ERNIE 4.5 vs ERNIE X1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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ERNIE 4.5 by Baidu and ERNIE X1 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 X1 holds a notable pricing advantage for token ingestion. On output generation, ERNIE X1 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 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
ERNIE X1
Deep ThinkingBaidu specialized deliberate reasoning model focused on multi-step mathematical derivation, logical puzzles, and structured planning.
$0.40 / 1M
$1.60 / 1M
128K tokens
N/A
Key Differences at a Glance
- ERNIE X1 is 1.5x cheaper for API usage (input + output tokens combined).
- ERNIE 4.5 is faster at 75 tok/s vs 65 tok/s.
| Feature | ERNIE 4.5 | ERNIE X1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 82 |
Provider | Baidu | Baidu |
Input Price Cost per million input tokens | $0.60 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $1.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.40 / 1M | $0.27 / 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 | 8K | 16K |
Throughput Speed Tokens generated per second | 75 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 60% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.04 / 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.
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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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 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
ERNIE X1 · Strengths & Key Advantages
- Deep chain-of-thought exploration specifically optimized for Chinese STEM benchmarks
- Effective verification loops preventing common arithmetic and logic fallacies
- Affordable commercial API pricing ($0.40 / 1M input tokens)
Considerations & Trade-offs
- Higher latency due to extended thinking traces
- Text-only interface without visual chart verification
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | ERNIE 4.5 | ERNIE X1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.16 | $0.14 |
Output Tokens / Task Tokens generated per task | 18K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 4K | 11K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | ERNIE 4.5 | ERNIE X1 |
|---|---|---|
| Official API Model ID (SDK) | ernie-4-5 | ernie-x1 |
| API Protocol | Baidu REST API (OpenAI-Compatible) | Baidu REST API (OpenAI-Compatible) |
| Release Date | 2025-03 | 2025-03 |
| Knowledge Cutoff | December 2024 | January 2025 |
| Architecture | Multimodal Foundation Knowledge Transformer | Deep Deliberate Reasoning Transformer |
| Parameters | Confidential | 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 ($0.6/1M in) across full 128K window | Flat rate ($0.4/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Cached input at $0.04/1M 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 | 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) | Text BPE (~4 chars/token) |
| 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 | text |
| Output Modalities | text | text |
ERNIE 4.5 vs ERNIE X1: Quick Answers
Which is more intelligent, ERNIE 4.5 or ERNIE X1?
ERNIE X1 is more intelligent. It scores 82 compared with 81 on the composite Intelligence Index.
Which model is faster?
ERNIE 4.5 is faster, generating 75 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
ERNIE X1 is cheaper at $0.27 per 1M tokens blended, compared with $0.40 (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 0.82s.
Verdict
Verdict & Model Selection Recommendation: Choose ERNIE 4.5 if your priority is Chinese enterprise applications, local compliance and legal review, and multimodal knowledge search. Its performance in demanding tasks justifies its operational token cost. Choose ERNIE X1 if your workflow prioritizes Mathematical tutoring, competitive programming in Chinese, and structured business planning. 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 4.5 compare to ERNIE X1 in API token pricing and cost per token?+
ERNIE 4.5 costs $0.60 per 1M input tokens and $2.40 per 1M output tokens, whereas ERNIE X1 costs $0.40 input and $1.60 output. Overall, ERNIE X1 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 4.5 or ERNIE X1?+
Both ERNIE 4.5 and ERNIE X1 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 4.5 or ERNIE X1?+
ERNIE 4.5 scores at the frontier of coding benchmarks, while ERNIE X1 scores competitively for software generation. For complex autonomous debugging and agentic engineering, ERNIE 4.5 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between ERNIE 4.5 and ERNIE X1?+
ERNIE 4.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, ERNIE X1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between ERNIE 4.5 and ERNIE X1?+
ERNIE 4.5 generates approximately 75 tokens per second, while ERNIE X1 delivers 65 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 4.5 and ERNIE X1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to ERNIE X1 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to ERNIE 4.5. 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