GPT-5.3 Codex vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-5.3 Codex by OpenAI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
Cambia y Compara Cualquier 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.
GPT-5.3 Codex
Salto en codificaciónun codificación modelo optimised para repositorio edits, revisión y agentic ingeniería de software.
$2.00 / 1M
$16.00 / 1M
400K tokens
79%
Claude Opus 5.5
Titán de inteligencia de fronterade Anthropic pinnacle modelo de frontera diseñado para de largo alcance agentic ingeniería de software, denso multimodal documento síntesis, y autónomo investigación flujos de trabajo.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Diferencias Clave de un Vistazo
- GPT-5.3 Codex is 1.4x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 400K tokens).
- GPT-5.3 Codex is faster at 122 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 79.0%).
| Característica | GPT-5.3 Codex | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 72 | 94 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $2.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $16.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.14 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 122 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 79% | 84.6% |
GPQA Diamond Graduate-level reasoning | 79.2% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 1M | $0.40 / 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-5.3 Codex · Strengths & Key Advantages
- un clear step up en multifarchivo code razonamiento
- Fewer hallucinated APIs que earlier Codex generations
- sólido un test-driven repair bucles
- sólido un grande-escala automatizado migrations
Considerations & Trade-offs
- Premium precios para un code-solo modelo
- Diminishing returns versus GPT-5.2 Codex en pequeño tareas
Claude Opus 5.5 · Strengths & Key Advantages
- inigualable autónomo multipaso codificación agéntica y refactorización
- Flawless multidocumento razonamiento sobre 1M token contexto con no largo-prompt surcharge
- más alto Artificial análisis inteligencia Index ever measured (58/100), leading SWE-bench Pro un 89.9%
- Precise herramienta ejecución con robust error-recovery bucles
Considerations & Trade-offs
- Premium salida precios ($20.00/M) plus heavy razonamiento-token usage (~119K salida tokens por index tarea) keeps largo agentic se ejecuta expensive
- largo pensamiento times un maximum effort puede push un único hard agentic tarea toward ~$13
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.3 Codex | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.60 | $3.85 |
Output Tokens / Task Tokens generated per task | 62K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 42K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GPT-5.3 Codex | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-3-codex | claude-opus-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-02 | 2026-09 |
| Knowledge Cutoff | November 2025 | June 2026 |
| Architecture | Code-specialised unified reasoning model | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 400K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.2/1M (no extra cache-write fee) | $0.4/1M cached read (90% off); 5-min default & 1-hour extended 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Custom Model Tuning via Amazon Bedrock & Prompt 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) |
| 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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 |
| Output Modalities | text | text |
GPT-5.3 Codex vs Claude Opus 5.5: Respuestas Rápidas (FAQ)
Which is more intelligent, GPT-5.3 Codex or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 72 on the composite Intelligence Index.
Which model is faster?
GPT-5.3 Codex is faster, generating 122 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
GPT-5.3 Codex is cheaper at $2.14 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.3 Codex has lower latency, with a time to first token of 1.23s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 400K tokens.
Veredicto
Choose GPT-5.3 Codex for teams whose default flujo de trabajo es agente-driven development, or choose Claude Opus 5.5 for grande-escala software development agentes, empresarial codebases, y de misión crítica científico síntesis.
Preguntas frecuentes
Which is cheaper: GPT-5.3 Codex or Claude Opus 5.5?+
GPT-5.3 Codex costs $2/1M input and $16/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: GPT-5.3 Codex or Claude Opus 5.5?+
GPT-5.3 Codex supports 400K tokens whereas Claude Opus 5.5 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: Benchmarks, Pricing & Speed ComparisonComparar
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed ComparisonComparar
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed ComparisonComparar
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed ComparisonComparar
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed ComparisonComparar
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed ComparisonComparar
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: OpenAI & Anthropic Official Documentation