Head-to-Head Model Comparison

Command R7B vs Aya Expanse 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Command R7BProfile

Aya Expanse 32BProfile

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Popular Matchups:

Cohere

Command R7B

Open-Weights-Basismodell

Command R7B von Cohere ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 140 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und $0.07/$0.2 pro 1M Token.

Intelligence Index38 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.20 / 1M

Context Window:

128K tokens

SWE-bench Verified:

27%

Cohere

Aya Expanse 32B

Mehrsprachiges KI-Modell

Aya Expanse 32B von Cohere ist ein KI-Modell für präzise mehrsprachige Übersetzung, globale Kommunikation und sprachübergreifende Analysen mit 128K Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 44/100 und $0.25/$0.5 pro

Intelligence Index44 / 100

Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

33.5%

Wichtigste Unterschiede auf einen Blick

Command R7B is 2.9x cheaper for API usage (input + output tokens combined).
Command R7B is faster at 140 tok/s vs 85 tok/s.
Aya Expanse 32B leads on SWE-bench coding (33.5% vs 27.0%).

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

Feature	Command R7B	Aya Expanse 32B
Intelligence Index Composite reasoning & knowledge score (0-100)	38	44
Provider	Cohere	Cohere
Input Price Cost per million input tokens	$0.07 / 1M	$0.25 / 1M
Output Price Cost per million output tokens	$0.20 / 1M	$0.50 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.05 / 1M	$0.14 / 1M
Context Window Maximum input + output tokens	128K tokens~192 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	140 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	27%	33.5%
GPQA Diamond Graduate-level reasoning	42.5%	50%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.06 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Aya Expanse 32B

+6.5% SWE-bench lead

Cost

Command R7B

2.9x cheaper monthly runtime

Speed

Command R7B

+65% faster generation

Context

Tied

128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Command R7BAya Expanse 32B

SWE-bench Verified (Autonomous Coding)

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

27%vs33.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

42.5%vs50%

MMLU-Pro (Multi-task Language Understanding)

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

66%vs73%

LMSYS Chatbot Arena Elo

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

1215ptsvs1265pts

Long-Context Reasoning

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

68%vs72%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs85 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 R7B0.29s

Aya Expanse 32B0.44s

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

Command R7B3.86s

Aya Expanse 32B6.32s

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 R7B Monthly API Bill

$0.32 / month

$0.07/1M in · $0.2/1M out

Aya Expanse 32B Monthly API Bill

$0.92 / month

$0.25/1M in · $0.5/1M out

Command R7B saves approximately $0.60 / month (66%) under this workload.2.9x Savings

Command R7B · Strengths & Key Advantages

128K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 27% · GPQA Diamond 42.5%)
Hohe Generierungsgeschwindigkeit von 140 Token/Sekunde bei nur 290 ms Erstlatenz (TTFT)
Offene Modellgewichte (7B) 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 extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Aya Expanse 32B · Strengths & Key Advantages

128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 33.5% · GPQA Diamond 50%)
Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 440 ms Erstlatenz (TTFT)
Offene Modellgewichte (32B) 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 extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

Metric	Command R7B	Aya Expanse 32B
Cost per Task Estimated USD to finish one task	$0.05	$0.18
Output Tokens / Task Tokens generated per task	20K	28K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Command R7B	Aya Expanse 32B
Official API Model ID (SDK)	command-r7b	aya-expanse-32b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-12	2024-10
Knowledge Cutoff	October 2024	August 2024
Architecture	Efficient Compact RAG Transformer	High-Capacity Multilingual Transformer
Parameters	7B	32B
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 ($0.07/1M in) across full 128K window	Flat rate ($0.25/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Cached input at $0.06/1M 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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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
Output Modalities	text	text

Command R7B vs Aya Expanse 32B: Schnelle Antworten (FAQ)

Which is more intelligent, Command R7B or Aya Expanse 32B?

Aya Expanse 32B is more intelligent. It scores 44 compared with 38 on the composite Intelligence Index.

Which model is faster?

Command R7B is faster, generating 140 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Command R7B is cheaper at $0.05 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Command R7B has lower latency, with a time to first token of 0.29s compared with 0.44s.

Fazit

Sowohl Command R7B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Aya Expanse 32B (Optimal für präzise mehrsprachige Übersetzung, globale Kommunikation und sprachübergreifende Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Aya Expanse 32B führt bei komplexem Reasoning und Coding, Command R7B liefert den höheren Token-Durchsatz und Command R7B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Command R7B oder Aya Expanse 32B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Aya Expanse 32B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Command R7B und Aya Expanse 32B?+

Command R7B kostet $0.07 / $0.20 pro 1M Input-/Output-Tokens, Aya Expanse 32B liegt bei $0.25 / $0.50. Beim gewichteten Gesamtpreis ist Command R7B günstiger.

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

Command R7B bietet 128K Tokens Kontext gegenüber 128K Tokens bei Aya Expanse 32B. Bei der Generierungsgeschwindigkeit liegt Command R7B 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 R7B vs Aya Expanse 32B

Beliebige Modelle wechseln & vergleichen

Command R7B

Aya Expanse 32B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Command R7B · Strengths & Key Advantages

Considerations & Trade-offs

Aya Expanse 32B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Command R7B vs Aya Expanse 32B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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