Model profile
baidu

Baidu: ERNIE 4.5 VL 424B A47B

Baidu: ERNIE 4.5 VL 424B A47B is a budget multimodal generalist from baidu with a heavy runtime profile, standard context posture, and the clearest fit around multimodal / long-context research.

Best for: Multimodal / Long-context researchHeavy latencyStandard contextBudget pricing
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
123K Tokens

Standard

Input Price
$0.42

Budget tier

Decision snapshot
54

Baidu: ERNIE 4.5 VL 424B A47B currently reads as a budget multimodal option with standard context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Multimodal / Long-context research
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterVision signal

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
N/A
44

General reasoning and benchmark headroom.

Situational
Speed
N/A
46

Latency data is partial.

Situational
Context
123K Tokens
64

How much prompt and task state can stay in view.

Competitive
Price
$0.42
86

$1.25 output / 1M

Efficient

Editorial Profile

Baidu: ERNIE 4.5 VL 424B A47B in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 40Math score 36Vision enabled

ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. It is trained jointly on text and image data using a heterogeneous MoE architecture and modality-isolated routing to enable high-fidelity cross-modal reasoning, image understanding, and long-context generation (up to 131k tokens). Fine-tuned with techniques like SFT, DPO, UPO, and RLVR, this model supports both “thinking” and non-thinking inference modes. Designed for vision-language tasks in English and Chinese, it is optimized for efficient scaling and can operate under 4-bit/8-bit quantization.

Identity

baidu multimodal profile

Positioning

Multimodal / Long-context research with standard context and heavy runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Vision-capable routing opens up multimodal review and extraction workflows.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

No benchmark data is available for this model yet.

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
123K Tokens
Vision
Enabled
Modalities
text, image->text, image
Tokenizer
Other
Max Completion
16000
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningrepetition_penaltyseedstoptemperaturetop_ktop_p
Input Modalities
imagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.42
Output
per 1M output tokens
$1.25
Blended
AA 3:1 mix
N/A

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.