바이두 개발의 최신 기초 모델. 중국어 대응에 뛰어난 고성능 언어 모델입니다.
파라미터
8000
컨텍스트
128K
라이선스
Proprietary
출시일
2026-04-30
벤치마크 성능
AA Intelligence Index
—
LMArena Elo
—
HLE
—
ARC-AGI-2
—
SWE-bench Verified
—
GPQA Diamond
—
MMLU-Pro
—
LiveCodeBench
—
AIME 2025
—
MATH-500
—
일본어 처리 능력
General multilingual model. Basic Japanese processing is possible, but inferior to specialized models.
API 가격
이 모델의 API 가격 정보는 현재 공개되지 않았습니다
강점
약점
활용 사례
심층 분석
Arena Text Elo
1476
#13-14 globally, #1 among Chinese models
Search Arena Elo
1223
#4 globally, #1 in China
Pre-training Cost
~6% of comparable models
vs industry benchmarks
Total Parameters
~8000B
compressed from ERNIE 5.0
Context Window
128K tokens
standard for modern LLMs
API Pricing
0.004元/千tokens (input)
extremely cost-effective
강점
- ・Industry-leading cost efficiency with only ~6% training cost of comparable models
- ・Top-tier search and information retrieval capabilities (global top 4 in Search Arena)
- ・Strong agent/tool-calling capabilities surpassing DeepSeek-V4-Pro in benchmarks
약점
- ・Weak in pure mathematical reasoning and advanced spreadsheet operations
- ・Closed-source model with no local deployment options
- ・API pricing details remain unclear and may change after preview period
경쟁사 비교
| Model | Arena | SWE | GPQA | Price |
|---|---|---|---|---|
| ERNIE 5.1 | 1476 | N/A | 91.0 | 0.004元/千tokens input |
| DeepSeek-V4-Pro | <1476 (not top 20) | N/A | 90.1 | Not publicly disclosed |
| Gemini 3.1 Pro | Top 5 globally | N/A | 94.1 | $12-18/1M tokens (estimated) |
ERNIE 5.1 Preview represents Baidu's latest breakthrough in cost-effective, high-performance large language models. Released in late April 2026, this model demonstrates that world-class performance doesn't require massive computational budgets. By compressing parameters to roughly one-third of its predecessor ERNIE 5.0 while maintaining competitive capabilities, Baidu has created what they call a "price-performance benchmark" for Chinese AI models. The model's standout achievements include topping China's rankings on the LMArena search leaderboard (1223 Elo, global #4) and achieving strong results across knowledge, reasoning, and agent capabilities.
The technical innovation centers on Baidu's "Once-for-All" elastic training framework, which enables simultaneous optimization of multiple model sizes during a single pre-training run. This approach, combined with a separated asynchronous reinforcement learning architecture, achieves remarkable efficiency - reducing pre-training costs to just 6% of comparable models. ERNIE 5.1 shows particular strength in Chinese language tasks, creative writing (approaching Gemini 3.1 Pro levels), and tool-calling/agent capabilities, making it especially valuable for Chinese enterprises and developers.
However, the model isn't without limitations. Independent benchmarks reveal significant gaps in pure mathematical reasoning and complex spreadsheet operations compared to top international models like Gemini and Claude. Additionally, as a closed-source model with uncertain long-term pricing, it may not satisfy developers needing local deployment or transparent costs. Baidu plans a full release at their Create 2026 developer conference in May, where pricing details and potential performance improvements are expected.
출처
분석 생성일: 2026-07-17