GPT-6 Astra vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-6 Astra by OpenAI and Gemini 4 Argon by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini 4 Argon holds a notable pricing advantage for token ingestion. On output generation, Gemini 4 Argon is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 4 Argon provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Gemini 4 Argon leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GPT-6 Astra
Modelo de Raciocínio ProfundoO GPT-6 Astra da OpenAI é um modelo de inteligência artificial com janela de contexto de 1M tokens e preço de API de $10.0/1M na entrada e $50.0/1M na saída, projetado para programação, raciocínio e automação empresarial.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Gemini 4 Argon
Modelo de Raciocínio ProfundoO Gemini 4 Argon da Google é um modelo de inteligência artificial com janela de contexto de 1M tokens e preço de API de $2/1M na entrada e $10/1M na saída, projetado para programação, raciocínio e automação empresarial.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Principais Diferenças em Resumo
- Gemini 4 Argon is 5.1x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 272K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 100 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 81.9%).
| Feature | GPT-6 Astra | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 96 | 97 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $10.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $50.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $7.70 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 272K tokens~408 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 1000K |
Throughput Speed Tokens generated per second | 100 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 82.4% | 81.9% |
GPQA Diamond Graduate-level reasoning | 80.9% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 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.
GPT-6 Astra · Strengths & Key Advantages
- Arquitetura Omni-Adaptive Autoregressive with Dynamic CoT avançada da OpenAI com contexto de 1M tokens
- Excelente relação custo-benefício ($10.0/1M entrada, $50.0/1M 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
Gemini 4 Argon · Strengths & Key Advantages
- Arquitetura Pathways Multimodal Sparse Mixture-of-Experts avançada da Google com contexto de 1M tokens
- Excelente relação custo-benefício ($2/1M entrada, $10/1M 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 | GPT-6 Astra | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.15 | $2.45 |
Output Tokens / Task Tokens generated per task | 58K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 32K | 36K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Astra | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-astra | models/gemini-4-argon |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-09 | 2026-09 |
| Knowledge Cutoff | June 2026 | June 2026 |
| Architecture | Omni-Adaptive Autoregressive with Dynamic CoT | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 ($10/1M in) across full 272K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $1/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) · 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 | OpenAI Preparedness Framework Audited & System Card Published | 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, vision, audio | text, vision, audio, video |
| Output Modalities | text, audio | text |
GPT-6 Astra vs Gemini 4 Argon: Respostas Rápidas (FAQ)
Which is more intelligent, GPT-6 Astra or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 96 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 4 Argon has lower latency, with a time to first token of 0.85s compared with 0.95s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 272K tokens.
Veredito
Verdict & Model Selection Recommendation: Choose GPT-6 Astra if your priority is Autonomous browser/computer workflows, real-time voice agents, and high-difficulty mathematical reasoning. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 4 Argon if your workflow prioritizes Whole-repository code generation, full-book synthesis, video understanding, and multi-hour audio processing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does GPT-6 Astra compare to Gemini 4 Argon in API token pricing and cost per token?+
GPT-6 Astra costs $10.00 per 1M input tokens and $50.00 per 1M output tokens, whereas Gemini 4 Argon costs $2.00 input and $10.00 output. Overall, Gemini 4 Argon is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-6 Astra or Gemini 4 Argon?+
GPT-6 Astra supports a context window of 272K tokens (~204k words), while Gemini 4 Argon supports 1,000K tokens (~750k words). Therefore, Gemini 4 Argon can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-6 Astra or Gemini 4 Argon?+
GPT-6 Astra scores 82.4% on SWE-bench Verified while Gemini 4 Argon scores 81.9%. For autonomous software engineering and multi-file debugging, GPT-6 Astra holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-6 Astra and Gemini 4 Argon?+
GPT-6 Astra features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 4 Argon provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-6 Astra and Gemini 4 Argon?+
GPT-6 Astra generates approximately 100 tokens per second, while Gemini 4 Argon delivers 115 tokens per second. In latency-sensitive production environments, Gemini 4 Argon provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-6 Astra and Gemini 4 Argon to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 4 Argon to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-6 Astra. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs