Qwen3-Coder 30B-A3B Instruct vs Qwen 2.5 Coder 32B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3-Coder 30B-A3B Instruct dari Alibaba dan Qwen 2.5 Coder 32B dari Alibaba adalah dua model AI unggulan. Qwen3-Coder 30B-A3B Instruct menawarkan jendela konteks 262.1K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan Qwen 2.5 Coder 32B menyediakan konteks 128K token seharga $0.20/$0.60 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Qwen3-Coder 30B-A3B Instruct
Model Dasar Bobot TerbukaQwen3-Coder 30B-A3B 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 140 token/detik, indeks kecerdasan 76/100, s
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Qwen 2.5 Coder 32B
Spesialis Kode Open-WeightsQwen 2.5 Coder 32B dari Alibaba adalah model AI yang dirancang untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), menghadirkan jendela konteks 128K, kecepatan 78 token/detik, indeks kecerdasan 43/100,
$0.20 / 1M
$0.60 / 1M
128K tokens
48.6%
Perbedaan Utama Sekilas
- Qwen3-Coder 30B-A3B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3-Coder 30B-A3B Instruct has a larger context window (262K vs 128K tokens).
- Qwen3-Coder 30B-A3B Instruct is faster at 140 tok/s vs 78 tok/s.
| Feature | Qwen3-Coder 30B-A3B Instruct | Qwen 2.5 Coder 32B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 43 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.20 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.13 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 140 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 48.6% |
GPQA Diamond Graduate-level reasoning | N/A | 48.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.04 / 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.
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 30B-A3B Instruct · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 74% · Arena Elo 1285)
- Kecepatan generasi tinggi 140 token/detik dengan latensi token pertama (TTFT) hanya 280 ms
- Bobot terbuka (30B total (3B 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
Qwen 2.5 Coder 32B · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 48.6% · GPQA Diamond 48.2%)
- Kecepatan generasi tinggi 78 token/detik dengan latensi token pertama (TTFT) hanya 540 ms
- Bobot terbuka (32B) 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 30B-A3B Instruct | Qwen 2.5 Coder 32B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.18 |
Output Tokens / Task Tokens generated per task | 15K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3-Coder 30B-A3B Instruct | Qwen 2.5 Coder 32B |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-30b-a3b-instruct | qwen-2-5-coder-32b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2024-11 |
| Knowledge Cutoff | March 2025 | September 2024 |
| Architecture | Compact Sparse Mixture-of-Experts Transformer | Dense Code-Specialized Transformer |
| Parameters | 30B total (3B active MoE) | 32B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.2/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.04/1M tokens |
| 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Qwen3-Coder 30B-A3B Instruct vs Qwen 2.5 Coder 32B: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen3-Coder 30B-A3B Instruct or Qwen 2.5 Coder 32B?
Qwen3-Coder 30B-A3B Instruct is more intelligent. It scores 76 compared with 43 on the composite Intelligence Index.
Which model is faster?
Qwen3-Coder 30B-A3B Instruct is faster, generating 140 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Qwen3-Coder 30B-A3B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $0.13 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3-Coder 30B-A3B Instruct has lower latency, with a time to first token of 0.28s compared with 0.54s.
Which model has a larger context window?
Qwen3-Coder 30B-A3B Instruct has a larger context window, supporting 262.1K tokens compared with 128K tokens.
Kesimpulan
Baik Qwen3-Coder 30B-A3B Instruct (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen 2.5 Coder 32B (Sangat cocok untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3-Coder 30B-A3B Instruct unggul dalam penalaran dan pemrograman, Qwen3-Coder 30B-A3B Instruct memberikan kecepatan generasi token lebih tinggi, dan Qwen3-Coder 30B-A3B Instruct lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Qwen3-Coder 30B-A3B Instruct atau Qwen 2.5 Coder 32B?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3-Coder 30B-A3B Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Qwen3-Coder 30B-A3B Instruct vs Qwen 2.5 Coder 32B?+
Qwen3-Coder 30B-A3B Instruct mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen 2.5 Coder 32B sebesar $0.20 / $0.60. Secara keseluruhan, Qwen3-Coder 30B-A3B Instruct lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen3-Coder 30B-A3B Instruct mendukung konteks 262.1K token dibandingkan 128K token pada Qwen 2.5 Coder 32B, sementara Qwen3-Coder 30B-A3B Instruct 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).