ERNIE 4.5 vs Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct significantly outperforms across most benchmarks. Qwen3 VL 235B A22B Instruct is 2.2x cheaper per token.
Baidu · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
ERNIE 4.5 outperforms in 0 benchmarks, while Qwen3 VL 235B A22B Instruct is better at 4 benchmarks (MMLU, MMLU-Pro, MMLU-Redux, SimpleQA). Qwen3 VL 235B A22B Instruct significantly outperforms across most benchmarks.
On price, Qwen3 VL 235B A22B Instruct is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 235B A22B Instruct also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose ERNIE 4.5
- you want predictable pricing at $0.40/M input and $4.00/M output
Choose Qwen3 VL 235B A22B Instruct
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
- cost matters — it's about 2.2x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
ERNIE 4.5 outperforms in 0 benchmarks, while Qwen3 VL 235B A22B Instruct is better at 4 benchmarks (MMLU, MMLU-Pro, MMLU-Redux, SimpleQA).
Qwen3 VL 235B A22B Instruct significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, ERNIE 4.5 ($0.40/1M tokens) is 1.3x more expensive than Qwen3 VL 235B A22B Instruct ($0.30/1M tokens).
For output processing, ERNIE 4.5 ($4.00/1M tokens) is 2.7x more expensive than Qwen3 VL 235B A22B Instruct ($1.49/1M tokens).
In conclusion, ERNIE 4.5 is more expensive than Qwen3 VL 235B A22B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3 VL 235B A22B Instruct has 215.0B more parameters than ERNIE 4.5, making it 1023.8% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 235B A22B Instruct accepts 262,144 input tokens compared to ERNIE 4.5's 128,000 tokens. Qwen3 VL 235B A22B Instruct can generate longer responses up to 262,144 tokens, while ERNIE 4.5 is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 235B A22B Instruct supports multimodal inputs, whereas ERNIE 4.5 does not.
Qwen3 VL 235B A22B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
ERNIE 4.5
Qwen3 VL 235B A22B Instruct
License
Usage and distribution terms
ERNIE 4.5 is licensed under a proprietary license, while Qwen3 VL 235B A22B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
ERNIE 4.5 was released on 2025-06-25, while Qwen3 VL 235B A22B Instruct was released on 2025-09-22.
Qwen3 VL 235B A22B Instruct is 3 months newer than ERNIE 4.5.
Jun 25, 2025
1.2 years ago
Sep 22, 2025
11 months ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
ERNIE 4.5 is available from Novita. Qwen3 VL 235B A22B Instruct is available from DeepInfra, Novita.
ERNIE 4.5
Qwen3 VL 235B A22B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against ERNIE 4.5 and Qwen3 VL 235B A22B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about ERNIE 4.5 vs Qwen3 VL 235B A22B Instruct.