Qwen3-Coder 480B-A35B Instruct vs Claude 3.7 Sonnet
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3-Coder 480B-A35B Instruct di Alibaba e Claude 3.7 Sonnet di Anthropic sono due modelli di IA di riferimento. Qwen3-Coder 480B-A35B Instruct offre una finestra di contesto di 262.1K token a $0.00/$0.00 per milione di token, mentre Claude 3.7 Sonnet mette a disposizione 200K token di contesto a $3.00/$15.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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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%
Claude 3.7 Sonnet
Leader in Programmazione e CodiceClaude 3.7 Sonnet di Anthropic 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 200K, velocità di 72 token/s, indice di intelligenza di 48/100 e
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
Differenze Chiave a Colpo d’Occhio
- Qwen3-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3-Coder 480B-A35B Instruct has a larger context window (262K vs 200K tokens).
- Claude 3.7 Sonnet is faster at 72 tok/s vs 52 tok/s.
- Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 56.5%).
| Feature | Qwen3-Coder 480B-A35B Instruct | Claude 3.7 Sonnet |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 48 |
Provider | Alibaba | Anthropic |
Input Price Cost per million input tokens | $0.00 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $2.31 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 16K | 64K |
Throughput Speed Tokens generated per second | 52 tok/s | 72 tok/s |
SWE-bench Verified Real-world software engineering | 56.5% | 70.3% |
GPQA Diamond Graduate-level reasoning | 70% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.30 / 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.
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
Claude 3.7 Sonnet · Strengths & Key Advantages
- Finestra di contesto da 200K con capacità massima di output fino a 64K token (SWE-bench Verified 70.3% · GPQA Diamond 65%)
- Throughput elevato di 72 token/secondo con latenza al primo token (TTFT) di soli 520 ms
- Economia API competitiva ($3/$15 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
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3-Coder 480B-A35B Instruct | Claude 3.7 Sonnet |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $1.42 |
Output Tokens / Task Tokens generated per task | 25K | 41K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 22K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3-Coder 480B-A35B Instruct | Claude 3.7 Sonnet |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-480b-a35b-instruct | claude-3-7-sonnet-20250201 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-04 | 2025-02 |
| Knowledge Cutoff | March 2025 | November 2024 |
| Architecture | Code-Specialized Sparse Mixture-of-Experts Transformer | Hybrid Reasoning MoE |
| Parameters | 480B total (35B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | $0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 298 GB VRAM (4-bit Quantized) · 1032 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Qwen3-Coder 480B-A35B Instruct vs Claude 3.7 Sonnet: Risposte Rapide (FAQ)
Which is more intelligent, Qwen3-Coder 480B-A35B Instruct or Claude 3.7 Sonnet?
Qwen3-Coder 480B-A35B Instruct is more intelligent. It scores 86 compared with 48 on the composite Intelligence Index.
Which model is faster?
Claude 3.7 Sonnet is faster, generating 72 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 $2.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude 3.7 Sonnet has lower latency, with a time to first token of 0.52s compared with 0.76s.
Which model has a larger context window?
Qwen3-Coder 480B-A35B Instruct has a larger context window, supporting 262.1K tokens compared with 200K tokens.
Verdetto
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.) sia Claude 3.7 Sonnet (Ideale per l’ingegneria del software autonoma, il refactoring multi-file e il debug avanzato, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Qwen3-Coder 480B-A35B Instruct guida nei benchmark di ragionamento e programmazione, Claude 3.7 Sonnet 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, Qwen3-Coder 480B-A35B Instruct o Claude 3.7 Sonnet?+
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 Qwen3-Coder 480B-A35B Instruct e Claude 3.7 Sonnet?+
Qwen3-Coder 480B-A35B Instruct costa $0.00 / $0.00 per milione di token di input/output, mentre Claude 3.7 Sonnet costa $3.00 / $15.00. Nel costo combinato, Qwen3-Coder 480B-A35B Instruct risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Qwen3-Coder 480B-A35B Instruct supporta 262.1K token di contesto contro 200K token di Claude 3.7 Sonnet, mentre Claude 3.7 Sonnet 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.
Related reading
- Claude 3.7 Sonnet vs DeepSeek R1: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Claude 3.7 Sonnet vs GPT-4o: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Claude 3.7 Sonnet vs o3-mini: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Claude Sonnet 4 vs Claude 3.7 Sonnet: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Devstral 2 vs Qwen3-Coder 480B-A35B Instruct: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Gemini 2.0 Pro vs Claude 3.7 Sonnet: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
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 Alibaba e Anthropic, schede di sistema e benchmark indipendenti (2026).