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Head-to-Head Model Comparison

Command A Plus vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command A Plus vs Claude Opus 5.5: Benchmarks, Pricing & Speed Comparison

Command A Plus von Cohere und Claude Opus 5.5 von Anthropic gehören zu den führenden KI-Modellen. Command A Plus bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $3.00/$12.00 pro 1M Tokens, während Claude Opus 5.5 1M Tokens Kontext zu $4.00/$20.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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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%

Anthropic

Claude Opus 5.5

Frontier-Flaggschiff-Modell

Claude Opus 5.5 von Anthropic ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 78 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und $4/$20 pro 1M Toke

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Wichtigste Unterschiede auf einen Blick

  • Command A Plus is 1.5x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
  • Claude Opus 5.5 is faster at 78 tok/s vs 68 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 60.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand A PlusClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5894
Provider
CohereAnthropic
Input Price
Cost per million input tokens
$3.00 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$12.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.33 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K128K
Throughput Speed
Tokens generated per second
68 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
60.5%84.6%
GPQA Diamond
Graduate-level reasoning
74%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.40 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Claude Opus 5.5
+24.1% SWE-bench lead
Cost
Command A Plus
1.5x cheaper monthly runtime
Speed
Claude Opus 5.5
+15% faster generation
Context
Claude Opus 5.5
1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Command A PlusClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

60.5%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

74%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

86.8%vs91.5%
LMSYS Chatbot Arena Elo

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

1410ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

50%vs66.4%
SciCode (Scientific Computing)

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

N/Avs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs83.1%
AIME (Advanced Math Competition)

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

82.5%vs95.4%
Long-Context Reasoning

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

88%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs78 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
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command A Plus7.91s
Claude Opus 5.57.81s

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 Plus Monthly API Bill
$16.43 / month
$3/1M in · $12/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Command A Plus saves approximately $8.18 / month (33%) under this workload.1.5x 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

Claude Opus 5.5 · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
  • Hohe Generierungsgeschwindigkeit von 78 Token/Sekunde bei nur 1400 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($4/$20 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 PlusClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.85$3.85
Output Tokens / Task
Tokens generated per task
45K68K
Reasoning Tokens / Task
Thinking tokens burned per task
22K42K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureCommand A PlusClaude Opus 5.5
Official API Model ID (SDK)command-a-plusclaude-opus-5-5-20260901
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-052026-09
Knowledge CutoffMarch 2026June 2026
ArchitectureFlagship Multimodal Agentic MoEExtended Autonomous Hybrid Reasoning Transformer
ParametersConfidential (~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 Cloud4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($3/1M in) across full 128K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.75/1M tokens$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
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 Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)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) · 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 ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
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 Modalitiestext, visiontext, vision
Output Modalitiestexttext

Command A Plus vs Claude Opus 5.5: Schnelle Antworten (FAQ)

Which is more intelligent, Command A Plus or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 58 on the composite Intelligence Index.

Which model is faster?

Claude Opus 5.5 is faster, generating 78 tokens per second compared with 68 tokens per second.

Which model is cheaper to run?

Command A Plus is cheaper at $2.33 per 1M tokens blended, compared with $3.08 (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 1.40s.

Which model has a larger context window?

Claude Opus 5.5 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 Claude Opus 5.5 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Claude Opus 5.5 führt bei komplexem Reasoning und Coding, Claude Opus 5.5 liefert den höheren Token-Durchsatz und Command A Plus ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Command A Plus oder Claude Opus 5.5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Claude Opus 5.5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Command A Plus und Claude Opus 5.5?+

Command A Plus kostet $3.00 / $12.00 pro 1M Input-/Output-Tokens, Claude Opus 5.5 liegt bei $4.00 / $20.00. Beim gewichteten Gesamtpreis ist Command A Plus günstiger.

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

Command A Plus bietet 128K Tokens Kontext gegenüber 1M Tokens bei Claude Opus 5.5. Bei der Generierungsgeschwindigkeit liegt Claude Opus 5.5 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 Anthropic, Systemkarten und unabhängige Benchmark-Auswertungen (2026).