Nex-N2-Mini vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Nex-N2-Mini by Nex AGI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Nex-N2-Mini
深度逻辑推理引擎Nex AGI 推出的 Nex-N2-Mini 是一款专为博士级数理逻辑、科学推导与多步思维链 (CoT) 设计的深度推理大模型，具备 262K 上下文窗口、155 Token/秒生成速度与 75/100 综合智能指数（每百万 Token $0.08 输入 / $0.32 输出）。
$0.08 / 1M
$0.32 / 1M
262.1K tokens
N/A
Claude Opus 5.5
旗舰前沿大模型Anthropic 推出的 Claude Opus 5.5 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型，具备 1M 上下文窗口、78 Token/秒生成速度与 94/100 综合智能指数（每百万 Token $4 输入 / $20 输出）。
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
核心差异一览
- Nex-N2-Mini is 58.6x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 262K tokens).
- Nex-N2-Mini is faster at 155 tok/s vs 78 tok/s.
| Feature | Nex-N2-Mini | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 94 |
Provider | Nex AGI | Anthropic |
Input Price Cost per million input tokens | $0.08 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.32 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 155 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 84.6% |
GPQA Diamond Graduate-level reasoning | N/A | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.40 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
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.
Nex-N2-Mini · Strengths & Key Advantages
- 提供 262K 上下文窗口与最高 8K 输出容量，核心基准表现优异（MMLU-Pro 72% · Arena Elo 1260）
- 高达 155 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 270 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.08 输入 / $0.32 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
Claude Opus 5.5 · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 84.6% · GPQA Diamond 82.3%）
- 高达 78 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 1400 毫秒
- 极具竞争力的 API 定价（每百万 Token $4 输入 / $20 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 | Nex-N2-Mini | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $3.85 |
Output Tokens / Task Tokens generated per task | 12K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 42K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Nex-N2-Mini | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | nex-n2-mini | claude-opus-5-5-20260901 |
| API Protocol | Nex AGI REST API (OpenAI-Compatible) | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-06 | 2026-09 |
| Knowledge Cutoff | March 2026 | June 2026 |
| Architecture | Compact Budget Reasoning Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.08/1M in) across full 262K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.008/1M tokens | $0.4/1M cached read (90% off); 5-min default & 1-hour extended 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| 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 | Nex AGI Production Safety & Alignment Verified | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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 |
| Output Modalities | text | text |
Nex-N2-Mini vs Claude Opus 5.5: 常见问题解答 (FAQ)
Which is more intelligent, Nex-N2-Mini or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 75 on the composite Intelligence Index.
Which model is faster?
Nex-N2-Mini is faster, generating 155 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Nex-N2-Mini is cheaper at $0.05 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nex-N2-Mini has lower latency, with a time to first token of 0.27s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 262.1K tokens.
综合结论
Choose Nex-N2-Mini for 专为博士级数理逻辑、科学推导与多步思维链 (cot) 设计的深度推理任务、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 Claude Opus 5.5 for 面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、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: Nex-N2-Mini or Claude Opus 5.5?+
Nex-N2-Mini costs $0.08/1M input and $0.32/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Nex-N2-Mini or Claude Opus 5.5?+
Nex-N2-Mini supports 262K tokens whereas Claude Opus 5.5 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: Nex AGI & Anthropic Official Documentation