Amazon Nova Micro vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Amazon Nova Micro by Amazon against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Amazon Nova Micro
轻量级高性价比模型Amazon 推出的 Amazon Nova Micro 是一款兼顾极低 API 调用成本与高吞吐效率的轻量级大模型，具备 128K 上下文窗口、175 Token/秒生成速度与 34/100 综合智能指数（每百万 Token $0.035 输入 / $0.14 输出）。
$0.04 / 1M
$0.14 / 1M
128K tokens
23%
Gemini 4 Argon
原生多模态视觉模型Google 推出的 Gemini 4 Argon 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 1M 上下文窗口、115 Token/秒生成速度与 97/100 综合智能指数（每百万 Token $2 输入 / $10 输出）。
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
核心差异一览
- Amazon Nova Micro is 63.3x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Amazon Nova Micro is faster at 175 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 23.0%).
| Feature | Amazon Nova Micro | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 34 | 97 |
Provider | Amazon | |
Input Price Cost per million input tokens | $0.04 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.14 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 1000K |
Throughput Speed Tokens generated per second | 175 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 23% | 81.9% |
GPQA Diamond Graduate-level reasoning | 33.5% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Amazon Nova Micro · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 23% · GPQA Diamond 33.5%）
- 高达 175 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 180 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.035 输入 / $0.14 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Gemini 4 Argon · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 1M 输出容量，核心基准表现优异（SWE-bench Verified 81.9% · GPQA Diamond 81.5%）
- 高达 115 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 850 毫秒
- 极具竞争力的 API 定价（每百万 Token $2 输入 / $10 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Amazon Nova Micro | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.45 |
Output Tokens / Task Tokens generated per task | 15K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Amazon Nova Micro | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | nova-micro | models/gemini-4-argon |
| API Protocol | Amazon REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-12 | 2026-09 |
| Knowledge Cutoff | October 2024 | June 2026 |
| Architecture | Ultra-Fast Text-Only Dense Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.035/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.009/1M 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 | API Prompt Engineering & RAG Distillation | 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 | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Amazon Production Safety & Alignment Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 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 |
Amazon Nova Micro vs Gemini 4 Argon: 常见问题解答 (FAQ)
Which is more intelligent, Amazon Nova Micro or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 34 on the composite Intelligence Index.
Which model is faster?
Amazon Nova Micro is faster, generating 175 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Amazon Nova Micro is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Amazon Nova Micro has lower latency, with a time to first token of 0.18s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.
综合结论
Choose Amazon Nova Micro for 兼顾极低 api 调用成本与高吞吐效率的轻量级任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。, or choose Gemini 4 Argon for 原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。.
常见问题 (FAQ)
Which is cheaper: Amazon Nova Micro or Gemini 4 Argon?+
Amazon Nova Micro costs $0.035/1M input and $0.14/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Amazon Nova Micro or Gemini 4 Argon?+
Amazon Nova Micro supports 128K 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.
数据来源: Amazon & Google Official Documentation