Model profile
Baidu

Baidu: ERNIE 4.5 300B A47B

Baidu: ERNIE 4.5 300B A47B is a budget-priced text-first model from Baidu with heavy runtime profile, standard context posture, and the clearest fit around long-context research / reasoning.

Best for: Long-context research / ReasoningHeavy latencyStandard contextBudget pricing
Intelligence
15.0

Benchmark blend

Coding
14.5

Dev workflow signal

Context
123K Tokens

Standard

Input Price
$0.28

Budget tier

Decision snapshot
39

Baidu: ERNIE 4.5 300B A47B currently reads as a budget text-first option with standard context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Reasoning
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

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
15.0
15

General reasoning and benchmark headroom.

Limited
Speed
26 tok/s
41

TTFT 1.67s

Limited
Context
123K Tokens
64

How much prompt and task state can stay in view.

Competitive
Price
$0.28
86

$1.10 output / 1M

Efficient

Editorial Profile

Baidu: ERNIE 4.5 300B 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 15Math score 41

ERNIE-4.5-300B-A47B is a 300B parameter Mixture-of-Experts (MoE) language model developed by Baidu as part of the ERNIE 4.5 series. It activates 47B parameters per token and supports text generation in...

Identity

Baidu text-first profile

Positioning

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

Cost posture

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

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

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.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

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

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Context
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Context
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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.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
15.0
MMLU Pro
77.6%
GPQA
81.1%
HLE
3.5%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
14.5
LiveCodeBench
0.467
SciCode
31.5%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
41.3
AIME
49.3%
AIME 2025
41.3%
Math 500
93.1%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
39.1%
TAU2
0.0%
TerminalBench Hard
6.1%
LCR
2.3%

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
Text-first
Modalities
text
Tokenizer
Other
Max Completion
12000
Moderation
No
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.28
Output
per 1M output tokens
$1.10
Blended
AA 3:1 mix
$0.48

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.