Gemma 4 26B A4B vs Qwen3.6 27B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemma 4 26B A4B dari Google dan Qwen3.6 27B dari Alibaba adalah dua model AI unggulan. Gemma 4 26B A4B menawarkan jendela konteks 262.1K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan Qwen3.6 27B 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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Gemma 4 26B A4B
Model Dasar Bobot TerbukaGemma 4 26B A4B dari Google adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 160 token/detik, indeks kecerdasan 74/100, serta akses gra
$0.00 / 1M
$0.00 / 1M
262.1K tokens
61%
Qwen3.6 27B
AI Multimodal AsliQwen3.6 27B dari Alibaba adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 262K, kecepatan 92 token/detik, indeks kecerdasan 77/100, serta akses grat
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Perbedaan Utama Sekilas
- Both models cost the same under this workload — choose on capability, not price.
- Gemma 4 26B A4B is faster at 160 tok/s vs 92 tok/s.
| Feature | Gemma 4 26B A4B | Qwen3.6 27B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 74 | 77 |
Provider | Alibaba | |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 33K | 8K |
Throughput Speed Tokens generated per second | 160 tok/s | 92 tok/s |
SWE-bench Verified Real-world software engineering | 61% | N/A |
GPQA Diamond Graduate-level reasoning | 71.5% | 58% |
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.
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.
Gemma 4 26B A4B · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 61% · GPQA Diamond 71.5%)
- Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 290 ms
- Bobot terbuka (26B (4B 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
Qwen3.6 27B · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 58% · MMLU-Pro 75.5%)
- Kecepatan generasi tinggi 92 token/detik dengan latensi token pertama (TTFT) hanya 390 ms
- Bobot terbuka (27B) 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 | Gemma 4 26B A4B | Qwen3.6 27B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 8K | 14K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 3K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 4 26B A4B | Qwen3.6 27B |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-26b-a4b | qwen3-6-27b-instruct |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | January 2026 | February 2026 |
| Architecture | Sparse Mixture-of-Experts open transformer (26B total, 4B active) | Refined Dense Multimodal Transformer |
| Parameters | 26B (4B active MoE) | 27B |
| 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) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | 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) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 16 GB VRAM (4-bit Quantized) · 56 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 17 GB VRAM (4-bit Quantized) · 58 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | 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, vision |
| Output Modalities | text | text |
Gemma 4 26B A4B vs Qwen3.6 27B: Jawaban Cepat (FAQ)
Which is more intelligent, Gemma 4 26B A4B or Qwen3.6 27B?
Qwen3.6 27B is more intelligent. It scores 77 compared with 74 on the composite Intelligence Index.
Which model is faster?
Gemma 4 26B A4B is faster, generating 160 tokens per second compared with 92 tokens per second.
Which model has lower latency?
Gemma 4 26B A4B has lower latency, with a time to first token of 0.29s compared with 0.39s.
Kesimpulan
Baik Gemma 4 26B A4B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.6 27B (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.6 27B unggul dalam penalaran dan pemrograman, Gemma 4 26B A4B memberikan kecepatan generasi token lebih tinggi, dan Gemma 4 26B A4B lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Gemma 4 26B A4B atau Qwen3.6 27B?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.6 27B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Gemma 4 26B A4B vs Qwen3.6 27B?+
Gemma 4 26B A4B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Qwen3.6 27B sebesar $0.00 / $0.00. Secara keseluruhan, Gemma 4 26B A4B lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Gemma 4 26B A4B mendukung konteks 262.1K token dibandingkan 262.1K token pada Qwen3.6 27B, sementara Gemma 4 26B A4B 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
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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 Google & Alibaba serta evaluasi benchmark independen (2026).