Head-to-head · multimodal vs multimodal

ERNIE 4.5 Pro vs GLM-4.6 Chat: which model should you choose?

We compared ERNIE 4.5 Pro and GLM-4.6 Chat on price, context window and 12 benchmarks. ERNIE 4.5 Pro has the stronger average benchmark score; GLM-4.6 Chat 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
Zhipu AI

GLM-4.6 Chat

GLM-4.6 Chat is a multimodal model from Zhipu AI. It offers a 400,000-token context window, costs $4.89 per million input tokens and $21.46 per million output tokens, and averages 79.8 across the index's benchmark suite.

Input
$4.89 / 1M
Output
$21.46 / 1M
Context
400,000
Avg score
79.8
Open weights
Yes
Full GLM-4.6 Chat page

Specification comparison

SpecERNIE 4.5 ProGLM-4.6 ChatEdge
Input price / 1M$13.89$4.89B
Output price / 1M$65.19$21.46B
Context window1,000,000400,000A
Max output32,76816,384A
Average score80.579.8A
Arena Elo1414.31437.7B
Open weightsNoYesB

Benchmark comparison

ERNIE 4.5 Pro wins 7, GLM-4.6 Chat wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkERNIE 4.5 ProGLM-4.6 ChatGapWinner
MMLU-Pro80.382.21.9GLM-4.6 Chat
GPQA Diamond74.772.81.9ERNIE 4.5 Pro
SWE-bench Verified66.566.00.5ERNIE 4.5 Pro
HumanEval86.986.30.6ERNIE 4.5 Pro
MATH-50087.286.80.4ERNIE 4.5 Pro
AIME 202576.379.22.9GLM-4.6 Chat
LiveCodeBench76.469.66.8ERNIE 4.5 Pro
IFEval86.087.81.8GLM-4.6 Chat
TruthfulQA83.183.20.1GLM-4.6 Chat
HellaSwag90.289.90.3ERNIE 4.5 Pro
MMMU78.473.54.9ERNIE 4.5 Pro
Arena Elo1414.31437.723.4GLM-4.6 Chat

Price analysis

On a blended 3:1 input-to-output basis, ERNIE 4.5 Pro costs about $26.71 per million tokens and GLM-4.6 Chat costs about $9.03. GLM-4.6 Chat is roughly 66% 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 400,000 for GLM-4.6 Chat. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, ERNIE 4.5 Pro wins 7, GLM-4.6 Chat wins 5, and 0 are tied. On the index average, ERNIE 4.5 Pro leads GLM-4.6 Chat by 0.7 points (80.5 versus 79.8).

The price picture favours GLM-4.6 Chat. 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 GLM-4.6 Chat 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.
  • You need the larger 32,768-token output limit.

Choose GLM-4.6 Chat if

  • Unit cost matters and you want the cheaper blended price.
  • You need open weights for self-hosting or fine-tuning.

FAQ

Is ERNIE 4.5 Pro better than GLM-4.6 Chat?

On the index average, ERNIE 4.5 Pro leads GLM-4.6 Chat by 0.7 points (80.5 versus 79.8). The better choice depends on price, context and your task. ERNIE 4.5 Pro wins 7 benchmarks, GLM-4.6 Chat wins 5.

Which is cheaper, ERNIE 4.5 Pro or GLM-4.6 Chat?

GLM-4.6 Chat is cheaper on a blended 3:1 basis at about $9.03 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. GLM-4.6 Chat has 400,000 tokens.

Are these models open weights?

ERNIE 4.5 Pro is proprietary; GLM-4.6 Chat is open weights.

Which should I choose for production?

If you need a single default, start with ERNIE 4.5 Pro for quality-sensitive work and GLM-4.6 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Related comparisons