Head-to-Head Model Comparison

Command A Plus vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Command A PlusProfile

Gemini 4 ArgonProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command A Plus

Open-Weights-Basismodell

Command A Plus von Cohere ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 68 Token/s Durchsatz, einem Intelligenz-Index von 58/100 und $3/$12 pro 1M Token.

Intelligence Index58 / 100

Input Token:

$3.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

60.5%

Google

Gemini 4 Argon

Nativ Multimodale KI

Gemini 4 Argon von Google ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1M Kontextfenster, 115 Token/s Durchsatz, einem Intelligenz-Index von 97/100 und $2/$10 pro 1M Token.

Intelligence Index97 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

Wichtigste Unterschiede auf einen Blick

Gemini 4 Argon is 1.4x cheaper for API usage (input + output tokens combined).
Gemini 4 Argon has a larger context window (1M vs 128K tokens).
Gemini 4 Argon is faster at 115 tok/s vs 68 tok/s.
Gemini 4 Argon leads on SWE-bench coding (81.9% vs 60.5%).

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

Feature	Command A Plus	Gemini 4 Argon
Intelligence Index Composite reasoning & knowledge score (0-100)	58	97
Provider	Cohere	Google
Input Price Cost per million input tokens	$3.00 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$12.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$2.33 / 1M	$1.47 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	33K	1000K
Throughput Speed Tokens generated per second	68 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	60.5%	81.9%
GPQA Diamond Graduate-level reasoning	74%	81.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.75 / 1M	$0.10 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Gemini 4 Argon

+21.4% SWE-bench lead

Cost

Gemini 4 Argon

1.4x cheaper monthly runtime

Speed

Gemini 4 Argon

+69% faster generation

Context

Gemini 4 Argon

1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Command A PlusGemini 4 Argon

SWE-bench Verified (Autonomous Coding)

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

60.5%vs81.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

74%vs81.5%

MMLU-Pro (Multi-task Language Understanding)

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

86.8%vs90.8%

LMSYS Chatbot Arena Elo

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

1410ptsvs1525pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.9%

Terminal-Bench (Agentic Coding & Terminal Use)

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

50%vs57.4%

SciCode (Scientific Computing)

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

N/Avs56.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs80.5%

AIME (Advanced Math Competition)

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

82.5%vs94.8%

Long-Context Reasoning

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

88%vs96.4%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs115 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 Plus0.56s

Gemini 4 Argon0.85s

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

Command A Plus7.91s

Gemini 4 Argon5.20s

Interaktiver Token-Kosten-Simulator

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 Plus Monthly API Bill

$16.43 / month

$3/1M in · $12/1M out

Gemini 4 Argon Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

Gemini 4 Argon saves approximately $4.28 / month (26%) under this workload.1.4x Savings

Command A Plus · Strengths & Key Advantages

128K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 60.5% · GPQA Diamond 74%)
Hohe Generierungsgeschwindigkeit von 68 Token/Sekunde bei nur 560 ms Erstlatenz (TTFT)
Offene Modellgewichte (Confidential (~180B+ MoE)) 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

Gemini 4 Argon · Strengths & Key Advantages

1M Kontextfenster und bis zu 1M maximale Output-Token (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
Hohe Generierungsgeschwindigkeit von 115 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($2/$10 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.

Metric	Command A Plus	Gemini 4 Argon
Cost per Task Estimated USD to finish one task	$0.85	$2.45
Output Tokens / Task Tokens generated per task	45K	75K
Reasoning Tokens / Task Thinking tokens burned per task	22K	36K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Command A Plus	Gemini 4 Argon
Official API Model ID (SDK)	command-a-plus	models/gemini-4-argon
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-05	2026-09
Knowledge Cutoff	March 2026	June 2026
Architecture	Flagship Multimodal Agentic MoE	Pathways Multimodal Sparse Mixture-of-Experts
Parameters	Confidential (~180B+ MoE)	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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($3/1M in) across full 128K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Cached input at $0.75/1M tokens	Implicit + Explicit Context Caching at $0.1/1M with configurable TTL
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)	Vertex AI Supervised Adapter Tuning & Context Distillation
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) · Video (~258–300 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 112 GB VRAM (4-bit Quantized) · 387 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)	Google DeepMind Frontier Safety Framework Verified
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, vision	text, vision, audio, video
Output Modalities	text	text

Command A Plus vs Gemini 4 Argon: Schnelle Antworten (FAQ)

Which is more intelligent, Command A Plus or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 58 on the composite Intelligence Index.

Which model is faster?

Gemini 4 Argon is faster, generating 115 tokens per second compared with 68 tokens per second.

Which model is cheaper to run?

Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $2.33 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command A Plus has lower latency, with a time to first token of 0.56s compared with 0.85s.

Which model has a larger context window?

Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.

Fazit

Sowohl Command A Plus (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 4 Argon (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 4 Argon führt bei komplexem Reasoning und Coding, Gemini 4 Argon liefert den höheren Token-Durchsatz und Gemini 4 Argon ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Command A Plus oder Gemini 4 Argon?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 4 Argon vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Command A Plus und Gemini 4 Argon?+

Command A Plus kostet $3.00 / $12.00 pro 1M Input-/Output-Tokens, Gemini 4 Argon liegt bei $2.00 / $10.00. Beim gewichteten Gesamtpreis ist Gemini 4 Argon günstiger.

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

Command A Plus bietet 128K Tokens Kontext gegenüber 1M Tokens bei Gemini 4 Argon. Bei der Generierungsgeschwindigkeit liegt Gemini 4 Argon 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Command A Plus vs Gemini 4 Argon

Beliebige Modelle wechseln & vergleichen

Command A Plus

Gemini 4 Argon

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Command A Plus · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 4 Argon · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Command A Plus vs Gemini 4 Argon: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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