Command R+ vs Jamba 1.5 Large
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command R+ dari Cohere dan Jamba 1.5 Large dari AI21 adalah dua model AI unggulan. Command R+ menawarkan jendela konteks 128K token dengan harga $2.50/$10.00 per 1 juta token, sedangkan Jamba 1.5 Large menyediakan konteks 256K token seharga $2.00/$8.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
Ganti & Bandingkan Model Apa Saja
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Command R+
Model Dasar Bobot TerbukaCommand R+ dari Cohere adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 65 token/detik, indeks kecerdasan 48/100, serta tarif $2.5/$10
$2.50 / 1M
$10.00 / 1M
128K tokens
41.5%
Jamba 1.5 Large
Model Dasar Bobot TerbukaJamba 1.5 Large dari AI21 adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 256K, kecepatan 82 token/detik, indeks kecerdasan 42/100, serta tarif $2/$8
$2.00 / 1M
$8.00 / 1M
256K tokens
41.2%
Perbedaan Utama Sekilas
- Jamba 1.5 Large is 1.3x cheaper for API usage (input + output tokens combined).
- Jamba 1.5 Large has a larger context window (256K vs 128K tokens).
- Jamba 1.5 Large is faster at 82 tok/s vs 65 tok/s.
- Command R+ leads on SWE-bench coding (41.5% vs 41.2%).
| Feature | Command R+ | Jamba 1.5 Large |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 42 |
Provider | Cohere | AI21 |
Input Price Cost per million input tokens | $2.50 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $8.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.02 / 1M | $1.55 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 256K tokens~384 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 65 tok/s | 82 tok/s |
SWE-bench Verified Real-world software engineering | 41.5% | 41.2% |
GPQA Diamond Graduate-level reasoning | 56.5% | 51% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $0.50 / 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 R+ · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 41.5% · GPQA Diamond 56.5%)
- Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
- Bobot terbuka (104B) 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
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Jamba 1.5 Large · Strengths & Key Advantages
- Jendela konteks 256K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 41.2% · GPQA Diamond 51%)
- Kecepatan generasi tinggi 82 token/detik dengan latensi token pertama (TTFT) hanya 490 ms
- Bobot terbuka (398B (94B )) 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
- 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 R+ | Jamba 1.5 Large |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $0.48 |
Output Tokens / Task Tokens generated per task | 35K | 25K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Command R+ | Jamba 1.5 Large |
|---|---|---|
| Official API Model ID (SDK) | command-r-plus | jamba-1-5-large |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-08 | 2024-08 |
| Knowledge Cutoff | June 2024 | March 2024 |
| Architecture | Enterprise-Grade Dense Transformer | Hybrid SSM-Transformer (Mamba + Transformer MoE) |
| Parameters | 104B | 398B (94B active) |
| 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 ($2.5/1M in) across full 128K window | Flat rate ($2/1M in) across full 256K window |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Cached input at $0.5/1M 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) · 64 GB VRAM (4-bit Quantized) · 224 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 247 GB VRAM (4-bit Quantized) · 856 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 R+ vs Jamba 1.5 Large: Jawaban Cepat (FAQ)
Which is more intelligent, Command R+ or Jamba 1.5 Large?
Command R+ is more intelligent. It scores 48 compared with 42 on the composite Intelligence Index.
Which model is faster?
Jamba 1.5 Large is faster, generating 82 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Jamba 1.5 Large is cheaper at $1.55 per 1M tokens blended, compared with $2.02 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Jamba 1.5 Large has lower latency, with a time to first token of 0.49s compared with 0.58s.
Which model has a larger context window?
Jamba 1.5 Large has a larger context window, supporting 256K tokens compared with 128K tokens.
Kesimpulan
Baik Command R+ (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Jamba 1.5 Large (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 R+ unggul dalam penalaran dan pemrograman, Jamba 1.5 Large memberikan kecepatan generasi token lebih tinggi, dan Jamba 1.5 Large lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Command R+ atau Jamba 1.5 Large?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Command R+ memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Command R+ vs Jamba 1.5 Large?+
Command R+ mengenakan biaya $2.50 / $10.00 per 1M token input/output, sedangkan Jamba 1.5 Large sebesar $2.00 / $8.00. Secara keseluruhan, Jamba 1.5 Large lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Command R+ mendukung konteks 128K token dibandingkan 256K token pada Jamba 1.5 Large, sementara Jamba 1.5 Large 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 Expanse 8B vs Command R+: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Command R+ vs Command R: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Jamba 1.5 Large vs Jamba 1.5 Mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-3.0 vs Aion-Mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-3.0 vs Gemini 3.6 Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-Mini vs Laguna XS 2.1: 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 & AI21 serta evaluasi benchmark independen (2026).