GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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.
Changez et Comparez les Modèles
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GPT-5-Codex
Spécialiste du codageUne variante de GPT-5 optimisée pour les modifications de dépôts, la revue de code et le travail logiciel agentique.
$1.25 / 1M
$10.00 / 1M
400K tokens
74.5%
Qwen3-Coder 480B-A35B Instruct
Géant du code open sourcemassif ouvert codage champion trained specifically sur sur 7 billions tokens de code, dépôt graphs, et technique documentation.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
56.5%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5-Codex · Strengths & Key Advantages
- Codage agentique de premier ordre dans Codex et Copilot
- Conçu pour de longues sessions d'édition à l'échelle du dépôt
- Même tarification que GPT-5 malgré l'optimisation pour le code
- Excellent pour les réparations pilotées par les tests et le débogage itératif
Considerations & Trade-offs
- Moins naturel que GPT-5 en conversation générale
- Excessif pour les charges de travail hors code
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Sweeping génération de code capacités rivaling propriétaire codage leaders
- natif compréhension de multirepo dependencies, git diffs, et linting rules
- 262K fenêtre de contexte allows loading substantiel logiciel codebases directly
Considerations & Trade-offs
- substantiel VRAM requirements necessitate dédié multi-accelerator clusters
- Peut parfois surcharger inutilement des requêtes simples de fonctions utilitaires
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (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.
Verdict
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.
Questions fréquentes
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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs