ERNIE 4.5 Max vs MiniMax-M2: which model should you choose?
We compared ERNIE 4.5 Max and MiniMax-M2 on price, context window and 12 benchmarks. ERNIE 4.5 Max and MiniMax-M2 are effectively tied on the index average; MiniMax-M2 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
MiniMax-M2
MiniMax-M2 is a multimodal model from MiniMax. It offers a 200,000-token context window, costs $5.19 per million input tokens and $22.73 per million output tokens, and averages 80.6 across the index's benchmark suite.
- Input
- $5.19 / 1M
- Output
- $22.73 / 1M
- Context
- 200,000
- Avg score
- 80.6
- Open weights
- Yes
Specification comparison
| Spec | ERNIE 4.5 Max | MiniMax-M2 | Edge |
|---|---|---|---|
| Input price / 1M | $14.41 | $5.19 | B |
| Output price / 1M | $53.71 | $22.73 | B |
| Context window | 128,000 | 200,000 | B |
| Max output | 65,536 | 65,536 | Tie |
| Average score | 80.6 | 80.6 | Tie |
| Arena Elo | 1423.7 | 1423.8 | B |
| Open weights | No | Yes | B |
Benchmark comparison
ERNIE 4.5 Max wins 6, MiniMax-M2 wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | ERNIE 4.5 Max | MiniMax-M2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.8 | 81.8 | 1.0 | MiniMax-M2 |
| GPQA Diamond | 76.4 | 75.4 | 1.0 | ERNIE 4.5 Max |
| SWE-bench Verified | 68.9 | 69.9 | 1.0 | MiniMax-M2 |
| HumanEval | 87.7 | 88.0 | 0.3 | MiniMax-M2 |
| MATH-500 | 84.2 | 82.3 | 1.9 | ERNIE 4.5 Max |
| AIME 2025 | 79.5 | 78.1 | 1.4 | ERNIE 4.5 Max |
| LiveCodeBench | 73.0 | 73.4 | 0.4 | MiniMax-M2 |
| IFEval | 87.1 | 86.1 | 1.0 | ERNIE 4.5 Max |
| TruthfulQA | 83.6 | 83.5 | 0.1 | ERNIE 4.5 Max |
| HellaSwag | 87.7 | 90.4 | 2.7 | MiniMax-M2 |
| MMMU | 78.1 | 78.0 | 0.1 | ERNIE 4.5 Max |
| Arena Elo | 1423.7 | 1423.8 | 0.1 | MiniMax-M2 |
Price analysis
On a blended 3:1 input-to-output basis, ERNIE 4.5 Max costs about $24.23 per million tokens and MiniMax-M2 costs about $9.57. MiniMax-M2 is roughly 60% 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.
MiniMax-M2 has the larger context window at 200,000 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 6, MiniMax-M2 wins 6, and 0 are tied. The two models are effectively tied on the index average at 80.6.
The price picture favours MiniMax-M2. The context picture favours MiniMax-M2. 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 MiniMax-M2; it is cheaper on a blended basis and has the larger context window. Test it on your own traffic before committing.
Choose ERNIE 4.5 Max if
- You prefer Baidu's tooling or already use it.
Choose MiniMax-M2 if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 200,000-token context window.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is ERNIE 4.5 Max better than MiniMax-M2?
The two models are effectively tied on the index average at 80.6. The better choice depends on price, context and your task. ERNIE 4.5 Max wins 6 benchmarks, MiniMax-M2 wins 6.
Which is cheaper, ERNIE 4.5 Max or MiniMax-M2?
MiniMax-M2 is cheaper on a blended 3:1 basis at about $9.57 per million tokens, versus $24.23 for ERNIE 4.5 Max.
Which has the larger context window?
MiniMax-M2 has the larger context window at 200,000 tokens. ERNIE 4.5 Max has 128,000 tokens.
Are these models open weights?
ERNIE 4.5 Max is proprietary; MiniMax-M2 is open weights.
Which should I choose for production?
If you need a single default, start with MiniMax-M2; it is cheaper on a blended basis and has the larger context window. Test it on your own traffic before committing.