Qwen3 Coder Flash vs Qwen3-Coder 480B-A35B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3 Coder Flash dari Alibaba dan Qwen3-Coder 480B-A35B Instruct dari Alibaba adalah dua model AI unggulan. Qwen3 Coder Flash menawarkan jendela konteks 1M token dengan harga $0.10/$0.40 per 1 juta token, sedangkan Qwen3-Coder 480B-A35B Instruct menyediakan konteks 262.1K token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Qwen3 Coder Flash
Inferensi Super CepatQwen3 Coder Flash dari Alibaba adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 1M, kecepatan 130 token/detik, indeks kecerdasan 77/100, serta t
$0.10 / 1M
$0.40 / 1M
1M tokens
N/A
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%
Perbedaan Utama Sekilas
- Qwen3-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3 Coder Flash has a larger context window (1M vs 262K tokens).
- Qwen3 Coder Flash is faster at 130 tok/s vs 52 tok/s.
| Feature | Qwen3 Coder Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 77 | 86 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 130 tok/s | 52 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 56.5% |
GPQA Diamond Graduate-level reasoning | N/A | 70% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor 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.
Contamination-free competitive programming problems collected continuously over time.
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 Flash · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 74.5% · Arena Elo 1265)
- Kecepatan generasi tinggi 130 token/detik dengan latensi token pertama (TTFT) hanya 330 ms
- Skema harga API kompetitif (tarif $0.1/$0.4 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
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3 Coder Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $0.00 |
Output Tokens / Task Tokens generated per task | 15K | 25K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 9K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3 Coder Flash | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-flash-instruct | qwen3-coder-480b-a35b-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-07 | 2025-04 |
| Knowledge Cutoff | May 2025 | March 2025 |
| Architecture | Optimized Lightweight Hosted Code Transformer | Code-Specialized Sparse Mixture-of-Experts Transformer |
| Parameters | Confidential | 480B total (35B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.01/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 298 GB VRAM (4-bit Quantized) · 1032 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Alibaba Production Safety & Alignment Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Qwen3 Coder Flash vs Qwen3-Coder 480B-A35B Instruct: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen3 Coder Flash or Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is more intelligent. It scores 86 compared with 77 on the composite Intelligence Index.
Which model is faster?
Qwen3 Coder Flash is faster, generating 130 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 $0.07 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3 Coder Flash has lower latency, with a time to first token of 0.33s compared with 0.76s.
Which model has a larger context window?
Qwen3 Coder Flash has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Kesimpulan
Baik Qwen3 Coder Flash (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun 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.) sangat tangguh: Qwen3-Coder 480B-A35B Instruct unggul dalam penalaran dan pemrograman, Qwen3 Coder Flash 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 Flash atau Qwen3-Coder 480B-A35B Instruct?+
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 Flash vs Qwen3-Coder 480B-A35B Instruct?+
Qwen3 Coder Flash mengenakan biaya $0.10 / $0.40 per 1M token input/output, sedangkan Qwen3-Coder 480B-A35B Instruct sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3-Coder 480B-A35B Instruct lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen3 Coder Flash mendukung konteks 1M token dibandingkan 262.1K token pada Qwen3-Coder 480B-A35B Instruct, sementara Qwen3 Coder Flash 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.
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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 & Alibaba serta evaluasi benchmark independen (2026).