Head-to-Head Model Comparison

GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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Select any AI models from our verified benchmark database to generate live side-by-side matrices.

GPT-5-CodexProfile

Qwen3-Coder 480B-A35B InstructProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-5-Codex

Líder en Programación e Ingeniería

GPT-5-Codex de OpenAI es un modelo de IA diseñado para ingeniería de software autónoma, refactorización de código y depuración avanzada, con ventana de contexto de 400K, velocidad de 120 tokens/s, índice de inteligencia de 57/100 y tarifa d

Intelligence Index57 / 100

Input Token:

$1.25 / 1M

Output Token:

$10.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

74.5%

Alibaba

Qwen3-Coder 480B-A35B Instruct

Modelo Base de Pesos Abiertos

Qwen3-Coder 480B-A35B Instruct de Alibaba es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 262K, velocidad de 52 tokens/s, índice de inteligen

Intelligence Index86 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

56.5%

Diferencias Clave de un Vistazo

Qwen3-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
GPT-5-Codex has a larger context window (400K vs 262K tokens).
GPT-5-Codex is faster at 120 tok/s vs 52 tok/s.
GPT-5-Codex leads on SWE-bench coding (74.5% vs 56.5%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	GPT-5-Codex	Qwen3-Coder 480B-A35B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	57	86
Provider	OpenAI	Alibaba
Input Price Cost per million input tokens	$1.25 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$10.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.34 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	400K tokens~600 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	128K	16K
Throughput Speed Tokens generated per second	120 tok/s	52 tok/s
SWE-bench Verified Real-world software engineering	74.5%	56.5%
GPQA Diamond Graduate-level reasoning	80.5%	70%
Cached Input Price Prompt caching discount per 1M tokens	$0.13 / 1M	$0.00 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

GPT-5-Codex

+18.0% SWE-bench lead

Costo

Qwen3-Coder 480B-A35B Instruct

1.0x cheaper monthly runtime

Velocidad

GPT-5-Codex

+131% faster generation

Contexto

GPT-5-Codex

400K tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

GPT-5-CodexQwen3-Coder 480B-A35B Instruct

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

74.5%vs56.5%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

80.5%vs70%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

85.5%vs83%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1360ptsvs1355pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

12%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

50%vsN/A

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

45.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73.5%vs73%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

90.5%vsN/A

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

77%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

120 tok/s vs52 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

GPT-5-Codex1.25s

Qwen3-Coder 480B-A35B Instruct0.76s

Est. End-to-End Response (500 tokens)TTFT + generation time

GPT-5-Codex5.42s

Qwen3-Coder 480B-A35B Instruct10.38s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

GPT-5-Codex Monthly API Bill

$11.06 / month

$1.25/1M in · $10/1M out

Qwen3-Coder 480B-A35B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3-Coder 480B-A35B Instruct saves approximately $11.06 / month (100%) under this workload.1.0x Savings

GPT-5-Codex · Strengths & Key Advantages

Ventana de contexto de 400K con hasta 128K tokens de salida máxima (SWE-bench Verified 74.5% · GPQA Diamond 80.5%)
Rendimiento ágil de 120 tokens/segundo con latencia inicial (TTFT) de solo 1250 ms
Economía de API competitiva ($1.25/$10 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 16K tokens de salida máxima (SWE-bench Verified 56.5% · GPQA Diamond 70%)
Rendimiento ágil de 52 tokens/segundo con latencia inicial (TTFT) de solo 760 ms
Pesos abiertos (480B total (35B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	GPT-5-Codex	Qwen3-Coder 480B-A35B Instruct
Cost per Task Estimated USD to finish one task	$1.60	$0.00
Output Tokens / Task Tokens generated per task	58K	25K
Reasoning Tokens / Task Thinking tokens burned per task	32K	9K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	GPT-5-Codex	Qwen3-Coder 480B-A35B Instruct
Official API Model ID (SDK)	gpt-5-codex	qwen3-coder-480b-a35b-instruct
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-09	2025-04
Knowledge Cutoff	September 2024	March 2025
Architecture	Code-specialised unified reasoning model	Code-Specialized Sparse Mixture-of-Experts Transformer
Parameters	Confidential	480B total (35B active MoE)
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($1.25/1M in) across full 400K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix caching at $0.125/1M (no extra cache-write fee)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 298 GB VRAM (4-bit Quantized) · 1032 GB VRAM (Native BF16)
Safety & Frontier Classification	OpenAI Preparedness Framework Audited & System Card Published	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text
Output Modalities	text	text

GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct: Respuestas Rápidas (FAQ)

Which is more intelligent, GPT-5-Codex or Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is more intelligent. It scores 86 compared with 57 on the composite Intelligence Index.

Which model is faster?

GPT-5-Codex is faster, generating 120 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen3-Coder 480B-A35B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $1.34 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3-Coder 480B-A35B Instruct has lower latency, with a time to first token of 0.76s compared with 1.25s.

Which model has a larger context window?

GPT-5-Codex has a larger context window, supporting 400K tokens compared with 262.1K tokens.

Veredicto

Tanto GPT-5-Codex (Ideal para ingeniería de software autónoma, refactorización de código y depuración avanzada, sistemas RAG empresariales y automatización mediante API.) como Qwen3-Coder 480B-A35B Instruct (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen3-Coder 480B-A35B Instruct lidera en inteligencia general y programación, GPT-5-Codex ofrece mayor velocidad de generación de tokens y Qwen3-Coder 480B-A35B Instruct resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, GPT-5-Codex o Qwen3-Coder 480B-A35B Instruct?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen3-Coder 480B-A35B Instruct obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de GPT-5-Codex frente a Qwen3-Coder 480B-A35B Instruct?+

GPT-5-Codex cuesta $1.25 / $10.00 por millón de tokens de entrada/salida, mientras que Qwen3-Coder 480B-A35B Instruct cuesta $0.00 / $0.00. En costo combinado, Qwen3-Coder 480B-A35B Instruct es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

GPT-5-Codex cuenta con 400K tokens de contexto frente a 262.1K tokens de Qwen3-Coder 480B-A35B Instruct. En velocidad de inferencia, GPT-5-Codex ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct

Cambia y Compara Cualquier Modelo

GPT-5-Codex

Qwen3-Coder 480B-A35B Instruct

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

GPT-5-Codex · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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