ERNIE 5.0 vs ERNIE 4.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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ERNIE 5.0 by Baidu and ERNIE 4.5 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, ERNIE 4.5 holds a notable pricing advantage for token ingestion. On output generation, ERNIE 4.5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, ERNIE 4.5 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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ERNIE 5.0
Chinese Culture KingBaidu's 800-billion parameter omnimodal flagship mastering Chinese culture, classical literature, and native 4-modality processing.
$1.50 / 1M
$6.00 / 1M
128K tokens
60%
ERNIE 4.5
Chinese FlagshipBaidu's multimodal Chinese foundation model integrating text, vision, and audio comprehension with deep Chinese cultural knowledge.
$0.60 / 1M
$2.40 / 1M
128K tokens
N/A
Key Differences at a Glance
- ERNIE 4.5 is 2.5x cheaper for API usage (input + output tokens combined).
- ERNIE 4.5 is faster at 75 tok/s vs 70 tok/s.
| Feature | ERNIE 5.0 | ERNIE 4.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 90 | 81 |
Provider | Baidu | Baidu |
Input Price Cost per million input tokens | $1.50 / 1M | $0.60 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $2.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.01 / 1M | $0.40 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 70 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 60% | N/A |
GPQA Diamond Graduate-level reasoning | 74.5% | 60% |
Cached Input Price Prompt caching discount per 1M tokens | $0.15 / 1M | $0.06 / 1M |
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.
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.
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.
ERNIE 5.0 · Strengths & Key Advantages
- Unsurpassed mastery of Chinese cultural idioms, classical poetry, and legal history
- Comprehensive 4-modality comprehension including native video and audio ingestion
- High 1430 LMSYS Arena rating placing it among premier global systems
Considerations & Trade-offs
- Limited international developer community and English documentation
- Context window remains 128K compared to million-token competitors
ERNIE 4.5 · Strengths & Key Advantages
- State-of-the-art Chinese language understanding and cultural knowledge nuance
- Multimodal ingestion handling text, audio recordings, and visual imagery
- Seamless integration with Baidu Qianfan enterprise AI platform
Considerations & Trade-offs
- English performance lags behind top American frontier models
- Context window limited to 128K tokens
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | ERNIE 5.0 | ERNIE 4.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.32 | $0.16 |
Output Tokens / Task Tokens generated per task | 25K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 4K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | ERNIE 5.0 | ERNIE 4.5 |
|---|---|---|
| Official API Model ID (SDK) | ernie-5-0 | ernie-4-5 |
| API Protocol | Baidu REST API (OpenAI-Compatible) | Baidu REST API (OpenAI-Compatible) |
| Release Date | 2026-05 | 2025-03 |
| Knowledge Cutoff | February 2026 | December 2024 |
| Architecture | Colossus-Scale Omnimodal MoE Transformer | Multimodal Foundation Knowledge Transformer |
| Parameters | 800B total (45B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.5/1M in) across full 128K window | Flat rate ($0.6/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.15/1M tokens | Cached input at $0.06/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Baidu Production Safety & Alignment Verified | Baidu Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio, video | text, vision, audio |
| Output Modalities | text | text |
ERNIE 5.0 vs ERNIE 4.5: Quick Answers
Which is more intelligent, ERNIE 5.0 or ERNIE 4.5?
ERNIE 5.0 is more intelligent. It scores 90 compared with 81 on the composite Intelligence Index.
Which model is faster?
ERNIE 4.5 is faster, generating 75 tokens per second compared with 70 tokens per second.
Which model is cheaper to run?
ERNIE 4.5 is cheaper at $0.40 per 1M tokens blended, compared with $1.01 (7:2:1 cache:input:output ratio).
Which model has lower latency?
ERNIE 4.5 has lower latency, with a time to first token of 0.51s compared with 0.58s.
Verdict
Verdict & Model Selection Recommendation: Choose ERNIE 5.0 if your priority is Chinese creative arts, cultural education, national enterprise intelligence, and multimedia QA. Its performance in demanding tasks justifies its operational token cost. Choose ERNIE 4.5 if your workflow prioritizes Chinese enterprise applications, local compliance and legal review, and multimodal knowledge search. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does ERNIE 5.0 compare to ERNIE 4.5 in API token pricing and cost per token?+
ERNIE 5.0 costs $1.50 per 1M input tokens and $6.00 per 1M output tokens, whereas ERNIE 4.5 costs $0.60 input and $2.40 output. Overall, ERNIE 4.5 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: ERNIE 5.0 or ERNIE 4.5?+
Both ERNIE 5.0 and ERNIE 4.5 offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: ERNIE 5.0 or ERNIE 4.5?+
ERNIE 5.0 scores 60% on SWE-bench Verified, while ERNIE 4.5 scores competitively for software generation. For complex autonomous debugging and agentic engineering, ERNIE 5.0 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between ERNIE 5.0 and ERNIE 4.5?+
ERNIE 5.0 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, ERNIE 4.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between ERNIE 5.0 and ERNIE 4.5?+
ERNIE 5.0 generates approximately 70 tokens per second, while ERNIE 4.5 delivers 75 tokens per second. In latency-sensitive production environments, ERNIE 4.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining ERNIE 5.0 and ERNIE 4.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to ERNIE 4.5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to ERNIE 5.0. This tiered routing pattern cuts overall API bills by up to 70%.
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