Sonar vs Gemini 1.5 Pro
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Perplexity 的 Sonar 与 Google 的 Gemini 1.5 Pro 是当前备受关注的两款主力 AI 模型。Sonar 提供 128K Token 上下文窗口，API 定价为每百万 Token 输入 $1.00 / 输出 $1.00；而 Gemini 1.5 Pro 提供 2M Token 上下文窗口，定价为每百万 Token 输入 $1.25 / 输出 $5.00。以下为两者的基准测试、延迟与成本全面对比。
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Sonar
轻量级高性价比模型Perplexity 推出的 Sonar 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、130 Token/秒生成速度与 73/100 综合智能指数（每百万 Token $1 输入 / $1 输出）。
$1.00 / 1M
$1.00 / 1M
128K tokens
N/A
Gemini 1.5 Pro
经典世代基础模型Google 推出的 Gemini 1.5 Pro 是一款面向企业级对话与通用文本生成任务的高可靠性经典大模型，具备 2M 上下文窗口、65 Token/秒生成速度与 46/100 综合智能指数（每百万 Token $1.25 输入 / $5 输出）。
$1.25 / 1M
$5.00 / 1M
2M tokens
32.5%
核心差异一览
- Sonar is 2.7x cheaper for API usage (input + output tokens combined).
- Gemini 1.5 Pro has a larger context window (2M vs 128K tokens).
- Sonar is faster at 130 tok/s vs 65 tok/s.
| Feature | Sonar | Gemini 1.5 Pro |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 73 | 46 |
Provider | Perplexity | |
Input Price Cost per million input tokens | $1.00 / 1M | $1.25 / 1M |
Output Price Cost per million output tokens | $1.00 / 1M | $5.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.37 / 1M | $0.97 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 2M tokens~3,000 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 130 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 32.5% |
GPQA Diamond Graduate-level reasoning | N/A | 58.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.31 / 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.
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.
Sonar · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 4K 输出容量，核心基准表现优异（MMLU-Pro 71% · Arena Elo 1245）
- 高达 130 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 350 毫秒
- 极具竞争力的 API 定价（每百万 Token $1 输入 / $1 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 1.5 Pro · Strengths & Key Advantages
- 提供 2M 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 32.5% · GPQA Diamond 58.5%）
- 高达 65 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 800 毫秒
- 极具竞争力的 API 定价（每百万 Token $1.25 输入 / $5 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Sonar | Gemini 1.5 Pro |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $0.20 |
Output Tokens / Task Tokens generated per task | 10K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Sonar | Gemini 1.5 Pro |
|---|---|---|
| Official API Model ID (SDK) | sonar | models/gemini-1-5-pro |
| API Protocol | Perplexity REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-01 | 2024-02 |
| Knowledge Cutoff | Real-time (Web-grounded) | May 2024 |
| Architecture | Web-Search Augmented Autoregressive Transformer | Sparse Mixture-of-Experts (MoE) Multimodal |
| Parameters | Confidential / Llama 3 Fine-Tune | 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 ($1/1M 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 $0.1/1M tokens | Implicit + Explicit Context Caching at $0.31/1M 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 | Perplexity 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 |
Sonar vs Gemini 1.5 Pro: 常见问题解答 (FAQ)
Which is more intelligent, Sonar or Gemini 1.5 Pro?
Sonar is more intelligent. It scores 73 compared with 46 on the composite Intelligence Index.
Which model is faster?
Sonar is faster, generating 130 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Sonar is cheaper at $0.37 per 1M tokens blended, compared with $0.97 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Sonar has lower latency, with a time to first token of 0.35s compared with 0.80s.
Which model has a larger context window?
Gemini 1.5 Pro has a larger context window, supporting 2M tokens compared with 128K tokens.
综合结论
综合来看，Sonar（适合：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。）与 Gemini 1.5 Pro（适合：面向企业级对话与通用文本生成任务的高可靠性经典任务、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。）各具优势：Sonar 在综合智能与编程基准测试中表现更强，Sonar 拥有更快的 Token 生成速度，而 Sonar 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Sonar 和 Gemini 1.5 Pro 哪个更强？+
在综合智能指数与编程基准评测中，Sonar 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Sonar 与 Gemini 1.5 Pro 的 API 价格哪个更便宜？+
Sonar 的 API 价格为输入 $1.00 / 输出 $1.00（每百万 Token），Gemini 1.5 Pro 为输入 $1.25 / 输出 $5.00。综合混合成本来看，Sonar 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Sonar 支持 128K Token 上下文窗口，而 Gemini 1.5 Pro 支持 2M Token。在流式生成速度方面，Sonar 的吞吐表现更快。
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: Perplexity 与 Google 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。