| Signal | ERNIE 4.5 21B A3B Thinking | Delta | Qwen3 VL 30B A3B Instruct |
|---|---|---|---|
Capabilities | 33 | -33 | |
Pricing | 0 | 0 | |
Context window size | 81 | -- | |
Recency | 100 | -- | |
Output Capacity | 80 | +5 | |
| Overall Result | 1 wins | of 5 | 2 wins |
7
days higher
5
days
18
days higher
Baidu
Alibaba
ERNIE 4.5 21B A3B Thinking saves you $18.00/month
That's $216.00/year compared to Qwen3 VL 30B A3B Instruct at your current usage level of 100K calls/month.
| Metric | ERNIE 4.5 21B A3B Thinking | Qwen3 VL 30B A3B Instruct | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #189 | #191 | ERNIE 4.5 21B A3B Thinking |
| Quality Rank | #189 | #191 | ERNIE 4.5 21B A3B Thinking |
| Adoption Rank | #189 | #191 | ERNIE 4.5 21B A3B Thinking |
| Parameters | 21B | 30B | -- |
| Context Window | 131K | 131K | -- |
| Pricing | $0.07/$0.28/M | $0.13/$0.52/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 67 | Qwen3 VL 30B A3B Instruct |
| Pricing | 0 | 1 | Qwen3 VL 30B A3B Instruct |
| Context window size | 81 | 81 | ERNIE 4.5 21B A3B Thinking |
| Recency | 100 | 100 | ERNIE 4.5 21B A3B Thinking |
| Output Capacity | 80 | 75 | ERNIE 4.5 21B A3B Thinking |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 40/100 (rank #189), placing it in the top 35% of all 290 models tracked.
Scores 40/100 (rank #191), placing it in the top 34% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
ERNIE 4.5 21B A3B Thinking offers 46% better value per quality point. At 1M tokens/day, you'd spend $5.25/month with ERNIE 4.5 21B A3B Thinking vs $9.75/month with Qwen3 VL 30B A3B Instruct - a $4.50 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. ERNIE 4.5 21B A3B Thinking also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.28/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
ERNIE 4.5 21B A3B Thinking and Qwen3 VL 30B A3B Instruct are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
ERNIE 4.5 21B A3B Thinking
Marginally better benchmark scores; both are excellent
Best for Cost
ERNIE 4.5 21B A3B Thinking
46% lower pricing; better value at scale
Best for Reliability
ERNIE 4.5 21B A3B Thinking
Higher uptime and faster response speeds
Best for Prototyping
ERNIE 4.5 21B A3B Thinking
Stronger community support and better developer experience
Best for Production
ERNIE 4.5 21B A3B Thinking
Wider enterprise adoption and proven at scale
by Baidu
| Capability | ERNIE 4.5 21B A3B Thinking | Qwen3 VL 30B A3B Instruct |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Baidu
Alibaba
ERNIE 4.5 21B A3B Thinking saves you $0.3960/month
That's 46% cheaper than Qwen3 VL 30B A3B Instruct at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | ERNIE 4.5 21B A3B Thinking | Qwen3 VL 30B A3B Instruct |
|---|---|---|
| Context Window | 131K | 131K |
| Max Output Tokens | 65,536 | 32,768 |
| Open Source | Yes | Yes |
| Created | Oct 9, 2025 | Oct 6, 2025 |
Both ERNIE 4.5 21B A3B Thinking and Qwen3 VL 30B A3B Instruct score 40/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
ERNIE 4.5 21B A3B Thinking is ranked #189 and Qwen3 VL 30B A3B Instruct is ranked #191 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
ERNIE 4.5 21B A3B Thinking is cheaper at $0.28/M output tokens vs Qwen3 VL 30B A3B Instruct's $0.52/M output tokens - 1.9x more expensive. Input token pricing: ERNIE 4.5 21B A3B Thinking at $0.07/M vs Qwen3 VL 30B A3B Instruct at $0.13/M.
ERNIE 4.5 21B A3B Thinking has a larger context window of 131,072 tokens compared to Qwen3 VL 30B A3B Instruct's 131,072 tokens. A larger context window means the model can process longer documents and conversations.