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 da Alibaba e Claude 3.7 Sonnet da Anthropic são dois modelos de IA de destaque. O Qwen3-Coder 480B-A35B Instruct oferece uma janela de contexto de 262.1K tokens a $0.00/$0.00 por milhão de tokens, enquanto o Claude 3.7 Sonnet oferece 200K tokens de contexto a $3.00/$15.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
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Qwen3-Coder 480B-A35B Instruct
Modelo Base de Pesos AbertosO Qwen3-Coder 480B-A35B Instruct da Alibaba é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 262K, velocidade de 52 tokens/s, índice de inteligênc
$0.00 / 1M
$0.00 / 1M
262.1K tokens
56.5%
Claude 3.7 Sonnet
Líder em Programação e CódigoO Claude 3.7 Sonnet da Anthropic é um modelo de IA projetado para engenharia de software autônoma, refatoração de código e depuração avançada, oferecendo janela de contexto de 200K, velocidade de 72 tokens/s, índice de inteligência de 48/10
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
Principais Diferenças em Resumo
- 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 |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Janela de contexto de 262K com limite máximo de saída de 16K tokens (SWE-bench Verified 56.5% · GPQA Diamond 70%)
- Taxa de transferência rápida de 52 tokens/segundo com latência inicial (TTFT) de 760 ms
- Pesos abertos (480B total (35B MoE)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Claude 3.7 Sonnet · Strengths & Key Advantages
- Janela de contexto de 200K com limite máximo de saída de 64K tokens (SWE-bench Verified 70.3% · GPQA Diamond 65%)
- Taxa de transferência rápida de 72 tokens/segundo com latência inicial (TTFT) de 520 ms
- Economia de API competitiva ($3/$15 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
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 |
Especificações Técnicas
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: Respostas Rápidas (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.
Veredito
Tanto Qwen3-Coder 480B-A35B Instruct (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Claude 3.7 Sonnet (Indicado para engenharia de software autônoma, refatoração de código e depuração avançada, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Qwen3-Coder 480B-A35B Instruct lidera em inteligência geral e programação, Claude 3.7 Sonnet oferece geração mais rápida e Qwen3-Coder 480B-A35B Instruct apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Qwen3-Coder 480B-A35B Instruct ou Claude 3.7 Sonnet?+
Nas avaliações de inteligência e código, Qwen3-Coder 480B-A35B Instruct leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Qwen3-Coder 480B-A35B Instruct ou Claude 3.7 Sonnet?+
O Qwen3-Coder 480B-A35B Instruct custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Claude 3.7 Sonnet custa $3.00 / $15.00. No custo combinado, Qwen3-Coder 480B-A35B Instruct é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Qwen3-Coder 480B-A35B Instruct suporta 262.1K tokens de contexto contra 200K tokens do Claude 3.7 Sonnet, enquanto Claude 3.7 Sonnet entrega maior velocidade de geração.
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: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Claude 3.7 Sonnet vs. GPT-4o: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Claude 3.7 Sonnet vs. o3-mini: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Claude Sonnet 4 vs. Claude 3.7 Sonnet: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Devstral 2 vs. Qwen3-Coder 480B-A35B Instruct: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Gemini 2.0 Pro vs. Claude 3.7 Sonnet: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
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.
Fontes oficiais: Documentação oficial de API da Alibaba e Anthropic e benchmarks independentes (2026).