Aya Vision 8B vs Command R7B Arabic
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Aya Vision 8B dari Cohere dan Command R7B Arabic dari Cohere adalah dua model AI unggulan. Aya Vision 8B menawarkan jendela konteks 16K token dengan harga $0.10/$0.25 per 1 juta token, sedangkan Command R7B Arabic menyediakan konteks 128K token seharga $0.07/$0.20 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Aya Vision 8B
Model Multibahasa GlobalAya Vision 8B dari Cohere adalah model AI yang dirancang untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, menghadirkan jendela konteks 16K, kecepatan 110 token/detik, indeks kecerdasan 38/100, serta tarif $0.1/$0
$0.10 / 1M
$0.25 / 1M
16K tokens
22%
Command R7B Arabic
Model Dasar Bobot TerbukaCommand R7B Arabic dari Cohere adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 135 token/detik, indeks kecerdasan 39/100, serta tarif
$0.07 / 1M
$0.20 / 1M
128K tokens
26%
Perbedaan Utama Sekilas
- Command R7B Arabic is 1.3x cheaper for API usage (input + output tokens combined).
- Command R7B Arabic has a larger context window (128K vs 16K tokens).
- Command R7B Arabic is faster at 135 tok/s vs 110 tok/s.
- Command R7B Arabic leads on SWE-bench coding (26.0% vs 22.0%).
| Feature | Aya Vision 8B | Command R7B Arabic |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 39 |
Provider | Cohere | Cohere |
Input Price Cost per million input tokens | $0.10 / 1M | $0.07 / 1M |
Output Price Cost per million output tokens | $0.25 / 1M | $0.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 16K tokens~24 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 110 tok/s | 135 tok/s |
SWE-bench Verified Real-world software engineering | 22% | 26% |
GPQA Diamond Graduate-level reasoning | 41% | 43% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.02 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Aya Vision 8B · Strengths & Key Advantages
- Jendela konteks 16K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 22% · GPQA Diamond 41%)
- Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 350 ms
- Bobot terbuka (8B) 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
Command R7B Arabic · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 26% · GPQA Diamond 43%)
- Kecepatan generasi tinggi 135 token/detik dengan latensi token pertama (TTFT) hanya 310 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Aya Vision 8B | Command R7B Arabic |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $0.05 |
Output Tokens / Task Tokens generated per task | 18K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Aya Vision 8B | Command R7B Arabic |
|---|---|---|
| Official API Model ID (SDK) | aya-vision-8b | command-r7b-arabic |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-03 | 2025-02 |
| Knowledge Cutoff | January 2025 | December 2024 |
| Architecture | Multilingual Vision-Language Transformer | Arabic-Specialized RAG Transformer |
| Parameters | 8B | 7B |
| 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 ($0.1/1M in) across full 16K window | Flat rate ($0.07/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Cached input at $0.02/1M 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 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, vision | text |
| Output Modalities | text | text |
Aya Vision 8B vs Command R7B Arabic: Jawaban Cepat (FAQ)
Which is more intelligent, Aya Vision 8B or Command R7B Arabic?
Command R7B Arabic is more intelligent. It scores 39 compared with 38 on the composite Intelligence Index.
Which model is faster?
Command R7B Arabic is faster, generating 135 tokens per second compared with 110 tokens per second.
Which model is cheaper to run?
Command R7B Arabic is cheaper at $0.05 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command R7B Arabic has lower latency, with a time to first token of 0.31s compared with 0.35s.
Which model has a larger context window?
Command R7B Arabic has a larger context window, supporting 128K tokens compared with 16K tokens.
Kesimpulan
Baik Aya Vision 8B (Sangat cocok untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Command R7B Arabic (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Command R7B Arabic unggul dalam penalaran dan pemrograman, Command R7B Arabic memberikan kecepatan generasi token lebih tinggi, dan Command R7B Arabic lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Aya Vision 8B atau Command R7B Arabic?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Command R7B Arabic memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Aya Vision 8B vs Command R7B Arabic?+
Aya Vision 8B mengenakan biaya $0.10 / $0.25 per 1M token input/output, sedangkan Command R7B Arabic sebesar $0.07 / $0.20. Secara keseluruhan, Command R7B Arabic lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Aya Vision 8B mendukung konteks 16K token dibandingkan 128K token pada Command R7B Arabic, sementara Command R7B Arabic unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Aya Vision 32B vs Aya Vision 8B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aya Vision 8B vs Mistral Small 3: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Command R7B Arabic vs Command R7B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Command R7B Arabic vs Mistral Small 3: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Phi-4 Multimodal vs Command R7B Arabic: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-3.0 vs Aion-Mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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.
Sumber resmi: Dokumentasi API resmi Cohere & Cohere serta evaluasi benchmark independen (2026).