ERNIE 4.5 Reasoning vs MiniMax-M2: which model should you choose?
We compared ERNIE 4.5 Reasoning and MiniMax-M2 on price, context window and 12 benchmarks. MiniMax-M2 has the stronger average benchmark score; MiniMax-M2 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
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 Reasoning | MiniMax-M2 | Edge |
|---|---|---|---|
| Input price / 1M | $14.36 | $5.19 | B |
| Output price / 1M | $78.75 | $22.73 | B |
| Context window | 1,000,000 | 200,000 | A |
| Max output | 8,192 | 65,536 | B |
| Average score | 79.8 | 80.6 | B |
| Arena Elo | 1421.5 | 1423.8 | B |
| Open weights | No | Yes | B |
Benchmark comparison
ERNIE 4.5 Reasoning wins 3, MiniMax-M2 wins 8, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | ERNIE 4.5 Reasoning | MiniMax-M2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.6 | 81.8 | 1.2 | MiniMax-M2 |
| GPQA Diamond | 75.7 | 75.4 | 0.3 | ERNIE 4.5 Reasoning |
| SWE-bench Verified | 69.8 | 69.9 | 0.1 | MiniMax-M2 |
| HumanEval | 85.0 | 88.0 | 3.0 | MiniMax-M2 |
| MATH-500 | 82.5 | 82.3 | 0.2 | ERNIE 4.5 Reasoning |
| AIME 2025 | 78.1 | 78.1 | 0.0 | Tie |
| LiveCodeBench | 71.9 | 73.4 | 1.5 | MiniMax-M2 |
| IFEval | 88.7 | 86.1 | 2.6 | ERNIE 4.5 Reasoning |
| TruthfulQA | 82.9 | 83.5 | 0.6 | MiniMax-M2 |
| HellaSwag | 88.0 | 90.4 | 2.4 | MiniMax-M2 |
| MMMU | 74.4 | 78.0 | 3.6 | MiniMax-M2 |
| Arena Elo | 1421.5 | 1423.8 | 2.3 | MiniMax-M2 |
Price analysis
On a blended 3:1 input-to-output basis, ERNIE 4.5 Reasoning costs about $30.46 per million tokens and MiniMax-M2 costs about $9.57. MiniMax-M2 is roughly 69% 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.
ERNIE 4.5 Reasoning has the larger context window at 1,000,000 tokens, compared with 200,000 for MiniMax-M2. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, ERNIE 4.5 Reasoning wins 3, MiniMax-M2 wins 8, and 1 are tied. On the index average, MiniMax-M2 leads ERNIE 4.5 Reasoning by 0.8 points (80.6 versus 79.8).
The price picture favours MiniMax-M2. The context picture favours ERNIE 4.5 Reasoning. 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 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose ERNIE 4.5 Reasoning if
- You need the larger 1,000,000-token context window.
- The workload needs multi-step reasoning.
Choose MiniMax-M2 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 65,536-token output limit.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is ERNIE 4.5 Reasoning better than MiniMax-M2?
On the index average, MiniMax-M2 leads ERNIE 4.5 Reasoning by 0.8 points (80.6 versus 79.8). The better choice depends on price, context and your task. ERNIE 4.5 Reasoning wins 3 benchmarks, MiniMax-M2 wins 8.
Which is cheaper, ERNIE 4.5 Reasoning or MiniMax-M2?
MiniMax-M2 is cheaper on a blended 3:1 basis at about $9.57 per million tokens, versus $30.46 for ERNIE 4.5 Reasoning.
Which has the larger context window?
ERNIE 4.5 Reasoning has the larger context window at 1,000,000 tokens. MiniMax-M2 has 200,000 tokens.
Are these models open weights?
ERNIE 4.5 Reasoning is proprietary; MiniMax-M2 is open weights.
Which should I choose for production?
If you need a single default, start with MiniMax-M2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.