Command R+ vs Jamba 1.5 Large
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command R+ by Cohere and Jamba 1.5 Large by AI21 represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Jamba 1.5 Large holds a notable pricing advantage for token ingestion. On output generation, Jamba 1.5 Large is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Jamba 1.5 Large provides an expanded memory horizon with 256K tokens. In latency-sensitive production environments, Jamba 1.5 Large leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
任意のAIモデルを切り替えて比較
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Command R+
オープンウェイトモデルCohere が提供する Command R+ は、最大 128K トークンのコンテキストウィンドウを備え、入力 $2.5/100万トークン・出力 $10/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.50 / 1M
$10.00 / 1M
128K tokens
41.5%
Jamba 1.5 Large
オープンウェイトモデルAI21 が提供する Jamba 1.5 Large は、最大 256K トークンのコンテキストウィンドウを備え、入力 $2/100万トークン・出力 $8/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.00 / 1M
$8.00 / 1M
256K tokens
41.2%
ひと目でわかる主な違い
- 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 |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command R+ · Strengths & Key Advantages
- Cohere の高度な Enterprise-Grade Dense Transformer アーキテクチャと 128K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2.5/1M、出力 $10/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Jamba 1.5 Large · Strengths & Key Advantages
- AI21 の高度な Hybrid SSM-Transformer (Mamba + Transformer MoE) アーキテクチャと 256K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2/1M、出力 $8/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
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 |
技術仕様・スペック
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: よくある質問 (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.
総合評価
Verdict & Model Selection Recommendation: Choose Command R+ if your priority is Enterprise knowledge hubs, mission-critical legal document search, and corporate workflows. Its performance in demanding tasks justifies its operational token cost. Choose Jamba 1.5 Large if your workflow prioritizes Enterprise long-context legal analytics, financial filings, and private Mamba hosting. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Command R+ compare to Jamba 1.5 Large in API token pricing and cost per token?+
Command R+ costs $2.50 per 1M input tokens and $10.00 per 1M output tokens, whereas Jamba 1.5 Large costs $2.00 input and $8.00 output. Overall, Jamba 1.5 Large is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Command R+ or Jamba 1.5 Large?+
Command R+ supports a context window of 128K tokens (~96k words), while Jamba 1.5 Large supports 256K tokens (~192k words). Therefore, Jamba 1.5 Large can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Command R+ or Jamba 1.5 Large?+
Command R+ scores 41.5% on SWE-bench Verified while Jamba 1.5 Large scores 41.2%. For autonomous software engineering and multi-file debugging, Command R+ holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Command R+ and Jamba 1.5 Large?+
Command R+ is optimized for low-latency direct generation. In contrast, Jamba 1.5 Large focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command R+ and Jamba 1.5 Large?+
Command R+ generates approximately 65 tokens per second, while Jamba 1.5 Large delivers 82 tokens per second. In latency-sensitive production environments, Jamba 1.5 Large provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Command R+ and Jamba 1.5 Large to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Jamba 1.5 Large to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Command R+. This tiered routing pattern cuts overall API bills by up to 70%.
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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs