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Head-to-Head Model Comparison

Command R+ vs Jamba 1.5 Large

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Command R+ vs Jamba 1.5 Large: Benchmarks, Pricing & Speed Comparison

Cohere의 Command R+와 AI21의 Jamba 1.5 Large는 현재 가장 주목받는 AI 모델입니다. Command R+는 128K 토큰 컨텍스트 윈도우와 100만 토큰당 $2.50/$10.00(입력/출력)의 요금을 제공하며, Jamba 1.5 Large는 256K 토큰 컨텍스트와 $2.00/$8.00의 요금을 제공합니다. 아래에서 벤치마크, 지연 시간 및 비용을 상세히 비교해 보세요.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

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Cohere

Command R+

오픈 웨이트 파운데이션 모델

Cohere의 Command R+ 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 65 토큰의 생성 속도, 종합 지능 지수 48/100점(1M 토큰당 입력 $2.5 / 출력 $10)을 제공합니다.

Intelligence Index48 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

41.5%

AI21

Jamba 1.5 Large

오픈 웨이트 파운데이션 모델

AI21의 Jamba 1.5 Large 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 256K 컨텍스트 윈도우, 초당 82 토큰의 생성 속도, 종합 지능 지수 42/100점(1M 토큰당 입력 $2 / 출력 $8)을 제공합니다.

Intelligence Index42 / 100
Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

41.2%

한눈에 보는 핵심 차이점

  • 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%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureCommand R+Jamba 1.5 Large
Intelligence Index
Composite reasoning & knowledge score (0-100)
4842
Provider
CohereAI21
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 text256K tokens~384 A4 pages of text
Max Output Tokens
Per single request
8K4K
Throughput Speed
Tokens generated per second
65 tok/s82 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

부문별 우위 모델

At-a-glance decision framework
Coding
Command R+
+0.3% SWE-bench lead
Cost
Jamba 1.5 Large
1.3x cheaper monthly runtime
Speed
Jamba 1.5 Large
+26% faster generation
Context
Jamba 1.5 Large
256K tokens window

검증된 벤치마크 점수 비교

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

Command R+Jamba 1.5 Large
SWE-bench Verified (Autonomous Coding)

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

41.5%vs41.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs51%
MMLU-Pro (Multi-task Language Understanding)

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

78.5%vs70.2%
LMSYS Chatbot Arena Elo

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

1300ptsvs1275pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs6.9%
Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vs26.5%
SciCode (Scientific Computing)

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

N/Avs27.2%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs45%
AIME (Advanced Math Competition)

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

66%vs54%
Long-Context Reasoning

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

80%vs75%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs82 tok/s

지연 시간(Latency) 및 응답 속도

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 R+0.58s
Jamba 1.5 Large0.49s
Est. End-to-End Response (500 tokens)TTFT + generation time
Command R+8.27s
Jamba 1.5 Large6.59s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Command R+ Monthly API Bill
$13.88 / month
$2.5/1M in · $10/1M out
Jamba 1.5 Large Monthly API Bill
$10.95 / month
$2/1M in · $8/1M out
Jamba 1.5 Large saves approximately $2.93 / month (21%) under this workload.1.3x Savings

Command R+ · Strengths & Key Advantages

  • 128K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 41.5% · GPQA Diamond 56.5%)
  • 초당 65 토큰의 빠른 처리 속도와 580ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (104B)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Jamba 1.5 Large · Strengths & Key Advantages

  • 256K 컨텍스트 윈도우 및 최대 4K 출력 토큰 생성 지원 (SWE-bench Verified 41.2% · GPQA Diamond 51%)
  • 초당 82 토큰의 빠른 처리 속도와 490ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (398B (94B ))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Token Use & Task Economics

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

MetricCommand 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
35K25K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureCommand R+Jamba 1.5 Large
Official API Model ID (SDK)command-r-plusjamba-1-5-large
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-082024-08
Knowledge CutoffJune 2024March 2024
ArchitectureEnterprise-Grade Dense TransformerHybrid SSM-Transformer (Mamba + Transformer MoE)
Parameters104B398B (94B active)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 windowFlat rate ($2/1M in) across full 256K window
Prompt Cache Policy & TTLCached input at $0.75/1M tokensCached input at $0.5/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Command R+ vs Jamba 1.5 Large: 핵심 요약 문답 (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.

종합 평가

Command R+(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Jamba 1.5 Large(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Command R+가 앞서며, 토큰 생성 속도에서는 Jamba 1.5 Large가 빠르고, 대규모 API 운영 비용 면에서는 Jamba 1.5 Large가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Command R+와 Jamba 1.5 Large 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Command R+가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Command R+와 Jamba 1.5 Large의 API 요금 차이는 얼마인가요?+

Command R+는 100만 토큰당 입력 $2.50 / 출력 $10.00이며, Jamba 1.5 Large는 입력 $2.00 / 출력 $8.00입니다. 혼합 비용 기준으로는 Jamba 1.5 Large가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Command R+가 128K 토큰, Jamba 1.5 Large가 256K 토큰를 지원하며, 출력 속도 면에서는 Jamba 1.5 Large가 더 빠릅니다.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

공식 출처: Cohere 및 AI21 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).