Head-to-head · multimodal vs multimodal

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

We compared ERNIE 4.5 Turbo and GLM-4.6 Chat on price, context window and 12 benchmarks. GLM-4.6 Chat has the stronger average benchmark score; GLM-4.6 Chat is cheaper on a blended 3:1 basis.

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
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 TurboGLM-4.6 ChatEdge
Input price / 1M$13.75$4.89B
Output price / 1M$54.36$21.46B
Context window128,000400,000B
Max output32,76816,384A
Average score79.779.8B
Arena Elo1419.81437.7B
Open weightsNoYesB

Benchmark comparison

ERNIE 4.5 Turbo 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 TurboGLM-4.6 ChatGapWinner
MMLU-Pro84.082.21.8ERNIE 4.5 Turbo
GPQA Diamond73.072.80.2ERNIE 4.5 Turbo
SWE-bench Verified67.566.01.5ERNIE 4.5 Turbo
HumanEval86.486.30.1ERNIE 4.5 Turbo
MATH-50086.486.80.4GLM-4.6 Chat
AIME 202580.779.21.5ERNIE 4.5 Turbo
LiveCodeBench70.569.60.9ERNIE 4.5 Turbo
IFEval85.287.82.6GLM-4.6 Chat
TruthfulQA82.583.20.7GLM-4.6 Chat
HellaSwag87.089.92.9GLM-4.6 Chat
MMMU73.773.50.2ERNIE 4.5 Turbo
Arena Elo1419.81437.717.9GLM-4.6 Chat

Price analysis

On a blended 3:1 input-to-output basis, ERNIE 4.5 Turbo costs about $23.90 per million tokens and GLM-4.6 Chat costs about $9.03. GLM-4.6 Chat is roughly 62% cheaper than ERNIE 4.5 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

GLM-4.6 Chat has the larger context window at 400,000 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 Turbo wins 7, GLM-4.6 Chat wins 5, and 0 are tied. On the index average, GLM-4.6 Chat leads ERNIE 4.5 Turbo by 0.1 points (79.8 versus 79.7).

The price picture favours GLM-4.6 Chat. The context picture favours GLM-4.6 Chat. 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 GLM-4.6 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.

Choose ERNIE 4.5 Turbo if

  • You need the larger 32,768-token output limit.

Choose GLM-4.6 Chat if

  • You want the higher average benchmark score in this index.
  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 400,000-token context window.
  • You need open weights for self-hosting or fine-tuning.

FAQ

Is ERNIE 4.5 Turbo better than GLM-4.6 Chat?

On the index average, GLM-4.6 Chat leads ERNIE 4.5 Turbo by 0.1 points (79.8 versus 79.7). The better choice depends on price, context and your task. ERNIE 4.5 Turbo wins 7 benchmarks, GLM-4.6 Chat wins 5.

Which is cheaper, ERNIE 4.5 Turbo 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 $23.90 for ERNIE 4.5 Turbo.

Which has the larger context window?

GLM-4.6 Chat has the larger context window at 400,000 tokens. ERNIE 4.5 Turbo has 128,000 tokens.

Are these models open weights?

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

Which should I choose for production?

If you need a single default, start with GLM-4.6 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.

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