Head-to-Head Model Comparison

Jamba 1.5 Large vs Jamba 1.5 Mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Jamba 1.5 LargeProfile

Jamba 1.5 MiniProfile

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Popular Matchups:

AI21

Jamba 1.5 Large

Open-Weights-Basismodell

Jamba 1.5 Large von AI21 ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 256K Kontextfenster, 82 Token/s Durchsatz, einem Intelligenz-Index von 42/100 und $2/$8 pro 1M Token.

Intelligence Index42 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

41.2%

AI21

Jamba 1.5 Mini

Ultraschnelle Echtzeit-Inferenz

Jamba 1.5 Mini von AI21 ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 256K Kontextfenster, 120 Token/s Durchsatz, einem Intelligenz-Index von 39/100 und $0.2/$0.4 pro 1M Token.

Intelligence Index39 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.40 / 1M

Context Window:

256K tokens

SWE-bench Verified:

35.8%

Wichtigste Unterschiede auf einen Blick

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%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Jamba 1.5 Large

+5.4% SWE-bench lead

Cost

Jamba 1.5 Mini

14.9x cheaper monthly runtime

Speed

Jamba 1.5 Mini

+46% faster generation

Context

Tied

256K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Jamba 1.5 LargeJamba 1.5 Mini

SWE-bench Verified (Autonomous Coding)

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

41.2%vs35.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

51%vs44.2%

MMLU-Pro (Multi-task Language Understanding)

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

70.2%vs64%

LMSYS Chatbot Arena Elo

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

1275ptsvs1242pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

6.9%vs4.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

26.5%vs21.5%

SciCode (Scientific Computing)

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

27.2%vs22.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

45%vs37.5%

AIME (Advanced Math Competition)

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

54%vs45%

Long-Context Reasoning

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

75%vs72%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

82 tok/s vs120 tok/s

Latenz & Antwortzeit

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

Jamba 1.5 Large0.49s

Jamba 1.5 Mini0.34s

Est. End-to-End Response (500 tokens)TTFT + generation time

Jamba 1.5 Large6.59s

Jamba 1.5 Mini4.51s

Interaktiver Token-Kosten-Simulator

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Jamba 1.5 Large Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

Jamba 1.5 Mini Monthly API Bill

$0.74 / month

$0.2/1M in · $0.4/1M out

Jamba 1.5 Mini saves approximately $10.21 / month (93%) under this workload.14.9x Savings

Jamba 1.5 Large · Strengths & Key Advantages

256K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 41.2% · GPQA Diamond 51%)
Hohe Generierungsgeschwindigkeit von 82 Token/Sekunde bei nur 490 ms Erstlatenz (TTFT)
Offene Modellgewichte (398B (94B )) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Jamba 1.5 Mini · Strengths & Key Advantages

256K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 35.8% · GPQA Diamond 44.2%)
Hohe Generierungsgeschwindigkeit von 120 Token/Sekunde bei nur 340 ms Erstlatenz (TTFT)
Offene Modellgewichte (52B (12B )) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl Jamba 1.5 Large (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Jamba 1.5 Mini (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Jamba 1.5 Large führt bei komplexem Reasoning und Coding, Jamba 1.5 Mini liefert den höheren Token-Durchsatz und Jamba 1.5 Mini ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Jamba 1.5 Large oder Jamba 1.5 Mini?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Jamba 1.5 Large vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Jamba 1.5 Large und Jamba 1.5 Mini?+

Jamba 1.5 Large kostet $2.00 / $8.00 pro 1M Input-/Output-Tokens, Jamba 1.5 Mini liegt bei $0.20 / $0.40. Beim gewichteten Gesamtpreis ist Jamba 1.5 Mini günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Jamba 1.5 Large bietet 256K Tokens Kontext gegenüber 256K Tokens bei Jamba 1.5 Mini. Bei der Generierungsgeschwindigkeit liegt Jamba 1.5 Mini vorn.

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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Jamba 1.5 Large vs Jamba 1.5 Mini

Beliebige Modelle wechseln & vergleichen

Jamba 1.5 Large

Jamba 1.5 Mini

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Jamba 1.5 Large · Strengths & Key Advantages

Considerations & Trade-offs

Jamba 1.5 Mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Jamba 1.5 Large vs Jamba 1.5 Mini: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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