Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen 2.5 32B Instruct dari Alibaba dan Qwen 2.5 7B Instruct dari Alibaba adalah dua model AI unggulan. Qwen 2.5 32B Instruct menawarkan jendela konteks 131.1K token dengan harga $0.18/$0.36 per 1 juta token, sedangkan Qwen 2.5 7B Instruct menyediakan konteks 131.1K token seharga $0.06/$0.12 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
Ganti & Bandingkan Model Apa Saja
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Qwen 2.5 32B Instruct
Model Dasar Bobot TerbukaQwen 2.5 32B Instruct dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 85 token/detik, indeks kecerdasan 41/100, serta tar
$0.18 / 1M
$0.36 / 1M
131.1K tokens
40.8%
Qwen 2.5 7B Instruct
Model Ringan & Hemat BiayaQwen 2.5 7B Instruct dari Alibaba adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 131K, kecepatan 130 token/detik, indeks kecerdasan 35/100, serta
$0.06 / 1M
$0.12 / 1M
131.1K tokens
28.5%
Perbedaan Utama Sekilas
- Qwen 2.5 7B Instruct is 3.0x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 7B Instruct is faster at 130 tok/s vs 85 tok/s.
- Qwen 2.5 32B Instruct leads on SWE-bench coding (40.8% vs 28.5%).
| Feature | Qwen 2.5 32B Instruct | Qwen 2.5 7B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 41 | 35 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.18 / 1M | $0.06 / 1M |
Output Price Cost per million output tokens | $0.36 / 1M | $0.12 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 85 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 40.8% | 28.5% |
GPQA Diamond Graduate-level reasoning | 48.5% | 36.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.01 / 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.
Qwen 2.5 32B Instruct · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
- Kecepatan generasi tinggi 85 token/detik dengan latensi token pertama (TTFT) hanya 480 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
Qwen 2.5 7B Instruct · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 28.5% · GPQA Diamond 36.8%)
- Kecepatan generasi tinggi 130 token/detik dengan latensi token pertama (TTFT) hanya 290 ms
- Bobot terbuka (7B) 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 | Qwen 2.5 32B Instruct | Qwen 2.5 7B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.16 | $0.05 |
Output Tokens / Task Tokens generated per task | 28K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen 2.5 32B Instruct | Qwen 2.5 7B Instruct |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-32b-instruct | qwen-2-5-7b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-09 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Dense Autoregressive Transformer | Dense Autoregressive Transformer |
| Parameters | 32B | 7B |
| 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) | Flat rate ($0.18/1M in) across full 131K window | Flat rate ($0.06/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.036/1M tokens | Cached input at $0.012/1M 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) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 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 |
Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen 2.5 32B Instruct or Qwen 2.5 7B Instruct?
Qwen 2.5 32B Instruct is more intelligent. It scores 41 compared with 35 on the composite Intelligence Index.
Which model is faster?
Qwen 2.5 7B Instruct is faster, generating 130 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s compared with 0.48s.
Kesimpulan
Baik Qwen 2.5 32B 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 7B Instruct (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen 2.5 32B Instruct unggul dalam penalaran dan pemrograman, Qwen 2.5 7B Instruct memberikan kecepatan generasi token lebih tinggi, dan Qwen 2.5 7B Instruct lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Qwen 2.5 32B Instruct atau Qwen 2.5 7B Instruct?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen 2.5 32B Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct?+
Qwen 2.5 32B Instruct mengenakan biaya $0.18 / $0.36 per 1M token input/output, sedangkan Qwen 2.5 7B Instruct sebesar $0.06 / $0.12. Secara keseluruhan, Qwen 2.5 7B Instruct lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Qwen 2.5 32B Instruct mendukung konteks 131.1K token dibandingkan 131.1K token pada Qwen 2.5 7B Instruct, sementara Qwen 2.5 7B 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.
Related reading
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Llama 3.2 1B vs Qwen 2.5 32B Instruct: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Pixtral 12B vs Qwen 2.5 32B Instruct: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Qwen 2.5 72B vs Qwen 2.5 32B Instruct: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-3.0 vs Aion-Mini: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Aion-3.0 vs Gemini 3.6 Flash: 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 & Alibaba serta evaluasi benchmark independen (2026).