DBRX Instruct vs ERNIE Speed
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DBRX Instruct dari Databricks dan ERNIE Speed dari Baidu adalah dua model AI unggulan. DBRX Instruct menawarkan jendela konteks 32.8K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan ERNIE Speed menyediakan konteks 128K token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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DBRX Instruct
Model Dasar Bobot TerbukaDBRX Instruct dari Databricks adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 33K, kecepatan 75 token/detik, indeks kecerdasan 76/100, serta akses gra
$0.00 / 1M
$0.00 / 1M
32.8K tokens
N/A
ERNIE Speed
Inferensi Super CepatERNIE Speed dari Baidu adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 128K, kecepatan 175 token/detik, indeks kecerdasan 68/100, serta akses g
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
Perbedaan Utama Sekilas
- Both models cost the same under this workload — choose on capability, not price.
- ERNIE Speed has a larger context window (128K vs 33K tokens).
- ERNIE Speed is faster at 175 tok/s vs 75 tok/s.
| Feature | DBRX Instruct | ERNIE Speed |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 68 |
Provider | Databricks | Baidu |
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 | 32.8K tokens~49 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 75 tok/s | 175 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
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.
DBRX Instruct · Strengths & Key Advantages
- Jendela konteks 33K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 74% · Arena Elo 1265)
- Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 480 ms
- Bobot terbuka (132B total (36B 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
ERNIE Speed · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 64% · Arena Elo 1205)
- Kecepatan generasi tinggi 175 token/detik dengan latensi token pertama (TTFT) hanya 230 ms
- Bobot terbuka (Confidential / 8B Scale) 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 | DBRX Instruct | ERNIE Speed |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 14K | 8K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | DBRX Instruct | ERNIE Speed |
|---|---|---|
| Official API Model ID (SDK) | dbrx-instruct | ernie-speed |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-03 | 2024-03 |
| Knowledge Cutoff | December 2023 | December 2023 |
| Architecture | Fine-Grained Sparse Mixture-of-Experts Transformer | Distilled High-Throughput Transformer |
| Parameters | 132B total (36B active MoE) | Confidential / 8B Scale |
| 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 | 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) · 82 GB VRAM (4-bit Quantized) · 284 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 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 |
DBRX Instruct vs ERNIE Speed: Jawaban Cepat (FAQ)
Which is more intelligent, DBRX Instruct or ERNIE Speed?
DBRX Instruct is more intelligent. It scores 76 compared with 68 on the composite Intelligence Index.
Which model is faster?
ERNIE Speed is faster, generating 175 tokens per second compared with 75 tokens per second.
Which model has lower latency?
ERNIE Speed has lower latency, with a time to first token of 0.23s compared with 0.48s.
Which model has a larger context window?
ERNIE Speed has a larger context window, supporting 128K tokens compared with 32.8K tokens.
Kesimpulan
Baik DBRX Instruct (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun ERNIE Speed (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: DBRX Instruct unggul dalam penalaran dan pemrograman, ERNIE Speed memberikan kecepatan generasi token lebih tinggi, dan DBRX Instruct lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, DBRX Instruct atau ERNIE Speed?+
Dalam indeks kecerdasan gabungan dan benchmark coding, DBRX Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API DBRX Instruct vs ERNIE Speed?+
DBRX Instruct mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan ERNIE Speed sebesar $0.00 / $0.00. Secara keseluruhan, DBRX Instruct lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
DBRX Instruct mendukung konteks 32.8K token dibandingkan 128K token pada ERNIE Speed, sementara ERNIE Speed 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
- DBRX Instruct vs ERNIE Lite: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- ERNIE Speed vs ERNIE Lite: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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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 Databricks & Baidu serta evaluasi benchmark independen (2026).