Command R vs Jamba 1.5 Mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command R by Cohere and Jamba 1.5 Mini 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, Command R holds a notable pricing advantage for token ingestion. On output generation, Jamba 1.5 Mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Jamba 1.5 Mini provides an expanded memory horizon with 256K tokens. In latency-sensitive production environments, Jamba 1.5 Mini 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.
Changez et Comparez les Modèles
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Command R
Bourreau de travail RAG35-milliards paramètre d'entreprise bourreau de travail optimisé pour à long contexte recherche, élevé-précision RAG, et automatisé citations ancrage.
$0.15 / 1M
$0.60 / 1M
128K tokens
32%
Jamba 1.5 Mini
Modèle open-sourceModèle Jamba 1.5 Mini développé par AI21, offrant une inférence rapide et une grande fiabilité pour les flux professionnels.
$0.20 / 1M
$0.40 / 1M
256K tokens
35.8%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Command R · Strengths & Key Advantages
- standard de l'industrie RAG ancrage avec precise source citations génération
- Superb prix-à-performances ratio ($0.15 entrée / $0.60 sortie par millions)
- 128K fenêtre de contexte avec à poids ouverts pour privé d'entreprise auto-hébergement
Considerations & Trade-offs
- inférieur performances sur complexe compétitif codage que spécialisé code modèles
- texte-seulement sans natif multimodal vision support
Jamba 1.5 Mini · Strengths & Key Advantages
- Architecture haute performance optimisée par AI21
- Excellente prise en charge des sorties structurées JSON et des appels d outils
- Stabilité démontrée en environnement de production
Considerations & Trade-offs
- Nécessite une infrastructure dédiée ou des clés API adaptées pour les forts volumes
- Attention à la taille de la fenêtre de contexte pour les très grands documents
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (FAQ)
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.
Verdict
Verdict & Model Selection Recommendation: Choose Command R if your priority is Enterprise RAG search, document question answering, and customer support with citations. Its performance in demanding tasks justifies its operational token cost. Choose Jamba 1.5 Mini if your workflow prioritizes Cost-effective long-document processing, customer dialogue logging, and high-speed retrieval. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Command R compare to Jamba 1.5 Mini in API token pricing and cost per token?+
Command R costs $0.15 per 1M input tokens and $0.60 per 1M output tokens, whereas Jamba 1.5 Mini costs $0.20 input and $0.40 output. Overall, Command R 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 Mini?+
Command R supports a context window of 128K tokens (~96k words), while Jamba 1.5 Mini supports 256K tokens (~192k words). Therefore, Jamba 1.5 Mini 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 Mini?+
Command R scores 32% on SWE-bench Verified while Jamba 1.5 Mini scores 35.8%. For autonomous software engineering and multi-file debugging, Jamba 1.5 Mini holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Command R and Jamba 1.5 Mini?+
Command R is optimized for low-latency direct generation. In contrast, Jamba 1.5 Mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between Command R and Jamba 1.5 Mini?+
Command R generates approximately 85 tokens per second, while Jamba 1.5 Mini delivers 120 tokens per second. In latency-sensitive production environments, Jamba 1.5 Mini 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 Mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Command R to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Jamba 1.5 Mini. 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs