to first token of 0.75s compared with 2.80s.

Which model has a larger context window?

Command A Reasoning has a larger context window, supporting 256K tokens compared with 200K tokens.

Verdetto

Sia Command A Reasoning (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia o3 (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: o3 guida nei benchmark di ragionamento e programmazione, o3 garantisce una generazione più rapida e o3 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Command A Reasoning o o3?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale o3. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Command A Reasoning e o3?+

Command A Reasoning costa $2.50 / $10.00 per milione di token di input/output, mentre o3 costa $2.00 / $8.00. Nel costo combinato, o3 risulta più economico.

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

Command A Reasoning supporta 256K token di contesto contro 200K token di o3, mentre o3 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 Reasoning vs o3

Cambia e Confronta Qualsiasi Modello

Command A Reasoning

o3

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Command A Reasoning · Strengths & Key Advantages

Considerations & Trade-offs

o3 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Command A Reasoning vs o3: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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