GPT-5.5 Pro vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5.5 Pro 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, GPT-5.5 Pro provides an expanded memory horizon with 1,050K 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.
Cambia y Compara Cualquier Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GPT-5.5 Pro
Máxima precisiónel slowest but la mayoría de accurate GPT-5.5 nivel para razonamiento y codificación.
$50.00 / 1M
$300.00 / 1M
1.05M tokens
82.6%
Gemini 4 Argon
Rey multimodal con salida de 1MGoogle de DeepMind insignia de frontera razonamiento motor con 1-millones-token salida limits, autónomo cybersecurity defense, y en tiempo real audio/visión ingesta.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Diferencias Clave de un Vistazo
- Gemini 4 Argon is 29.0x cheaper for API usage (input + output tokens combined).
- GPT-5.5 Pro has a larger context window (1M vs 1M tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 42 tok/s.
- GPT-5.5 Pro leads on SWE-bench coding (82.6% vs 81.9%).
| Característica | GPT-5.5 Pro | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 87 | 97 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $50.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $300.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $43.50 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,575 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 | 42 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 82.6% | 81.9% |
GPQA Diamond Graduate-level reasoning | 81.2% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $5.00 / 1M | $0.10 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones 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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.5 Pro · Strengths & Key Advantages
- más alto precisión OpenAI ofrece en this generación
- excepcional en adversarial y perimetral-case razonamiento
- más alto verified precisión OpenAI publishes para this generación
Considerations & Trade-offs
- Extremely expensive y slow
- solo rational para un pequeño number de alto-valor tareas
Gemini 4 Argon · Strengths & Key Advantages
- Groundbreaking 1,000,000 token maximum salida generación capacidad via largo Decode Continuation
- 1M-token entrada window con 99.7% GraphWalks recall (84.2% en el 256K-1M range)
- nativo procesamiento de completo audio, video streams, y visual datos (91.7% en LVBench)
- Half el token precio de de Anthropic Opus 5.5 y #1 en Chatbot Arena (1525 Elo)
Considerations & Trade-offs
- masivo token outputs puede incur alto absolute costos if unconstrained (~62K salida tokens por index tarea)
- $2/$10 precios es un introductory 50% discount; estándar rates revert un $4/$20 once el promotion ends
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.5 Pro | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $66.50 | $2.45 |
Output Tokens / Task Tokens generated per task | 78K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 52K | 36K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GPT-5.5 Pro | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-5-pro | models/gemini-4-argon |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | December 2025 | June 2026 |
| Architecture | Maximum-accuracy unified reasoning model | Pathways Multimodal Sparse Mixture-of-Experts |
| 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) | Flat rate ($50/1M in) across full 1050K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $5/1M (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) | 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 | text, vision, audio, video |
| Output Modalities | text | text |
GPT-5.5 Pro vs Gemini 4 Argon: Respuestas Rápidas (FAQ)
Which is more intelligent, GPT-5.5 Pro or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 87 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 42 tokens per second.
Which model is cheaper to run?
Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $43.50 (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 4.80s.
Which model has a larger context window?
GPT-5.5 Pro has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Veredicto
Verdict & Model Selection Recommendation: Choose GPT-5.5 Pro if your priority is Final-pass verification on the highest-stakes deliverables. 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.
Preguntas frecuentes
How does GPT-5.5 Pro compare to Gemini 4 Argon in API token pricing and cost per token?+
GPT-5.5 Pro costs $50.00 per 1M input tokens and $300.00 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-5.5 Pro or Gemini 4 Argon?+
GPT-5.5 Pro supports a context window of 1,050K tokens (~788k words), while Gemini 4 Argon supports 1,000K tokens (~750k words). Therefore, GPT-5.5 Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.5 Pro or Gemini 4 Argon?+
GPT-5.5 Pro scores 82.6% on SWE-bench Verified while Gemini 4 Argon scores 81.9%. For autonomous software engineering and multi-file debugging, GPT-5.5 Pro holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.5 Pro and Gemini 4 Argon?+
GPT-5.5 Pro 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-5.5 Pro and Gemini 4 Argon?+
GPT-5.5 Pro generates approximately 42 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-5.5 Pro 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-5.5 Pro. 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs