GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5-Codex di OpenAI e Qwen3-Coder 480B-A35B Instruct di Alibaba sono due modelli di IA di riferimento. GPT-5-Codex offre una finestra di contesto di 400K token a $1.25/$10.00 per milione di token, mentre Qwen3-Coder 480B-A35B Instruct mette a disposizione 262.1K token di contesto a $0.00/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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GPT-5-Codex
Leader in Programmazione e CodiceGPT-5-Codex di OpenAI è un modello di IA progettato per l’ingegneria del software autonoma, il refactoring multi-file e il debug avanzato, con finestra di contesto da 400K, velocità di 120 token/s, indice di intelligenza di 57/100 e tariffa
$1.25 / 1M
$10.00 / 1M
400K tokens
74.5%
Qwen3-Coder 480B-A35B Instruct
Modello Base a Pesi ApertiQwen3-Coder 480B-A35B Instruct di Alibaba è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 262K, velocità di 52 token/s, indice di intelli
$0.00 / 1M
$0.00 / 1M
262.1K tokens
56.5%
Differenze Chiave a Colpo d’Occhio
- 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5-Codex · Strengths & Key Advantages
- Finestra di contesto da 400K con capacità massima di output fino a 128K token (SWE-bench Verified 74.5% · GPQA Diamond 80.5%)
- Throughput elevato di 120 token/secondo con latenza al primo token (TTFT) di soli 1250 ms
- Economia API competitiva ($1.25/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Finestra di contesto da 262K con capacità massima di output fino a 16K token (SWE-bench Verified 56.5% · GPQA Diamond 70%)
- Throughput elevato di 52 token/secondo con latenza al primo token (TTFT) di soli 760 ms
- Pesi aperti (480B total (35B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Sia GPT-5-Codex (Ideale per l’ingegneria del software autonoma, il refactoring multi-file e il debug avanzato, sistemi RAG aziendali e integrazioni API scalabili.) sia Qwen3-Coder 480B-A35B Instruct (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Qwen3-Coder 480B-A35B Instruct guida nei benchmark di ragionamento e programmazione, GPT-5-Codex garantisce una generazione più rapida e Qwen3-Coder 480B-A35B Instruct offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, GPT-5-Codex o Qwen3-Coder 480B-A35B Instruct?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen3-Coder 480B-A35B Instruct. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra GPT-5-Codex e Qwen3-Coder 480B-A35B Instruct?+
GPT-5-Codex costa $1.25 / $10.00 per milione di token di input/output, mentre Qwen3-Coder 480B-A35B Instruct costa $0.00 / $0.00. Nel costo combinato, Qwen3-Coder 480B-A35B Instruct risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
GPT-5-Codex supporta 400K token di contesto contro 262.1K token di Qwen3-Coder 480B-A35B Instruct, mentre GPT-5-Codex offre una velocità di generazione superiore.
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.
Fonti ufficiali: Documentazione API ufficiale di OpenAI e Alibaba, schede di sistema e benchmark indipendenti (2026).