Gemma 3n vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemma 3n by Google against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
Troque e Compare Qualquer Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Gemma 3n
Modelo Nativo de Voz e ÁudioO Gemma 3n da Google é um modelo de IA projetado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, oferecendo janela de contexto de 32K, velocidade de 150 tokens/s, índice de inteligência de 52/100 e tarifa
$0.00 / 1M
$0.00 / 1M
32K tokens
N/A
GPT-6.1 Sol
Modelo Principal de FronteiraO GPT-6.1 Sol da OpenAI é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 1M, velocidade de 120 tokens/s, índice de inteligência de 94/100 e tar
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Principais Diferenças em Resumo
- Gemma 3n is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 32K tokens).
- Gemma 3n is faster at 150 tok/s vs 120 tok/s.
| Feature | Gemma 3n | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 94 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32K tokens~48 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 150 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkEvaluations ongoing
Pontuações 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.
Gemma 3n · Strengths & Key Advantages
- Janela de contexto de 32K com limite máximo de saída de 4K tokens (MMLU-Pro 64% · Arena Elo 1220)
- Taxa de transferência rápida de 150 tokens/segundo com latência inicial (TTFT) de 220 ms
- Pesos abertos (2B / 5B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
GPT-6.1 Sol · Strengths & Key Advantages
- Janela de contexto de 1M com limite máximo de saída de 128K tokens (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
- Taxa de transferência rápida de 120 tokens/segundo com latência inicial (TTFT) de 1120 ms
- Economia de API competitiva ($2/$10 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 3n | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.05 |
Output Tokens / Task Tokens generated per task | 3K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 3n | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-3n | gpt-6-1-sol |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-06 | 2026-09 |
| Knowledge Cutoff | March 2025 | April 2026 |
| Architecture | Edge-optimized multimodal transformer with audio support | Near-Astra unified reasoning model |
| Parameters | 2B / 5B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | 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) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio | text, vision |
| Output Modalities | text | text |
Gemma 3n vs GPT-6.1 Sol: Respostas Rápidas (FAQ)
Which is more intelligent, Gemma 3n 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?
Gemma 3n is faster, generating 150 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Gemma 3n is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemma 3n has lower latency, with a time to first token of 0.22s compared with 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 32K tokens.
Veredito
Choose Gemma 3n for indicado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, arquiteturas rag corporativas e integração escalável via api., or choose GPT-6.1 Sol for indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas rag corporativas e integração escalável via api..
Perguntas Frequentes (FAQ)
Which is cheaper: Gemma 3n or GPT-6.1 Sol?+
Gemma 3n costs $0/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemma 3n or GPT-6.1 Sol?+
Gemma 3n supports 32K 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.
Related reading
- Aion-3.0 vs. Aion-Mini: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Aion-3.0 vs. Gemini 3.6 Flash: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Aion-Mini vs. Laguna XS 2.1: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Aya Expanse 32B vs. Llama 3.2 90B Vision: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Aya Expanse 8B vs. Command R+: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Aya Expanse 8B vs. Qwen 2.5 7B Instruct: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
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