Qwen3-Coder 480B-A35B Instruct vs Claude 3.7 Sonnet
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Qwen3-Coder 480B-A35B Instruct by Alibaba and Claude 3.7 Sonnet by Anthropic 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, Qwen3-Coder 480B-A35B Instruct provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Claude 3.7 Sonnet 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.
Cambia y Compara Cualquier Modelo
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Qwen3-Coder 480B-A35B Instruct
Gigante de código abiertomasivo abierto codificación champion trained specifically en sobre 7 billones tokens de code, repositorio graphs, y técnico documentation.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
56.5%
Claude 3.7 Sonnet
Codificación y razonamiento de fronteraEl modelo de frontera híbrido de Anthropic que proporciona respuestas instantáneas o razonamiento extendido mediante cadena de pensamiento, estableciendo récords en ingeniería de software.
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
Diferencias Clave de un Vistazo
- 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%).
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones 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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Sweeping generación de código capacidades rivaling propietario codificación leaders
- nativo comprensión de multirepo dependencies, git diffs, y linting rules
- 262K ventana de contexto allows loading sustancial software codebases directly
Considerations & Trade-offs
- sustancial VRAM requirements necessitate dedicado multi-accelerator clústeres
- Ocasionalmente puede sobrecomplicar solicitudes de funciones de utilidad simples
Claude 3.7 Sonnet · Strengths & Key Advantages
- mundo-leading SWE-bench Verified codificación capacidad (70.3%)
- Dynamic razonamiento económico slider (estándar vs pensamiento modo)
- Extremely grande salida generación (up un 64K tokens por turn)
- 90% discount en cached entrada prompts ($0.30 por 1M tokens)
Considerations & Trade-offs
- mayor salida precio ($15/1M) en comparación con ligero utility modelos
- No nativo audio entrada (requiere audio-un-texto preprocessing)
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas 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.
Veredicto
Verdict & Model Selection Recommendation: Choose Qwen3-Coder 480B-A35B Instruct if your priority is Enterprise software development agents, automated code migration, and security vulnerability auditing. Its performance in demanding tasks justifies its operational token cost. Choose Claude 3.7 Sonnet if your workflow prioritizes Full-stack software architecture, complex refactoring, and agentic workflows. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does Qwen3-Coder 480B-A35B Instruct compare to Claude 3.7 Sonnet in API token pricing and cost per token?+
Qwen3-Coder 480B-A35B Instruct costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Claude 3.7 Sonnet costs $3.00 input and $15.00 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: Qwen3-Coder 480B-A35B Instruct or Claude 3.7 Sonnet?+
Qwen3-Coder 480B-A35B Instruct supports a context window of 262K tokens (~197k words), while Claude 3.7 Sonnet supports 200K tokens (~150k words). Therefore, Qwen3-Coder 480B-A35B Instruct can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3-Coder 480B-A35B Instruct or Claude 3.7 Sonnet?+
Qwen3-Coder 480B-A35B Instruct scores 56.5% on SWE-bench Verified while Claude 3.7 Sonnet scores 70.3%. For autonomous software engineering and multi-file debugging, Claude 3.7 Sonnet holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3-Coder 480B-A35B Instruct and Claude 3.7 Sonnet?+
Qwen3-Coder 480B-A35B Instruct features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude 3.7 Sonnet provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3-Coder 480B-A35B Instruct and Claude 3.7 Sonnet?+
Qwen3-Coder 480B-A35B Instruct generates approximately 52 tokens per second, while Claude 3.7 Sonnet delivers 72 tokens per second. In latency-sensitive production environments, Claude 3.7 Sonnet provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3-Coder 480B-A35B Instruct and Claude 3.7 Sonnet 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 Claude 3.7 Sonnet. 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs