Jamba 1.5 Large vs Jamba 1.5 Mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Jamba 1.5 Large da AI21 e Jamba 1.5 Mini da AI21 são dois modelos de IA de destaque. O Jamba 1.5 Large oferece uma janela de contexto de 256K tokens a $2.00/$8.00 por milhão de tokens, enquanto o Jamba 1.5 Mini oferece 256K tokens de contexto a $0.20/$0.40 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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Jamba 1.5 Large
Modelo Base de Pesos AbertosO Jamba 1.5 Large da AI21 é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 256K, velocidade de 82 tokens/s, índice de inteligência de 42/100 e tar
$2.00 / 1M
$8.00 / 1M
256K tokens
41.2%
Jamba 1.5 Mini
Inferência UltrarrápidaO Jamba 1.5 Mini da AI21 é um modelo de IA projetado para respostas em tempo real com latência ultrabaixa e roteamento de alto volume, oferecendo janela de contexto de 256K, velocidade de 120 tokens/s, índice de inteligência de 39/100 e tar
$0.20 / 1M
$0.40 / 1M
256K tokens
35.8%
Principais Diferenças em Resumo
- Jamba 1.5 Mini is 14.9x cheaper for API usage (input + output tokens combined).
- Jamba 1.5 Mini is faster at 120 tok/s vs 82 tok/s.
- Jamba 1.5 Large leads on SWE-bench coding (41.2% vs 35.8%).
| Feature | Jamba 1.5 Large | Jamba 1.5 Mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 39 |
Provider | AI21 | AI21 |
Input Price Cost per million input tokens | $2.00 / 1M | $0.20 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $0.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $0.11 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 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 | 82 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 41.2% | 35.8% |
GPQA Diamond Graduate-level reasoning | 51% | 44.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.05 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Jamba 1.5 Large · Strengths & Key Advantages
- Janela de contexto de 256K com limite máximo de saída de 4K tokens (SWE-bench Verified 41.2% · GPQA Diamond 51%)
- Taxa de transferência rápida de 82 tokens/segundo com latência inicial (TTFT) de 490 ms
- Pesos abertos (398B (94B )) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Jamba 1.5 Mini · Strengths & Key Advantages
- Janela de contexto de 256K com limite máximo de saída de 4K tokens (SWE-bench Verified 35.8% · GPQA Diamond 44.2%)
- Taxa de transferência rápida de 120 tokens/segundo com latência inicial (TTFT) de 340 ms
- Pesos abertos (52B (12B )) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Jamba 1.5 Large | Jamba 1.5 Mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.48 | $0.12 |
Output Tokens / Task Tokens generated per task | 25K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Jamba 1.5 Large | Jamba 1.5 Mini |
|---|---|---|
| Official API Model ID (SDK) | jamba-1-5-large | 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 | March 2024 | March 2024 |
| Architecture | Hybrid SSM-Transformer (Mamba + Transformer MoE) | Hybrid SSM-Transformer (Mamba + Transformer MoE) |
| Parameters | 398B (94B active) | 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 ($2/1M in) across full 256K window | Flat rate ($0.2/1M in) across full 256K window |
| Prompt Cache Policy & TTL | Cached input at $0.5/1M 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) · 247 GB VRAM (4-bit Quantized) · 856 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 |
Jamba 1.5 Large vs Jamba 1.5 Mini: Respostas Rápidas (FAQ)
Which is more intelligent, Jamba 1.5 Large or Jamba 1.5 Mini?
Jamba 1.5 Large is more intelligent. It scores 42 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 82 tokens per second.
Which model is cheaper to run?
Jamba 1.5 Mini is cheaper at $0.11 per 1M tokens blended, compared with $1.55 (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 0.49s.
Veredito
Tanto Jamba 1.5 Large (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Jamba 1.5 Mini (Indicado para respostas em tempo real com latência ultrabaixa e roteamento de alto volume, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Jamba 1.5 Large lidera em inteligência geral e programação, Jamba 1.5 Mini oferece geração mais rápida e Jamba 1.5 Mini apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Jamba 1.5 Large ou Jamba 1.5 Mini?+
Nas avaliações de inteligência e código, Jamba 1.5 Large leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Jamba 1.5 Large ou Jamba 1.5 Mini?+
O Jamba 1.5 Large custa $2.00 / $8.00 por 1M de tokens (entrada/saída) e o Jamba 1.5 Mini custa $0.20 / $0.40. No custo combinado, Jamba 1.5 Mini é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Jamba 1.5 Large suporta 256K tokens de contexto contra 256K tokens do Jamba 1.5 Mini, enquanto Jamba 1.5 Mini entrega maior velocidade de geração.
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.
Fontes oficiais: Documentação oficial de API da AI21 e AI21 e benchmarks independentes (2026).