Gemini 2.0 Pro vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 2.0 Pro by Google against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Gemini 2.0 Pro
Modelo de Raciocínio ProfundoO Gemini 2.0 Pro da Google é um modelo de inteligência artificial com janela de contexto de 2M tokens e preço de API de $1.25/1M na entrada e $5/1M na saída, projetado para programação, raciocínio e automação empresarial.
$1.25 / 1M
$5.00 / 1M
2M tokens
63.8%
GPT-6.1 Sol
Modelo de Raciocínio ProfundoO GPT-6.1 Sol da OpenAI é um modelo de inteligência artificial com janela de contexto de 1M tokens e preço de API de $2.0/1M na entrada e $10.0/1M na saída, projetado para programação, raciocínio e automação empresarial.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Principais Diferenças em Resumo
- Gemini 2.0 Pro is 1.8x cheaper for API usage (input + output tokens combined).
- Gemini 2.0 Pro has a larger context window (2M vs 1M tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 95 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 63.8%).
| Feature | Gemini 2.0 Pro | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 94 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $1.25 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $5.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.97 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 2M tokens~3,000 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 | 95 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 63.8% | 82.3% |
GPQA Diamond Graduate-level reasoning | 71.5% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.31 / 1M | $0.10 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 2.0 Pro · Strengths & Key Advantages
- Arquitetura Multimodal Deep Reasoning Sparse MoE avançada da Google com contexto de 2M tokens
- Excelente relação custo-benefício ($1.25/1M entrada, $5/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
GPT-6.1 Sol · Strengths & Key Advantages
- Arquitetura Near-Astra unified reasoning model avançada da OpenAI com contexto de 1M tokens
- Excelente relação custo-benefício ($2.0/1M entrada, $10.0/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 2.0 Pro | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.59 | $2.05 |
Output Tokens / Task Tokens generated per task | 60K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 33K | 36K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 2.0 Pro | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-2-pro | gpt-6-1-sol |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-02 | 2026-09 |
| Knowledge Cutoff | January 2025 | April 2026 |
| Architecture | Multimodal Deep Reasoning Sparse 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 $0.31/1M 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 2.0 Pro vs GPT-6.1 Sol: Respostas Rápidas (FAQ)
Which is more intelligent, Gemini 2.0 Pro or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 52 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 95 tokens per second.
Which model is cheaper to run?
Gemini 2.0 Pro is cheaper at $0.97 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 2.0 Pro has lower latency, with a time to first token of 0.62s compared with 1.12s.
Which model has a larger context window?
Gemini 2.0 Pro has a larger context window, supporting 2M tokens compared with 1M tokens.
Veredito
Choose Gemini 2.0 Pro for automação corporativa, engenharia de software, agentes autônomos e pipelines de produção com google, or choose GPT-6.1 Sol for automação corporativa, engenharia de software, agentes autônomos e pipelines de produção com openai.
Perguntas Frequentes (FAQ)
Which is cheaper: Gemini 2.0 Pro or GPT-6.1 Sol?+
Gemini 2.0 Pro costs $1.25/1M input and $5/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemini 2.0 Pro or GPT-6.1 Sol?+
Gemini 2.0 Pro supports 2M 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.
Fontes oficiais: Google & OpenAI Official Documentation