Pixtral 12B vs Qwen 2.5 32B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Pixtral 12B dari Mistral AI dan Qwen 2.5 32B Instruct dari Alibaba adalah dua model AI unggulan. Pixtral 12B menawarkan jendela konteks 128K token dengan harga $0.15/$0.15 per 1 juta token, sedangkan Qwen 2.5 32B Instruct menyediakan konteks 131.1K token seharga $0.18/$0.36 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Pixtral 12B
AI Multimodal AsliPixtral 12B dari Mistral AI adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 128K, kecepatan 85 token/detik, indeks kecerdasan 40/100, serta tarif $
$0.15 / 1M
$0.15 / 1M
128K tokens
28%
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%
Perbedaan Utama Sekilas
- Pixtral 12B is 1.5x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 32B Instruct has a larger context window (131K vs 128K tokens).
- Qwen 2.5 32B Instruct is faster at 85 tok/s vs 85 tok/s.
- Qwen 2.5 32B Instruct leads on SWE-bench coding (40.8% vs 28.0%).
| Feature | Pixtral 12B | Qwen 2.5 32B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 40 | 41 |
Provider | Mistral AI | Alibaba |
Input Price Cost per million input tokens | $0.15 / 1M | $0.18 / 1M |
Output Price Cost per million output tokens | $0.15 / 1M | $0.36 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 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 | 85 tok/s |
SWE-bench Verified Real-world software engineering | 28% | 40.8% |
GPQA Diamond Graduate-level reasoning | 43.8% | 48.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.04 / 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.
Pixtral 12B · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 28% · GPQA Diamond 43.8%)
- Kecepatan generasi tinggi 85 token/detik dengan latensi token pertama (TTFT) hanya 450 ms
- Bobot terbuka (12B) 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 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Pixtral 12B | Qwen 2.5 32B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.16 |
Output Tokens / Task Tokens generated per task | 25K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Pixtral 12B | Qwen 2.5 32B Instruct |
|---|---|---|
| Official API Model ID (SDK) | pixtral-12b-latest | qwen-2-5-32b-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 | August 2024 | June 2024 |
| Architecture | Multimodal Vision-Language Transformer | Dense Autoregressive Transformer |
| Parameters | 12B | 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) | Flat rate ($0.15/1M in) across full 128K window | Flat rate ($0.18/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Cached input at $0.036/1M 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 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, vision | text |
| Output Modalities | text | text |
Pixtral 12B vs Qwen 2.5 32B Instruct: Jawaban Cepat (FAQ)
Which is more intelligent, Pixtral 12B or Qwen 2.5 32B Instruct?
Qwen 2.5 32B Instruct is more intelligent. It scores 41 compared with 40 on the composite Intelligence Index.
Which model is cheaper to run?
Pixtral 12B is cheaper at $0.08 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Pixtral 12B has lower latency, with a time to first token of 0.45s compared with 0.48s.
Which model has a larger context window?
Qwen 2.5 32B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Kesimpulan
Baik Pixtral 12B (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun 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.) sangat tangguh: Qwen 2.5 32B Instruct unggul dalam penalaran dan pemrograman, Pixtral 12B memberikan kecepatan generasi token lebih tinggi, dan Pixtral 12B lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Pixtral 12B atau Qwen 2.5 32B 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 Pixtral 12B vs Qwen 2.5 32B Instruct?+
Pixtral 12B mengenakan biaya $0.15 / $0.15 per 1M token input/output, sedangkan Qwen 2.5 32B Instruct sebesar $0.18 / $0.36. Secara keseluruhan, Pixtral 12B lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Pixtral 12B mendukung konteks 128K token dibandingkan 131.1K token pada Qwen 2.5 32B Instruct, sementara Pixtral 12B 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
- Llama 3.2 1B vs Qwen 2.5 32B Instruct: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Pixtral 12B vs Mistral Large 2: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Pixtral Large vs Pixtral 12B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Qwen 2.5 32B Instruct vs Qwen 2.5 7B 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
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 Mistral AI & Alibaba serta evaluasi benchmark independen (2026).