Head-to-Head Model Comparison

Command R vs Jamba 1.5 Mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

İstediğiniz Modelleri Seçin ve Karşılaştırın

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Command RProfile

Jamba 1.5 MiniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Command R

Açık Ağırlıklı Temel Model

Cohere tarafından geliştirilen Command R; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 128K bağlam penceresi, 85 token/sn hız, 43/100 zeka indeksi ve 1M token başına $0.15/$0.

Intelligence Index43 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

32%

AI21

Jamba 1.5 Mini

Ultra Hızlı Düşük Gecikme

AI21 tarafından geliştirilen Jamba 1.5 Mini; ultra düşük gecikmeli gerçek zamanlı sohbet ve yüksek hacimli yönlendirme için tasarlanmış olup 256K bağlam penceresi, 120 token/sn hız, 39/100 zeka indeksi ve 1M token başına $0.2/$0.4 fiyatland

Intelligence Index39 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.40 / 1M

Context Window:

256K tokens

SWE-bench Verified:

35.8%

Bir Bakışta Temel Farklar

Jamba 1.5 Mini is 1.1x cheaper for API usage (input + output tokens combined).
Jamba 1.5 Mini has a larger context window (256K vs 128K tokens).
Jamba 1.5 Mini is faster at 120 tok/s vs 85 tok/s.
Jamba 1.5 Mini leads on SWE-bench coding (35.8% vs 32.0%).

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

Feature	Command R	Jamba 1.5 Mini
Intelligence Index Composite reasoning & knowledge score (0-100)	43	39
Provider	Cohere	AI21
Input Price Cost per million input tokens	$0.15 / 1M	$0.20 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.40 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.13 / 1M	$0.11 / 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	4K	4K
Throughput Speed Tokens generated per second	85 tok/s	120 tok/s
SWE-bench Verified Real-world software engineering	32%	35.8%
GPQA Diamond Graduate-level reasoning	46%	44.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.05 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

Jamba 1.5 Mini

+3.8% SWE-bench lead

Cost

Jamba 1.5 Mini

1.1x cheaper monthly runtime

Speed

Jamba 1.5 Mini

+41% faster generation

Context

Jamba 1.5 Mini

256K tokens window

Doğrulanmış Benchmark Puanları

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

Command RJamba 1.5 Mini

SWE-bench Verified (Autonomous Coding)

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

32%vs35.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

46%vs44.2%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs64%

LMSYS Chatbot Arena Elo

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

1240ptsvs1242pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs4.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

28%vs21.5%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs22.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs37.5%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

52%vs45%

Long-Context Reasoning

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

75%vs72%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs120 tok/s

Gecikme Süresi ve Yanıt Hızı

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 R0.42s

Jamba 1.5 Mini0.34s

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

Command R6.30s

Jamba 1.5 Mini4.51s

Gerçek Zamanlı Token Maliyet Simülatörü

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 R Monthly API Bill

$0.84 / month

$0.15/1M in · $0.6/1M out

Jamba 1.5 Mini Monthly API Bill

$0.74 / month

$0.2/1M in · $0.4/1M out

Jamba 1.5 Mini saves approximately $0.10 / month (12%) under this workload.1.1x Savings

Command R · Strengths & Key Advantages

128K bağlam penceresi ve yanıt başına maksimum 4K çıktı tokenı kapasitesi (SWE-bench Verified 32% · GPQA Diamond 46%)
Sadece 420 ms ilk token gecikmesi (TTFT) ile saniyede 85 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (35B)

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Jamba 1.5 Mini · Strengths & Key Advantages

256K bağlam penceresi ve yanıt başına maksimum 4K çıktı tokenı kapasitesi (SWE-bench Verified 35.8% · GPQA Diamond 44.2%)
Sadece 340 ms ilk token gecikmesi (TTFT) ile saniyede 120 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (52B (12B ))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

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

Metric	Command R	Jamba 1.5 Mini
Cost per Task Estimated USD to finish one task	$0.12	$0.12
Output Tokens / Task Tokens generated per task	25K	20K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	Command R	Jamba 1.5 Mini
Official API Model ID (SDK)	command-r	jamba-1-5-mini
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	Retrieval-Augmented Dense Transformer	Hybrid SSM-Transformer (Mamba + Transformer MoE)
Parameters	35B	52B (12B 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 ($0.15/1M in) across full 128K window	Flat rate ($0.2/1M in) across full 256K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Cached input at $0.05/1M 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) · 22 GB VRAM (4-bit Quantized) · 75 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 32 GB VRAM (4-bit Quantized) · 112 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 Mini: Hızlı Yanıtlar (SSS)

Which is more intelligent, Command R or Jamba 1.5 Mini?

Command R is more intelligent. It scores 43 compared with 39 on the composite Intelligence Index.

Which model is faster?

Jamba 1.5 Mini is faster, generating 120 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Jamba 1.5 Mini is cheaper at $0.11 per 1M tokens blended, compared with $0.13 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Jamba 1.5 Mini has lower latency, with a time to first token of 0.34s compared with 0.42s.

Which model has a larger context window?

Jamba 1.5 Mini has a larger context window, supporting 256K tokens compared with 128K tokens.

Sonuç

Hem Command R (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Jamba 1.5 Mini (ultra düşük gecikmeli gerçek zamanlı sohbet ve yüksek hacimli yönlendirme, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Command R karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Jamba 1.5 Mini daha yüksek üretim hızı sağlar; Jamba 1.5 Mini ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Command R mi yoksa Jamba 1.5 Mini mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Command R önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Command R ve Jamba 1.5 Mini API fiyatları nasıl karşılaştırılır?+

Command R 1M giriş/çıkış tokeni başına $0.15 / $0.60 ücretlendirilirken, Jamba 1.5 Mini $0.20 / $0.40 seviyesindedir. Karma maliyette Jamba 1.5 Mini daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Command R 128K token bağlam penceresi sunarken Jamba 1.5 Mini 256K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Jamba 1.5 Mini öndedir.

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 R vs Jamba 1.5 Mini

İstediğiniz Modelleri Seçin ve Karşılaştırın

Command R

Jamba 1.5 Mini

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Command R · Strengths & Key Advantages

Considerations & Trade-offs

Jamba 1.5 Mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Command R vs Jamba 1.5 Mini: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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