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Head-to-Head Model Comparison

Jamba 1.5 Large vs Jamba 1.5 Mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Jamba 1.5 Large vs Jamba 1.5 Mini: Benchmarks, Pricing & Speed Comparison

Jamba 1.5 Large dari AI21 dan Jamba 1.5 Mini dari AI21 adalah dua model AI unggulan. Jamba 1.5 Large menawarkan jendela konteks 256K token dengan harga $2.00/$8.00 per 1 juta token, sedangkan Jamba 1.5 Mini menyediakan konteks 256K token seharga $0.20/$0.40 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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Popular Matchups:
AI21

Jamba 1.5 Large

Model Dasar Bobot Terbuka

Jamba 1.5 Large dari AI21 adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 256K, kecepatan 82 token/detik, indeks kecerdasan 42/100, serta tarif $2/$8

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

Inferensi Super Cepat

Jamba 1.5 Mini dari AI21 adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 256K, kecepatan 120 token/detik, indeks kecerdasan 39/100, serta tarif

Intelligence Index39 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.40 / 1M

Context Window:

256K tokens

SWE-bench Verified:

35.8%

Perbedaan Utama Sekilas

  • 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 →
FeatureJamba 1.5 LargeJamba 1.5 Mini
Intelligence Index
Composite reasoning & knowledge score (0-100)
4239
Provider
AI21AI21
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 text256K tokens~384 A4 pages of text
Max Output Tokens
Per single request
4K4K
Throughput Speed
Tokens generated per second
82 tok/s120 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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

100K / day
10K500K1M
30K / day
5K150K300K
50% 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

  • Jendela konteks 256K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 41.2% · GPQA Diamond 51%)
  • Kecepatan generasi tinggi 82 token/detik dengan latensi token pertama (TTFT) hanya 490 ms
  • Bobot terbuka (398B (94B )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

Jamba 1.5 Mini · Strengths & Key Advantages

  • Jendela konteks 256K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 35.8% · GPQA Diamond 44.2%)
  • Kecepatan generasi tinggi 120 token/detik dengan latensi token pertama (TTFT) hanya 340 ms
  • Bobot terbuka (52B (12B )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricJamba 1.5 LargeJamba 1.5 Mini
Cost per Task
Estimated USD to finish one task
$0.48$0.12
Output Tokens / Task
Tokens generated per task
25K20K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureJamba 1.5 LargeJamba 1.5 Mini
Official API Model ID (SDK)jamba-1-5-largejamba-1-5-mini
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-082024-08
Knowledge CutoffMarch 2024March 2024
ArchitectureHybrid SSM-Transformer (Mamba + Transformer MoE)Hybrid SSM-Transformer (Mamba + Transformer MoE)
Parameters398B (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 CloudUnlimited (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 windowFlat rate ($0.2/1M in) across full 256K window
Prompt Cache Policy & TTLCached input at $0.5/1M tokensCached input at $0.05/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Jamba 1.5 Large vs Jamba 1.5 Mini: Jawaban Cepat (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.

Kesimpulan

Baik Jamba 1.5 Large (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Jamba 1.5 Mini (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Jamba 1.5 Large unggul dalam penalaran dan pemrograman, Jamba 1.5 Mini memberikan kecepatan generasi token lebih tinggi, dan Jamba 1.5 Mini lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Jamba 1.5 Large atau Jamba 1.5 Mini?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Jamba 1.5 Large memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Jamba 1.5 Large vs Jamba 1.5 Mini?+

Jamba 1.5 Large mengenakan biaya $2.00 / $8.00 per 1M token input/output, sedangkan Jamba 1.5 Mini sebesar $0.20 / $0.40. Secara keseluruhan, Jamba 1.5 Mini lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Jamba 1.5 Large mendukung konteks 256K token dibandingkan 256K token pada Jamba 1.5 Mini, sementara Jamba 1.5 Mini unggul dalam kecepatan output token per detik.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Sumber resmi: Dokumentasi API resmi AI21 & AI21 serta evaluasi benchmark independen (2026).