GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-5-Codex by OpenAI and Qwen3-Coder 480B-A35B Instruct by Alibaba 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, Qwen3-Coder 480B-A35B Instruct holds a notable pricing advantage for token ingestion. On output generation, Qwen3-Coder 480B-A35B Instruct is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5-Codex provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5-Codex 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
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GPT-5-Codex
Especialista en codificaciónUna variante de GPT-5 optimizada para ediciones de repositorio, revisión de código y trabajo de software agéntico.
$1.25 / 1M
$10.00 / 1M
400K tokens
74.5%
Qwen3-Coder 480B-A35B Instruct
Gigante de código abiertomasivo abierto codificación champion trained specifically en sobre 7 billones tokens de code, repositorio graphs, y técnico documentation.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
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%).
| 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 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-Codex · Strengths & Key Advantages
- Codificación agéntica de primer nivel en Codex y Copilot
- Diseñado para largas sesiones de edición en todo el repositorio
- Mismo precio que GPT-5 a pesar del ajuste de codificación
- Excelente en reparación guiada por pruebas y depuración iterativa
Considerations & Trade-offs
- Menos natural que GPT-5 en conversación general
- Excesivo para cargas de trabajo que no son de código
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Sweeping generación de código capacidades rivaling propietario codificación leaders
- nativo comprensión de multirepo dependencies, git diffs, y linting rules
- 262K ventana de contexto allows loading sustancial software codebases directly
Considerations & Trade-offs
- sustancial VRAM requirements necessitate dedicado multi-accelerator clústeres
- Ocasionalmente puede sobrecomplicar solicitudes de funciones de utilidad simples
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
Verdict & Model Selection Recommendation: Choose GPT-5-Codex if your priority is Agentic coding assistants and automated code review. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3-Coder 480B-A35B Instruct if your workflow prioritizes Enterprise software development agents, automated code migration, and security vulnerability auditing. 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-Codex compare to Qwen3-Coder 480B-A35B Instruct in API token pricing and cost per token?+
GPT-5-Codex costs $1.25 per 1M input tokens and $10.00 per 1M output tokens, whereas Qwen3-Coder 480B-A35B Instruct costs $0.00 input and $0.00 output. Overall, Qwen3-Coder 480B-A35B Instruct 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-Codex or Qwen3-Coder 480B-A35B Instruct?+
GPT-5-Codex supports a context window of 400K tokens (~300k words), while Qwen3-Coder 480B-A35B Instruct supports 262K tokens (~197k words). Therefore, GPT-5-Codex can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5-Codex or Qwen3-Coder 480B-A35B Instruct?+
GPT-5-Codex scores 74.5% on SWE-bench Verified while Qwen3-Coder 480B-A35B Instruct scores 56.5%. For autonomous software engineering and multi-file debugging, GPT-5-Codex holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5-Codex and Qwen3-Coder 480B-A35B Instruct?+
GPT-5-Codex features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3-Coder 480B-A35B Instruct provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5-Codex and Qwen3-Coder 480B-A35B Instruct?+
GPT-5-Codex generates approximately 120 tokens per second, while Qwen3-Coder 480B-A35B Instruct delivers 52 tokens per second. In latency-sensitive production environments, GPT-5-Codex provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5-Codex and Qwen3-Coder 480B-A35B Instruct to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3-Coder 480B-A35B Instruct to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5-Codex. 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