| Signal | ERNIE 4.5 VL 28B A3B | Delta | Qwen3 30B A3B Instruct 2507 |
|---|---|---|---|
Capabilities | 67 | +17 | |
Pricing | 1 | +0 | |
Context window size | 71 | -15 | |
Recency | 91 | +3 | |
Output Capacity | 65 | -25 | |
| Overall Result | 3 wins | of 5 | 2 wins |
9
days higher
3
days
18
days higher
Baidu
Alibaba
Qwen3 30B A3B Instruct 2507 saves you $18.00/month
That's $216.00/year compared to ERNIE 4.5 VL 28B A3B at your current usage level of 100K calls/month.
| Metric | ERNIE 4.5 VL 28B A3B | Qwen3 30B A3B Instruct 2507 | Winner |
|---|---|---|---|
| Overall Score | 75 | 75 | Qwen3 30B A3B Instruct 2507 |
| Rank | #117 | #115 | Qwen3 30B A3B Instruct 2507 |
| Quality Rank | #117 | #115 | Qwen3 30B A3B Instruct 2507 |
| Adoption Rank | #117 | #115 | Qwen3 30B A3B Instruct 2507 |
| Parameters | 28B | 30B | -- |
| Context Window | 30K | 262K | Qwen3 30B A3B Instruct 2507 |
| Pricing | $0.14/$0.56/M | $0.09/$0.30/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | ERNIE 4.5 VL 28B A3B |
| Pricing | 1 | 0 | ERNIE 4.5 VL 28B A3B |
| Context window size | 71 | 86 | Qwen3 30B A3B Instruct 2507 |
| Recency | 91 | 88 | ERNIE 4.5 VL 28B A3B |
| Output Capacity | 65 | 90 | Qwen3 30B A3B Instruct 2507 |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 75/100 (rank #117), placing it in the top 60% of all 290 models tracked.
Scores 75/100 (rank #115), placing it in the top 61% 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.
Qwen3 30B A3B Instruct 2507 offers 44% better value per quality point. At 1M tokens/day, you'd spend $5.85/month with Qwen3 30B A3B Instruct 2507 vs $10.50/month with ERNIE 4.5 VL 28B A3B - a $4.65 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. Qwen3 30B A3B Instruct 2507 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.30/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (75/100) 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 VL 28B A3B and Qwen3 30B A3B Instruct 2507 are extremely close in overall performance (only 0.20000000000000284 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
ERNIE 4.5 VL 28B A3B
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3 30B A3B Instruct 2507
44% lower pricing; better value at scale
Best for Reliability
ERNIE 4.5 VL 28B A3B
Higher uptime and faster response speeds
Best for Prototyping
ERNIE 4.5 VL 28B A3B
Stronger community support and better developer experience
Best for Production
ERNIE 4.5 VL 28B A3B
Wider enterprise adoption and proven at scale
by Baidu
by Alibaba
| Capability | ERNIE 4.5 VL 28B A3B | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Baidu
Alibaba
Qwen3 30B A3B Instruct 2507 saves you $0.4020/month
That's 44% cheaper than ERNIE 4.5 VL 28B A3B 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 VL 28B A3B | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Context Window | 30K | 262K |
| Max Output Tokens | 8,000 | 262,144 |
| Open Source | Yes | Yes |
| Created | Aug 12, 2025 | Jul 29, 2025 |
Qwen3 30B A3B Instruct 2507 scores 75/100 (rank #115) compared to ERNIE 4.5 VL 28B A3B's 75/100 (rank #117), giving it a 0-point advantage. Qwen3 30B A3B Instruct 2507 is the stronger overall choice, though ERNIE 4.5 VL 28B A3B may excel in specific areas like certain benchmarks.
ERNIE 4.5 VL 28B A3B is ranked #117 and Qwen3 30B A3B Instruct 2507 is ranked #115 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Qwen3 30B A3B Instruct 2507 is cheaper at $0.30/M output tokens vs ERNIE 4.5 VL 28B A3B's $0.56/M output tokens - 1.9x more expensive. Input token pricing: ERNIE 4.5 VL 28B A3B at $0.14/M vs Qwen3 30B A3B Instruct 2507 at $0.09/M.
Qwen3 30B A3B Instruct 2507 has a larger context window of 262,144 tokens compared to ERNIE 4.5 VL 28B A3B's 30,000 tokens. A larger context window means the model can process longer documents and conversations.