Llama 4 Maverick Instruct vs MiniMax-M2: which model should you choose?
We compared Llama 4 Maverick Instruct and MiniMax-M2 on price, context window and 12 benchmarks. Llama 4 Maverick Instruct has the stronger average benchmark score; MiniMax-M2 is cheaper on a blended 3:1 basis.
Llama 4 Maverick Instruct
Llama 4 Maverick Instruct is a multimodal model from Meta AI. It offers a 200,000-token context window, costs $5.33 per million input tokens and $23.04 per million output tokens, and averages 81.7 across the index's benchmark suite.
- Input
- $5.33 / 1M
- Output
- $23.04 / 1M
- Context
- 200,000
- Avg score
- 81.7
- Open weights
- Yes
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 | Llama 4 Maverick Instruct | MiniMax-M2 | Edge |
|---|---|---|---|
| Input price / 1M | $5.33 | $5.19 | B |
| Output price / 1M | $23.04 | $22.73 | B |
| Context window | 200,000 | 200,000 | Tie |
| Max output | 16,384 | 65,536 | B |
| Average score | 81.7 | 80.6 | A |
| Arena Elo | 1438.5 | 1423.8 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
Llama 4 Maverick Instruct wins 9, MiniMax-M2 wins 3, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Llama 4 Maverick Instruct | MiniMax-M2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.6 | 81.8 | 2.8 | Llama 4 Maverick Instruct |
| GPQA Diamond | 79.5 | 75.4 | 4.1 | Llama 4 Maverick Instruct |
| SWE-bench Verified | 69.1 | 69.9 | 0.8 | MiniMax-M2 |
| HumanEval | 85.0 | 88.0 | 3.0 | MiniMax-M2 |
| MATH-500 | 83.4 | 82.3 | 1.1 | Llama 4 Maverick Instruct |
| AIME 2025 | 79.9 | 78.1 | 1.8 | Llama 4 Maverick Instruct |
| LiveCodeBench | 75.8 | 73.4 | 2.4 | Llama 4 Maverick Instruct |
| IFEval | 88.9 | 86.1 | 2.8 | Llama 4 Maverick Instruct |
| TruthfulQA | 84.0 | 83.5 | 0.5 | Llama 4 Maverick Instruct |
| HellaSwag | 89.6 | 90.4 | 0.8 | MiniMax-M2 |
| MMMU | 79.0 | 78.0 | 1.0 | Llama 4 Maverick Instruct |
| Arena Elo | 1438.5 | 1423.8 | 14.7 | Llama 4 Maverick Instruct |
Price analysis
On a blended 3:1 input-to-output basis, Llama 4 Maverick Instruct costs about $9.76 per million tokens and MiniMax-M2 costs about $9.57. MiniMax-M2 is roughly 2% cheaper than Llama 4 Maverick Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 200,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Llama 4 Maverick Instruct wins 9, MiniMax-M2 wins 3, and 0 are tied. On the index average, Llama 4 Maverick Instruct leads MiniMax-M2 by 1.1 points (81.7 versus 80.6).
The price picture favours MiniMax-M2. 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 Llama 4 Maverick Instruct for quality-sensitive work and MiniMax-M2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Llama 4 Maverick Instruct if
- You want the higher average benchmark score in this index.
Choose MiniMax-M2 if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 65,536-token output limit.
FAQ
Is Llama 4 Maverick Instruct better than MiniMax-M2?
On the index average, Llama 4 Maverick Instruct leads MiniMax-M2 by 1.1 points (81.7 versus 80.6). The better choice depends on price, context and your task. Llama 4 Maverick Instruct wins 9 benchmarks, MiniMax-M2 wins 3.
Which is cheaper, Llama 4 Maverick Instruct or MiniMax-M2?
MiniMax-M2 is cheaper on a blended 3:1 basis at about $9.57 per million tokens, versus $9.76 for Llama 4 Maverick Instruct.
Which has the larger context window?
Both models list a 200,000-token context window.
Are these models open weights?
Llama 4 Maverick Instruct is open weights; MiniMax-M2 is open weights.
Which should I choose for production?
If you need a single default, start with Llama 4 Maverick Instruct for quality-sensitive work and MiniMax-M2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.