Gemini 3.5 Flash vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 3.5 Flash by Google against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Gemini 3.5 Flash
旗舰前沿大模型Google 推出的 Gemini 3.5 Flash 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 1.0M 上下文窗口、190 Token/秒生成速度与 82/100 综合智能指数（每百万 Token $0.75 输入 / $3.75 输出）。
$0.75 / 1M
$3.75 / 1M
1.05M tokens
70.2%
GPT-6.1 Sol
旗舰前沿大模型OpenAI 推出的 GPT-6.1 Sol 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、120 Token/秒生成速度与 94/100 综合智能指数（每百万 Token $2 输入 / $10 输出）。
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
核心差异一览
- Gemini 3.5 Flash is 2.5x cheaper for API usage (input + output tokens combined).
- Gemini 3.5 Flash has a larger context window (1M vs 1M tokens).
- Gemini 3.5 Flash is faster at 190 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 70.2%).
| Feature | Gemini 3.5 Flash | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 94 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.75 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $3.75 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.66 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 128K |
Throughput Speed Tokens generated per second | 190 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 70.2% | 82.3% |
GPQA Diamond Graduate-level reasoning | 81% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.19 / 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.
Gemini 3.5 Flash · Strengths & Key Advantages
- 提供 1.0M 上下文窗口与最高 66K 输出容量，核心基准表现优异（SWE-bench Verified 70.2% · GPQA Diamond 81%）
- 高达 190 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 360 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.75 输入 / $3.75 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
GPT-6.1 Sol · 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 82.3% · GPQA Diamond 80.8%）
- 高达 120 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 1120 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 | Gemini 3.5 Flash | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.05 | $2.05 |
Output Tokens / Task Tokens generated per task | 38K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 36K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.5 Flash | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-5-flash | gpt-6-1-sol |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-05 | 2026-09 |
| Knowledge Cutoff | January 2026 | April 2026 |
| Architecture | Optimized Search-Grade Multimodal MoE | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1875/1M with configurable TTL | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | 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 | Google DeepMind Frontier Safety Framework Verified | OpenAI Preparedness Framework Audited & System Card Published |
| 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, vision, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 3.5 Flash vs GPT-6.1 Sol: 常见问题解答 (FAQ)
Which is more intelligent, Gemini 3.5 Flash or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 82 on the composite Intelligence Index.
Which model is faster?
Gemini 3.5 Flash is faster, generating 190 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Gemini 3.5 Flash is cheaper at $0.66 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.5 Flash has lower latency, with a time to first token of 0.36s compared with 1.12s.
Which model has a larger context window?
Gemini 3.5 Flash has a larger context window, supporting 1.05M tokens compared with 1M tokens.
综合结论
Choose Gemini 3.5 Flash 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。, or choose GPT-6.1 Sol 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: Gemini 3.5 Flash or GPT-6.1 Sol?+
Gemini 3.5 Flash costs $0.75/1M input and $3.75/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemini 3.5 Flash or GPT-6.1 Sol?+
Gemini 3.5 Flash supports 1.0M tokens whereas GPT-6.1 Sol 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: Google & OpenAI Official Documentation