Jamba 1.5 Mini vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Jamba 1.5 Mini by AI21 against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
بدّل وقارن بين أي نموذجين
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Jamba 1.5 Mini
نموذج مفتوح الأوزاننموذج Jamba 1.5 Mini المقدم من AI21 يوفر نافذة سياق تصل إلى 256K رمز، بتكلفة $0.2 لكل مليون رمز إدخال و$0.4 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.20 / 1M
$0.40 / 1M
256K tokens
35.8%
Gemini 4 Argon
نموذج استدلال عميقنموذج Gemini 4 Argon المقدم من Google يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $2 لكل مليون رمز إدخال و$10 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
أهم الفروقات في لمحة
- Jamba 1.5 Mini is 16.5x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 256K tokens).
- Jamba 1.5 Mini is faster at 120 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 35.8%).
| Feature | Jamba 1.5 Mini | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 97 |
Provider | AI21 | |
Input Price Cost per million input tokens | $0.20 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.11 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 1000K |
Throughput Speed Tokens generated per second | 120 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 35.8% | 81.9% |
GPQA Diamond Graduate-level reasoning | 44.2% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.10 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Jamba 1.5 Mini · Strengths & Key Advantages
- بنية Hybrid SSM-Transformer (Mamba + Transformer MoE) متطورة من AI21 مع سعة سياق تبلغ 256K رمز
- كفاءة عالية في التكلفة ($0.2/1M إدخال و$0.4/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Gemini 4 Argon · Strengths & Key Advantages
- بنية Pathways Multimodal Sparse Mixture-of-Experts متطورة من Google مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($2/1M إدخال و$10/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Jamba 1.5 Mini | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.12 | $2.45 |
Output Tokens / Task Tokens generated per task | 20K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Jamba 1.5 Mini | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | jamba-1-5-mini | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-08 | 2026-09 |
| Knowledge Cutoff | March 2024 | June 2026 |
| Architecture | Hybrid SSM-Transformer (Mamba + Transformer MoE) | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 52B (12B active) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 256K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 32 GB VRAM (4-bit Quantized) · 112 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision, audio, video |
| Output Modalities | text | text |
Jamba 1.5 Mini vs Gemini 4 Argon: إجابات سريعة (FAQ)
Which is more intelligent, Jamba 1.5 Mini or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 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 115 tokens per second.
Which model is cheaper to run?
Jamba 1.5 Mini is cheaper at $0.11 per 1M tokens blended, compared with $1.47 (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 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 256K tokens.
الحكم النهائي
Choose Jamba 1.5 Mini for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات ai21 الإنتاجية, or choose Gemini 4 Argon for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات google الإنتاجية.
الأسئلة الشائعة (FAQ)
Which is cheaper: Jamba 1.5 Mini or Gemini 4 Argon?+
Jamba 1.5 Mini costs $0.2/1M input and $0.4/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Jamba 1.5 Mini or Gemini 4 Argon?+
Jamba 1.5 Mini supports 256K tokens whereas Gemini 4 Argon supports 1M tokens.
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.
المصادر الرسمية: AI21 & Google Official Documentation