ERNIE 4.5 Reasoning vs GLM-4.6 Instruct: which model should you choose?
We compared ERNIE 4.5 Reasoning and GLM-4.6 Instruct on price, context window and 12 benchmarks. GLM-4.6 Instruct has the stronger average benchmark score; GLM-4.6 Instruct is cheaper on a blended 3:1 basis.
ERNIE 4.5 Reasoning
ERNIE 4.5 Reasoning is a multimodal model from Baidu. It offers a 1,000,000-token context window, costs $14.36 per million input tokens and $78.75 per million output tokens, and averages 79.8 across the index's benchmark suite.
- Input
- $14.36 / 1M
- Output
- $78.75 / 1M
- Context
- 1,000,000
- Avg score
- 79.8
- 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 Reasoning | GLM-4.6 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $14.36 | $5.15 | B |
| Output price / 1M | $78.75 | $24.32 | B |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 8,192 | 128,000 | B |
| Average score | 79.8 | 80.4 | B |
| Arena Elo | 1421.5 | 1426.6 | B |
| Open weights | No | Yes | B |
Benchmark comparison
ERNIE 4.5 Reasoning wins 2, GLM-4.6 Instruct wins 9, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | ERNIE 4.5 Reasoning | GLM-4.6 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.6 | 80.6 | 0.0 | Tie |
| GPQA Diamond | 75.7 | 75.8 | 0.1 | GLM-4.6 Instruct |
| SWE-bench Verified | 69.8 | 70.5 | 0.7 | GLM-4.6 Instruct |
| HumanEval | 85.0 | 85.3 | 0.3 | GLM-4.6 Instruct |
| MATH-500 | 82.5 | 84.9 | 2.4 | GLM-4.6 Instruct |
| AIME 2025 | 78.1 | 74.5 | 3.6 | ERNIE 4.5 Reasoning |
| LiveCodeBench | 71.9 | 72.6 | 0.7 | GLM-4.6 Instruct |
| IFEval | 88.7 | 87.4 | 1.3 | ERNIE 4.5 Reasoning |
| TruthfulQA | 82.9 | 83.8 | 0.9 | GLM-4.6 Instruct |
| HellaSwag | 88.0 | 90.6 | 2.6 | GLM-4.6 Instruct |
| MMMU | 74.4 | 78.9 | 4.5 | GLM-4.6 Instruct |
| Arena Elo | 1421.5 | 1426.6 | 5.1 | GLM-4.6 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, ERNIE 4.5 Reasoning costs about $30.46 per million tokens and GLM-4.6 Instruct costs about $9.94. GLM-4.6 Instruct is roughly 67% cheaper than ERNIE 4.5 Reasoning. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 1,000,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, ERNIE 4.5 Reasoning wins 2, GLM-4.6 Instruct wins 9, and 1 are tied. On the index average, GLM-4.6 Instruct leads ERNIE 4.5 Reasoning by 0.6 points (80.4 versus 79.8).
The price picture favours GLM-4.6 Instruct. The context picture is effectively a tie. 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 Instruct for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose ERNIE 4.5 Reasoning if
- The workload needs multi-step reasoning.
Choose GLM-4.6 Instruct 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 128,000-token output limit.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is ERNIE 4.5 Reasoning better than GLM-4.6 Instruct?
On the index average, GLM-4.6 Instruct leads ERNIE 4.5 Reasoning by 0.6 points (80.4 versus 79.8). The better choice depends on price, context and your task. ERNIE 4.5 Reasoning wins 2 benchmarks, GLM-4.6 Instruct wins 9.
Which is cheaper, ERNIE 4.5 Reasoning 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 $30.46 for ERNIE 4.5 Reasoning.
Which has the larger context window?
Both models list a 1,000,000-token context window.
Are these models open weights?
ERNIE 4.5 Reasoning is proprietary; GLM-4.6 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with GLM-4.6 Instruct for quality-sensitive work, and check whether its price holds at your expected volume before committing.