GetInfoUs
Head-to-Head Model Comparison

Command A Reasoning vs o3

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
Teilen
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command A Reasoning vs o3: Benchmarks, Pricing & Speed Comparison

Command A Reasoning von Cohere und o3 von OpenAI gehören zu den führenden KI-Modellen. Command A Reasoning bietet ein Kontextfenster von 256K Tokens bei API-Preisen von $2.50/$10.00 pro 1M Tokens, während o3 200K Tokens Kontext zu $2.00/$8.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

Beliebige Modelle wechseln & vergleichen

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

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
Cohere

Command A Reasoning

Open-Weights-Reasoning-Modell

Command A Reasoning von Cohere ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 256K Kontextfenster, 55 Token/s Durchsatz, einem Intelligenz-Index von 55/100 und $2.5/$10 pro

Intelligence Index55 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

53%

OpenAI

o3

Deep-Reasoning-Engine

o3 von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 75 Token/s Durchsatz, einem Intelligenz-Index von 56/100 und $2/$8 pro 1M Token.

Intelligence Index56 / 100
Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

69.1%

Wichtigste Unterschiede auf einen Blick

  • 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 →
FeatureCommand A Reasoningo3
Intelligence Index
Composite reasoning & knowledge score (0-100)
5556
Provider
CohereOpenAI
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 text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
33K100K
Throughput Speed
Tokens generated per second
55 tok/s75 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

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
o3
+16.1% SWE-bench lead
Cost
o3
1.3x cheaper monthly runtime
Speed
o3
+36% faster generation
Context
Command A Reasoning
256K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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 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

  • 256K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 53% · GPQA Diamond 70%)
  • Hohe Generierungsgeschwindigkeit von 55 Token/Sekunde bei nur 750 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (Confidential (~100B+)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

o3 · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 69.1% · GPQA Diamond 76.5%)
  • Hohe Generierungsgeschwindigkeit von 75 Token/Sekunde bei nur 2800 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$8 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Token Use & Task Economics

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

MetricCommand A Reasoningo3
Cost per Task
Estimated USD to finish one task
$0.95$1.35
Output Tokens / Task
Tokens generated per task
45K48K
Reasoning Tokens / Task
Thinking tokens burned per task
22K26K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A Reasoningo3
Official API Model ID (SDK)command-a-reasoningo3
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2025-082025-04
Knowledge CutoffJune 2025June 2024
ArchitectureEnterprise Chain-of-Thought Reasoning TransformerReasoning transformer with adjustable thinking effort
ParametersConfidential (~100B+)Confidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud10,000 – 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 windowFlat rate ($2/1M in) across full 200K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensAutomatic prefix caching at $0.5/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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 toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision
Output Modalitiestexttext

Command A Reasoning vs o3: Schnelle Antworten (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.

Fazit

Sowohl Command A Reasoning (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch o3 (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: o3 führt bei komplexem Reasoning und Coding, o3 liefert den höheren Token-Durchsatz und o3 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Command A Reasoning oder o3?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt o3 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Command A Reasoning und o3?+

Command A Reasoning kostet $2.50 / $10.00 pro 1M Input-/Output-Tokens, o3 liegt bei $2.00 / $8.00. Beim gewichteten Gesamtpreis ist o3 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Command A Reasoning bietet 256K Tokens Kontext gegenüber 200K Tokens bei o3. Bei der Generierungsgeschwindigkeit liegt o3 vorn.

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.

Offizielle Quellen: Offizielle API-Dokumentation von Cohere und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).