Head-to-Head Model Comparison

Command R7B vs Aya Expanse 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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

Command R7BProfile

Aya Expanse 32BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command R7B

Model Dasar Bobot Terbuka

Command R7B dari Cohere adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 140 token/detik, indeks kecerdasan 38/100, serta tarif $0.07/$

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

Model Multibahasa Global

Aya Expanse 32B dari Cohere adalah model AI yang dirancang untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, menghadirkan jendela konteks 128K, kecepatan 85 token/detik, indeks kecerdasan 44/100, serta tarif $0.25

Intelligence Index44 / 100

Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

33.5%

Perbedaan Utama Sekilas

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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Jendela konteks 128K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 27% · GPQA Diamond 42.5%)
Kecepatan generasi tinggi 140 token/detik dengan latensi token pertama (TTFT) hanya 290 ms
Bobot terbuka (7B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Aya Expanse 32B · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 33.5% · GPQA Diamond 50%)
Kecepatan generasi tinggi 85 token/detik dengan latensi token pertama (TTFT) hanya 440 ms
Bobot terbuka (32B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Command R7B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Aya Expanse 32B (Sangat cocok untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Aya Expanse 32B unggul dalam penalaran dan pemrograman, Command R7B memberikan kecepatan generasi token lebih tinggi, dan Command R7B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Command R7B atau Aya Expanse 32B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Aya Expanse 32B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Command R7B vs Aya Expanse 32B?+

Command R7B mengenakan biaya $0.07 / $0.20 per 1M token input/output, sedangkan Aya Expanse 32B sebesar $0.25 / $0.50. Secara keseluruhan, Command R7B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Command R7B mendukung konteks 128K token dibandingkan 128K token pada Aya Expanse 32B, sementara Command R7B unggul dalam kecepatan output token per detik.

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

Ganti & Bandingkan Model Apa Saja

Command R7B

Aya Expanse 32B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Command R7B · Strengths & Key Advantages

Considerations & Trade-offs

Aya Expanse 32B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Command R7B vs Aya Expanse 32B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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