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 dari Alibaba dan Claude 3.7 Sonnet dari Anthropic adalah dua model AI unggulan. Qwen3-Coder 480B-A35B Instruct menawarkan jendela konteks 262.1K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan Claude 3.7 Sonnet menyediakan konteks 200K token seharga $3.00/$15.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Qwen3-Coder 480B-A35B Instruct
Model Dasar Bobot TerbukaQwen3-Coder 480B-A35B Instruct dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 52 token/detik, indeks kecerdasan 86/100,
$0.00 / 1M
$0.00 / 1M
262.1K tokens
56.5%
Claude 3.7 Sonnet
Pemimpin Pemrograman & KodeClaude 3.7 Sonnet dari Anthropic adalah model AI yang dirancang untuk rekayasa perangkat lunak otonom, refactoring multi-file, dan debugging mendalam, menghadirkan jendela konteks 200K, kecepatan 72 token/detik, indeks kecerdasan 48/100, se
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 56.5% · GPQA Diamond 70%)
- Kecepatan generasi tinggi 52 token/detik dengan latensi token pertama (TTFT) hanya 760 ms
- Bobot terbuka (480B total (35B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Claude 3.7 Sonnet · Strengths & Key Advantages
- Jendela konteks 200K dengan kapasitas output maksimum hingga 64K token (SWE-bench Verified 70.3% · GPQA Diamond 65%)
- Kecepatan generasi tinggi 72 token/detik dengan latensi token pertama (TTFT) hanya 520 ms
- Skema harga API kompetitif (tarif $3/$15 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik Qwen3-Coder 480B-A35B Instruct (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Claude 3.7 Sonnet (Sangat cocok untuk rekayasa perangkat lunak otonom, refactoring multi-file, dan debugging mendalam, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3-Coder 480B-A35B Instruct unggul dalam penalaran dan pemrograman, Claude 3.7 Sonnet memberikan kecepatan generasi token lebih tinggi, dan Qwen3-Coder 480B-A35B Instruct lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Qwen3-Coder 480B-A35B Instruct atau Claude 3.7 Sonnet?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3-Coder 480B-A35B Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Qwen3-Coder 480B-A35B Instruct vs Claude 3.7 Sonnet?+
Qwen3-Coder 480B-A35B Instruct mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Claude 3.7 Sonnet sebesar $3.00 / $15.00. Secara keseluruhan, Qwen3-Coder 480B-A35B Instruct lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen3-Coder 480B-A35B Instruct mendukung konteks 262.1K token dibandingkan 200K token pada Claude 3.7 Sonnet, sementara Claude 3.7 Sonnet unggul dalam kecepatan output token per detik.
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: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Claude 3.7 Sonnet vs GPT-4o: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Claude 3.7 Sonnet vs o3-mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Claude Sonnet 4 vs Claude 3.7 Sonnet: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Devstral 2 vs Qwen3-Coder 480B-A35B Instruct: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Gemini 2.0 Pro vs Claude 3.7 Sonnet: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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.
Sumber resmi: Dokumentasi API resmi Alibaba & Anthropic serta evaluasi benchmark independen (2026).