Head-to-head · multimodal vs multimodal

ERNIE 4.5 Pro vs ERNIE 4.5 Turbo: which model should you choose?

We compared ERNIE 4.5 Pro and ERNIE 4.5 Turbo on price, context window and 12 benchmarks. ERNIE 4.5 Pro has the stronger average benchmark score; ERNIE 4.5 Turbo is cheaper on a blended 3:1 basis.

Baidu

ERNIE 4.5 Pro

ERNIE 4.5 Pro is a multimodal model from Baidu. It offers a 1,000,000-token context window, costs $13.89 per million input tokens and $65.19 per million output tokens, and averages 80.5 across the index's benchmark suite.

Input
$13.89 / 1M
Output
$65.19 / 1M
Context
1,000,000
Avg score
80.5
Open weights
No
Full ERNIE 4.5 Pro page
Baidu

ERNIE 4.5 Turbo

ERNIE 4.5 Turbo is a multimodal model from Baidu. It offers a 128,000-token context window, costs $13.75 per million input tokens and $54.36 per million output tokens, and averages 79.7 across the index's benchmark suite.

Input
$13.75 / 1M
Output
$54.36 / 1M
Context
128,000
Avg score
79.7
Open weights
No
Full ERNIE 4.5 Turbo page

Specification comparison

SpecERNIE 4.5 ProERNIE 4.5 TurboEdge
Input price / 1M$13.89$13.75B
Output price / 1M$65.19$54.36B
Context window1,000,000128,000A
Max output32,76832,768Tie
Average score80.579.7A
Arena Elo1414.31419.8B
Open weightsNoNoTie

Benchmark comparison

ERNIE 4.5 Pro wins 8, ERNIE 4.5 Turbo wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkERNIE 4.5 ProERNIE 4.5 TurboGapWinner
MMLU-Pro80.384.03.7ERNIE 4.5 Turbo
GPQA Diamond74.773.01.7ERNIE 4.5 Pro
SWE-bench Verified66.567.51.0ERNIE 4.5 Turbo
HumanEval86.986.40.5ERNIE 4.5 Pro
MATH-50087.286.40.8ERNIE 4.5 Pro
AIME 202576.380.74.4ERNIE 4.5 Turbo
LiveCodeBench76.470.55.9ERNIE 4.5 Pro
IFEval86.085.20.8ERNIE 4.5 Pro
TruthfulQA83.182.50.6ERNIE 4.5 Pro
HellaSwag90.287.03.2ERNIE 4.5 Pro
MMMU78.473.74.7ERNIE 4.5 Pro
Arena Elo1414.31419.85.5ERNIE 4.5 Turbo

Price analysis

On a blended 3:1 input-to-output basis, ERNIE 4.5 Pro costs about $26.71 per million tokens and ERNIE 4.5 Turbo costs about $23.90. ERNIE 4.5 Turbo is roughly 11% cheaper than ERNIE 4.5 Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

ERNIE 4.5 Pro has the larger context window at 1,000,000 tokens, compared with 128,000 for ERNIE 4.5 Turbo. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, ERNIE 4.5 Pro wins 8, ERNIE 4.5 Turbo wins 4, and 0 are tied. On the index average, ERNIE 4.5 Pro leads ERNIE 4.5 Turbo by 0.8 points (80.5 versus 79.7).

The price picture favours ERNIE 4.5 Turbo. The context picture favours ERNIE 4.5 Pro. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.

If you need a single default, start with ERNIE 4.5 Pro for quality-sensitive work and ERNIE 4.5 Turbo for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose ERNIE 4.5 Pro if

  • You want the higher average benchmark score in this index.
  • You need the larger 1,000,000-token context window.

Choose ERNIE 4.5 Turbo if

  • Unit cost matters and you want the cheaper blended price.

FAQ

Is ERNIE 4.5 Pro better than ERNIE 4.5 Turbo?

On the index average, ERNIE 4.5 Pro leads ERNIE 4.5 Turbo by 0.8 points (80.5 versus 79.7). The better choice depends on price, context and your task. ERNIE 4.5 Pro wins 8 benchmarks, ERNIE 4.5 Turbo wins 4.

Which is cheaper, ERNIE 4.5 Pro or ERNIE 4.5 Turbo?

ERNIE 4.5 Turbo is cheaper on a blended 3:1 basis at about $23.90 per million tokens, versus $26.71 for ERNIE 4.5 Pro.

Which has the larger context window?

ERNIE 4.5 Pro has the larger context window at 1,000,000 tokens. ERNIE 4.5 Turbo has 128,000 tokens.

Are these models open weights?

ERNIE 4.5 Pro is proprietary; ERNIE 4.5 Turbo is proprietary.

Which should I choose for production?

If you need a single default, start with ERNIE 4.5 Pro for quality-sensitive work and ERNIE 4.5 Turbo for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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