GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI 的 GPT-5-Codex 与 Alibaba 的 Qwen3-Coder 480B-A35B Instruct 是当前备受关注的两款主力 AI 模型。GPT-5-Codex 提供 400K Token 上下文窗口，API 定价为每百万 Token 输入 $1.25 / 输出 $10.00；而 Qwen3-Coder 480B-A35B Instruct 提供 262.1K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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GPT-5-Codex
顶级编程与软件工程OpenAI 推出的 GPT-5-Codex 是一款专为自主软件工程、跨文件代码重构与复杂调试打造的旗舰编程大模型，具备 400K 上下文窗口、120 Token/秒生成速度与 57/100 综合智能指数（每百万 Token $1.25 输入 / $10 输出）。
$1.25 / 1M
$10.00 / 1M
400K tokens
74.5%
Qwen3-Coder 480B-A35B Instruct
开源权重基础大模型Alibaba 推出的 Qwen3-Coder 480B-A35B Instruct 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 262K 上下文窗口、52 Token/秒生成速度与 86/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
56.5%
核心差异一览
- 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%).
| Feature | 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 |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5-Codex · Strengths & Key Advantages
- 提供 400K 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 74.5% · GPQA Diamond 80.5%）
- 高达 120 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 1250 毫秒
- 极具竞争力的 API 定价（每百万 Token $1.25 输入 / $10 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- 提供 262K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 56.5% · GPQA Diamond 70%）
- 高达 52 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 760 毫秒
- 开放模型权重 (480B total (35B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
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 |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: 常见问题解答 (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.
综合结论
综合来看，GPT-5-Codex（适合：专为自主软件工程、跨文件代码重构与复杂调试打造的旗舰编程任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Qwen3-Coder 480B-A35B Instruct（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Qwen3-Coder 480B-A35B Instruct 在综合智能与编程基准测试中表现更强，GPT-5-Codex 拥有更快的 Token 生成速度，而 Qwen3-Coder 480B-A35B Instruct 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，GPT-5-Codex 和 Qwen3-Coder 480B-A35B Instruct 哪个更强？+
在综合智能指数与编程基准评测中，Qwen3-Coder 480B-A35B Instruct 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
GPT-5-Codex 与 Qwen3-Coder 480B-A35B Instruct 的 API 价格哪个更便宜？+
GPT-5-Codex 的 API 价格为输入 $1.25 / 输出 $10.00（每百万 Token），Qwen3-Coder 480B-A35B Instruct 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Qwen3-Coder 480B-A35B Instruct 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
GPT-5-Codex 支持 400K Token 上下文窗口，而 Qwen3-Coder 480B-A35B Instruct 支持 262.1K Token。在流式生成速度方面，GPT-5-Codex 的吞吐表现更快。
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.
数据来源: OpenAI 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。