Command R7B Arabic vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Command R7B Arabic by Cohere against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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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%
GPT-6 Astra
Model Suara & Audio AsliGPT-6 Astra dari OpenAI adalah model AI yang dirancang untuk sintesis suara alami, pemrosesan audio real-time, dan transkripsi ucapan, menghadirkan jendela konteks 272K, kecepatan 100 token/detik, indeks kecerdasan 96/100, serta tarif $10/$
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Perbedaan Utama Sekilas
- Command R7B Arabic is 195.2x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 128K tokens).
- Command R7B Arabic is faster at 135 tok/s vs 100 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 26.0%).
| Feature | Command R7B Arabic | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 96 |
Provider | Cohere | OpenAI |
Input Price Cost per million input tokens | $0.07 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 135 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 26% | 82.4% |
GPQA Diamond Graduate-level reasoning | 43% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $1.00 / 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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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.
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
GPT-6 Astra · Strengths & Key Advantages
- Jendela konteks 272K dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Kecepatan generasi tinggi 100 token/detik dengan latensi token pertama (TTFT) hanya 950 ms
- Skema harga API kompetitif (tarif $10/$50 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Command R7B Arabic | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.15 |
Output Tokens / Task Tokens generated per task | 20K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Command R7B Arabic | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | command-r7b-arabic | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-02 | 2026-09 |
| Knowledge Cutoff | December 2024 | June 2026 |
| Architecture | Arabic-Specialized RAG Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 7B | 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.07/1M in) across full 128K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| 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) | 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) | OpenAI Preparedness Framework Audited & System Card Published |
| 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 | text, vision, audio |
| Output Modalities | text | text, audio |
Command R7B Arabic vs GPT-6 Astra: Jawaban Cepat (FAQ)
Which is more intelligent, Command R7B Arabic or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 39 on the composite Intelligence Index.
Which model is faster?
Command R7B Arabic is faster, generating 135 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Command R7B Arabic is cheaper at $0.05 per 1M tokens blended, compared with $7.70 (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 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 128K tokens.
Kesimpulan
Choose Command R7B Arabic for sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem rag perusahaan, dan integrasi api skala produksi., or choose GPT-6 Astra for sangat cocok untuk sintesis suara alami, pemrosesan audio real-time, dan transkripsi ucapan, sistem rag perusahaan, dan integrasi api skala produksi..
Pertanyaan Umum (FAQ)
Which is cheaper: Command R7B Arabic or GPT-6 Astra?+
Command R7B Arabic costs $0.07/1M input and $0.2/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Command R7B Arabic or GPT-6 Astra?+
Command R7B Arabic supports 128K tokens whereas GPT-6 Astra supports 272K tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: Cohere & OpenAI Official Documentation