Jamba 1.5 Large vs Jamba 1.5 Mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Jamba 1.5 Large di AI21 e Jamba 1.5 Mini di AI21 sono due modelli di IA di riferimento. Jamba 1.5 Large offre una finestra di contesto di 256K token a $2.00/$8.00 per milione di token, mentre Jamba 1.5 Mini mette a disposizione 256K token di contesto a $0.20/$0.40 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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Jamba 1.5 Large
Modello Base a Pesi ApertiJamba 1.5 Large di AI21 è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 256K, velocità di 82 token/s, indice di intelligenza di 42/100 e
$2.00 / 1M
$8.00 / 1M
256K tokens
41.2%
Jamba 1.5 Mini
Inferenza Ultra-RapidaJamba 1.5 Mini di AI21 è un modello di IA progettato per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, con finestra di contesto da 256K, velocità di 120 token/s, indice di intelligenza di 39/100 e tariffa d
$0.20 / 1M
$0.40 / 1M
256K tokens
35.8%
Differenze Chiave a Colpo d’Occhio
- 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Jamba 1.5 Large · Strengths & Key Advantages
- Finestra di contesto da 256K con capacità massima di output fino a 4K token (SWE-bench Verified 41.2% · GPQA Diamond 51%)
- Throughput elevato di 82 token/secondo con latenza al primo token (TTFT) di soli 490 ms
- Pesi aperti (398B (94B )) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
Jamba 1.5 Mini · Strengths & Key Advantages
- Finestra di contesto da 256K con capacità massima di output fino a 4K token (SWE-bench Verified 35.8% · GPQA Diamond 44.2%)
- Throughput elevato di 120 token/secondo con latenza al primo token (TTFT) di soli 340 ms
- Pesi aperti (52B (12B )) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Sia Jamba 1.5 Large (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Jamba 1.5 Mini (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Jamba 1.5 Large guida nei benchmark di ragionamento e programmazione, Jamba 1.5 Mini garantisce una generazione più rapida e Jamba 1.5 Mini offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Jamba 1.5 Large o Jamba 1.5 Mini?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Jamba 1.5 Large. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Jamba 1.5 Large e Jamba 1.5 Mini?+
Jamba 1.5 Large costa $2.00 / $8.00 per milione di token di input/output, mentre Jamba 1.5 Mini costa $0.20 / $0.40. Nel costo combinato, Jamba 1.5 Mini risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Jamba 1.5 Large supporta 256K token di contesto contro 256K token di Jamba 1.5 Mini, mentre Jamba 1.5 Mini offre una velocità di generazione superiore.
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.
Fonti ufficiali: Documentazione API ufficiale di AI21 e AI21, schede di sistema e benchmark indipendenti (2026).