ERNIE 4.5 Max vs GLM-4.6 Instruct: which model should you choose?
We compared ERNIE 4.5 Max and GLM-4.6 Instruct on price, context window and 12 benchmarks. ERNIE 4.5 Max has the stronger average benchmark score; GLM-4.6 Instruct is cheaper on a blended 3:1 basis.
ERNIE 4.5 Max
ERNIE 4.5 Max is a multimodal model from Baidu. It offers a 128,000-token context window, costs $14.41 per million input tokens and $53.71 per million output tokens, and averages 80.6 across the index's benchmark suite.
- Input
- $14.41 / 1M
- Output
- $53.71 / 1M
- Context
- 128,000
- Avg score
- 80.6
- Open weights
- No
GLM-4.6 Instruct
GLM-4.6 Instruct is a multimodal model from Zhipu AI. It offers a 1,000,000-token context window, costs $5.15 per million input tokens and $24.32 per million output tokens, and averages 80.4 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $24.32 / 1M
- Context
- 1,000,000
- Avg score
- 80.4
- Open weights
- Yes
Specification comparison
| Spec | ERNIE 4.5 Max | GLM-4.6 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $14.41 | $5.15 | B |
| Output price / 1M | $53.71 | $24.32 | B |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 65,536 | 128,000 | B |
| Average score | 80.6 | 80.4 | A |
| Arena Elo | 1423.7 | 1426.6 | B |
| Open weights | No | Yes | B |
Benchmark comparison
ERNIE 4.5 Max wins 5, GLM-4.6 Instruct wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | ERNIE 4.5 Max | GLM-4.6 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.8 | 80.6 | 0.2 | ERNIE 4.5 Max |
| GPQA Diamond | 76.4 | 75.8 | 0.6 | ERNIE 4.5 Max |
| SWE-bench Verified | 68.9 | 70.5 | 1.6 | GLM-4.6 Instruct |
| HumanEval | 87.7 | 85.3 | 2.4 | ERNIE 4.5 Max |
| MATH-500 | 84.2 | 84.9 | 0.7 | GLM-4.6 Instruct |
| AIME 2025 | 79.5 | 74.5 | 5.0 | ERNIE 4.5 Max |
| LiveCodeBench | 73.0 | 72.6 | 0.4 | ERNIE 4.5 Max |
| IFEval | 87.1 | 87.4 | 0.3 | GLM-4.6 Instruct |
| TruthfulQA | 83.6 | 83.8 | 0.2 | GLM-4.6 Instruct |
| HellaSwag | 87.7 | 90.6 | 2.9 | GLM-4.6 Instruct |
| MMMU | 78.1 | 78.9 | 0.8 | GLM-4.6 Instruct |
| Arena Elo | 1423.7 | 1426.6 | 2.9 | GLM-4.6 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, ERNIE 4.5 Max costs about $24.23 per million tokens and GLM-4.6 Instruct costs about $9.94. GLM-4.6 Instruct is roughly 59% cheaper than ERNIE 4.5 Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens, compared with 128,000 for ERNIE 4.5 Max. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, ERNIE 4.5 Max wins 5, GLM-4.6 Instruct wins 7, and 0 are tied. On the index average, ERNIE 4.5 Max leads GLM-4.6 Instruct by 0.2 points (80.6 versus 80.4).
The price picture favours GLM-4.6 Instruct. The context picture favours GLM-4.6 Instruct. 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 Max for quality-sensitive work and GLM-4.6 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose ERNIE 4.5 Max if
- You want the higher average benchmark score in this index.
Choose GLM-4.6 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is ERNIE 4.5 Max better than GLM-4.6 Instruct?
On the index average, ERNIE 4.5 Max leads GLM-4.6 Instruct by 0.2 points (80.6 versus 80.4). The better choice depends on price, context and your task. ERNIE 4.5 Max wins 5 benchmarks, GLM-4.6 Instruct wins 7.
Which is cheaper, ERNIE 4.5 Max or GLM-4.6 Instruct?
GLM-4.6 Instruct is cheaper on a blended 3:1 basis at about $9.94 per million tokens, versus $24.23 for ERNIE 4.5 Max.
Which has the larger context window?
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens. ERNIE 4.5 Max has 128,000 tokens.
Are these models open weights?
ERNIE 4.5 Max is proprietary; GLM-4.6 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with ERNIE 4.5 Max for quality-sensitive work and GLM-4.6 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.