DBRX Instruct vs ERNIE Speed
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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DBRX Instruct by Databricks and ERNIE Speed by Baidu represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, ERNIE Speed provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, ERNIE Speed leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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DBRX Instruct
Enterprise MoEDatabricks' pioneering 132B open MoE model utilizing 16 fine-grained experts (4 active) for enterprise SQL, coding, and general tasks.
$0.00 / 1M
$0.00 / 1M
32.8K tokens
N/A
ERNIE Speed
Fast BudgetFast, free-tier accessible language model offering rapid response generation for bulk processing and entry-level automation.
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
Key Differences at a Glance
- 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 |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DBRX Instruct · Strengths & Key Advantages
- Pioneered fine-grained 16-expert MoE architecture in commercial open-source models
- Strong out-of-the-box SQL generation and tabular database query capabilities
- Tight native integration with the Databricks Lakehouse platform
Considerations & Trade-offs
- 32K context window is smaller than modern 128K+ alternatives
- Requires substantial GPU VRAM for unquantized cluster hosting
ERNIE Speed · Strengths & Key Advantages
- Free of charge on Baidu Qianfan API for registered developers
- Sub-250ms initial token generation and fast 175 tokens/sec throughput
- Adequate accuracy for standard text summarization and sentiment tagging
Considerations & Trade-offs
- Limited analytical depth on complex technical or scientific problems
- Max output token ceiling of 4096 tokens
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 |
Technical Specifications
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: Quick Answers
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.
Verdict
Verdict & Model Selection Recommendation: Choose DBRX Instruct if your priority is Enterprise data warehouse analytics, automated SQL generation, and lakehouse assistants. Its performance in demanding tasks justifies its operational token cost. Choose ERNIE Speed if your workflow prioritizes Bulk text classification, simple conversational routing, and zero-cost prototyping. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does DBRX Instruct compare to ERNIE Speed in API token pricing and cost per token?+
Both DBRX Instruct and ERNIE Speed are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: DBRX Instruct or ERNIE Speed?+
DBRX Instruct supports a context window of 33K tokens (~25k words), while ERNIE Speed supports 128K tokens (~96k words). Therefore, ERNIE Speed can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: DBRX Instruct or ERNIE Speed?+
DBRX Instruct scores at the frontier of coding benchmarks, while ERNIE Speed scores competitively for software generation. For complex autonomous debugging and agentic engineering, DBRX Instruct provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between DBRX Instruct and ERNIE Speed?+
DBRX Instruct is optimized for low-latency direct generation. In contrast, ERNIE Speed focuses on standard fast inference.
How does inference speed (tokens per second) compare between DBRX Instruct and ERNIE Speed?+
DBRX Instruct generates approximately 75 tokens per second, while ERNIE Speed delivers 175 tokens per second. In latency-sensitive production environments, ERNIE Speed provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DBRX Instruct and ERNIE Speed to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to ERNIE Speed, and route deep architectural or complex reasoning queries to DBRX Instruct. This balanced routing strategy maximizes system throughput and answer quality.
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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs