massif ouvert codage champion trained specifically sur sur 7 billions tokens de code, dépôt graphs, et technique documentation.
Technical Specifications
- Provider
- Alibaba
- Category
- open-source
- Release Date
- 2025-04
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- qwen3-coder-480b-a35b-instruct
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- 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
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 298 GB VRAM (4-bit Quantized) · 1032 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Qwen3-Coder 480B-A35B Instruct Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Qwen3-Coder 480B-A35B Instruct:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 56.5%
- GPQA Diamond
- 70%
- MMLU-Pro
- 83%
- HumanEval
- 95%
- LiveCodeBench
- 73%
- Chatbot Arena Elo
- 1355pts
Strengths
- 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
Limitations & Trade-offs
- substantiel VRAM requirements necessitate dédié multi-accelerator clusters
- Peut parfois surcharger inutilement des requêtes simples de fonctions utilitaires
Editorial Assessment
le heavyweight souverain champion d'open source ingénierie logicielle.
Best for: d'entreprise logiciel development agents, automatisé code migration, et security vulnerability auditing
Compare Qwen3-Coder 480B-A35B Instruct
Frequently Asked Questions about Qwen3-Coder 480B-A35B Instruct
How much does the Qwen3-Coder 480B-A35B Instruct API cost per 1M tokens?+
Qwen3-Coder 480B-A35B Instruct by Alibaba is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the Qwen3-Coder 480B-A35B Instruct pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Qwen3-Coder 480B-A35B Instruct's context window?+
Qwen3-Coder 480B-A35B Instruct features a 262K tokens context window, which equals approximately 196,608 English words or ~524 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 16,384 tokens.
Can I run Qwen3-Coder 480B-A35B Instruct locally on my own GPU (Open Weights)?+
Yes, Qwen3-Coder 480B-A35B Instruct (480B total (35B active MoE)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Alibaba train on data sent to the Qwen3-Coder 480B-A35B Instruct API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Qwen3-Coder 480B-A35B Instruct perform on coding and reasoning benchmarks?+
Qwen3-Coder 480B-A35B Instruct achieves a composite Intelligence Index of 86/100, scoring 56.5% on SWE-bench Verified, 70% on GPQA Diamond, 83% on MMLU-Pro, and 1355 pts on Chatbot Arena Elo.
What is Qwen3-Coder 480B-A35B Instruct best used for?+
Qwen3-Coder 480B-A35B Instruct is best suited for: d'entreprise logiciel development agents, automatisé code migration, et security vulnerability auditing. Assessment: le heavyweight souverain champion d'open source ingénierie logicielle.
Alibaba Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Alibaba's flagship reasoning tier:
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AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Qwen3-Coder 480B-A35B Instruct:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
Un modèle d'IA entraîné par apprentissage par renforcement pour allouer du temps de calcul à la réflexion logique avant d'émettre une solution, idéal pour les problèmes scientifiques et le code complexe.
Une méthode de raisonnement pas-à-pas où le modèle décompose le problème en étapes logiques intermédiaires avant de conclure, augmentant drastiquement la précision en mathématiques et programmation.
La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.