ERNIE 4.5 VL 424B A47B model profile
Evidence snapshot:
ERNIE 4.5 VL 424B A47B is a Baidu model profile with a 123,000-token context window. The dated catalog snapshot records image, text modalities and interfaces for reasoning controls, text generation, vision.
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...
ERNIE 4.5 VL 424B A47B is a reference profile: Baidu publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
ERNIE 4.5 VL 424B A47B is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.
ERNIE 4.5 VL 424B A47B accepts image and text and returns text. The snapshot records reasoning controls, text generation, and vision as capabilities or interfaces.
The dated reference snapshot lists $0.42 input and $1.25 output per million tokens.
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.
Profile facts
- Profile type
- Model research profile
- Provider or publisher
- Baidu
- Context window
- 123,000 tokens
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- 2025-03-31
- Reference catalog ID
- baidu/ernie-4.5-vl-424b-a47b
When to pick ERNIE 4.5 VL 424B A47B
ERNIE 4.5 VL 424B A47B is a reference profile: Baidu publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.
- Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
- Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
- Look elsewhere when the prompt is long. The 123K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.
- Look elsewhere when you are building an agent. No tool-calling capability is recorded for this model in the snapshot. Agent loops need a route that can call functions.
Context, modalities, and identity
ERNIE 4.5 VL 424B A47B accepts image and text and returns text. The snapshot records reasoning controls, text generation, and vision as capabilities or interfaces.
- Provider or publisher
- Baidu
- Context window
- 123,000 tokens
- Maximum output
- 16,000 tokens
- Input modalities
- image and text
- Output modalities
- text
- Knowledge cutoff
- 2025-03-31
Model overview
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...
- Reference model ID
- baidu/ernie-4.5-vl-424b-a47b
- Canonical version
- baidu/ernie-4.5-vl-424b-a47b
- Hugging Face ID
- baidu/ERNIE-4.5-VL-424B-A47B-PT
Capabilities and supported controls
Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.
- Supported parameters
- frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, seed, stop, temperature, top_k, top_p
- reasoning controls
- text generation
- vision
Dated pricing snapshot
The dated reference snapshot lists $0.42 input and $1.25 output per million tokens.
These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.
- Price date
- 2026-09-06
- Input
- $0.42 per 1M tokens
- Cached input
- No verified rate
- Output
- $1.25 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 1 provider endpoint. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 1
- Best p50 latency
- No recent metric
- Best p50 throughput
- No recent metric
- Availability with routing
- No recent metric
- Availability without routing
- No recent metric
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| Novita | fp16 | $0.42 / 1M | $1.25 / 1M | No verified rate | 123,000 tokens | No recent p50 | No recent p50 | 100.00% |
Benchmark evidence and limitations
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.
Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Popularity and market context
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
- Third-party catalog rank
- No verified rank
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use ERNIE 4.5 VL 424B A47B?
ERNIE 4.5 VL 424B A47B is a reference profile: Baidu publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the prompt is long: The 123K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route. Look elsewhere when you are building an agent: No tool-calling capability is recorded for this model in the snapshot. Agent loops need a route that can call functions.
What is ERNIE 4.5 VL 424B A47B?
ERNIE 4.5 VL 424B A47B is a Baidu model profile with a 123,000-token context window. The dated catalog snapshot records image, text modalities and interfaces for reasoning controls, text generation, vision.
What context window does ERNIE 4.5 VL 424B A47B have?
The dated profile lists 123,000 tokens of context and up to 16,000 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for ERNIE 4.5 VL 424B A47B?
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Is ERNIE 4.5 VL 424B A47B available through Kendr?
This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.
Does the popularity rank represent ERNIE 4.5 VL 424B A47B's global market share?
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated. No global market-share percentage is inferred when the source does not publish one.
Sources and evidence dates
- ERNIE 4.5 VL 424B A47B third-party catalog record (catalog, checked 2026-09-06)
- Third-party model catalog methodology (methodology, checked 2026-09-06)