Head-to-head · multimodal vs multimodal

MiniMax-M2 Chat vs Nova Pro Max: which model should you choose?

We compared MiniMax-M2 Chat and Nova Pro Max on price, context window and 12 benchmarks. Nova Pro Max has the stronger average benchmark score; MiniMax-M2 Chat is cheaper on a blended 3:1 basis.

MiniMax

MiniMax-M2 Chat

MiniMax-M2 Chat is a multimodal model from MiniMax. It offers a 200,000-token context window, costs $5.02 per million input tokens and $18.95 per million output tokens, and averages 80.2 across the index's benchmark suite.

Input
$5.02 / 1M
Output
$18.95 / 1M
Context
200,000
Avg score
80.2
Open weights
Yes
Full MiniMax-M2 Chat page
Amazon

Nova Pro Max

Nova Pro Max is a multimodal model from Amazon. It offers a 1,000,000-token context window, costs $14.05 per million input tokens and $71.59 per million output tokens, and averages 81.2 across the index's benchmark suite.

Input
$14.05 / 1M
Output
$71.59 / 1M
Context
1,000,000
Avg score
81.2
Open weights
No
Full Nova Pro Max page

Specification comparison

SpecMiniMax-M2 ChatNova Pro MaxEdge
Input price / 1M$5.02$14.05A
Output price / 1M$18.95$71.59A
Context window200,0001,000,000B
Max output8,19216,384B
Average score80.281.2B
Arena Elo1443.01423.2A
Open weightsYesNoA

Benchmark comparison

MiniMax-M2 Chat wins 5, Nova Pro Max wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkMiniMax-M2 ChatNova Pro MaxGapWinner
MMLU-Pro84.384.20.1MiniMax-M2 Chat
GPQA Diamond77.878.30.5Nova Pro Max
SWE-bench Verified66.467.41.0Nova Pro Max
HumanEval84.287.53.3Nova Pro Max
MATH-50086.083.82.2MiniMax-M2 Chat
AIME 202578.379.31.0Nova Pro Max
LiveCodeBench70.477.06.6Nova Pro Max
IFEval88.086.41.6MiniMax-M2 Chat
TruthfulQA80.383.43.1Nova Pro Max
HellaSwag87.888.91.1Nova Pro Max
MMMU79.277.22.0MiniMax-M2 Chat
Arena Elo1443.01423.219.8MiniMax-M2 Chat

Price analysis

On a blended 3:1 input-to-output basis, MiniMax-M2 Chat costs about $8.50 per million tokens and Nova Pro Max costs about $28.44. MiniMax-M2 Chat is roughly 70% cheaper than Nova Pro Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Nova Pro Max has the larger context window at 1,000,000 tokens, compared with 200,000 for MiniMax-M2 Chat. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, MiniMax-M2 Chat wins 5, Nova Pro Max wins 7, and 0 are tied. On the index average, Nova Pro Max leads MiniMax-M2 Chat by 1.0 points (81.2 versus 80.2).

The price picture favours MiniMax-M2 Chat. The context picture favours Nova Pro Max. 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 Nova Pro Max for quality-sensitive work and MiniMax-M2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose MiniMax-M2 Chat if

  • Unit cost matters and you want the cheaper blended price.
  • You need open weights for self-hosting or fine-tuning.

Choose Nova Pro Max if

  • You want the higher average benchmark score in this index.
  • You need the larger 1,000,000-token context window.
  • You need the larger 16,384-token output limit.

FAQ

Is MiniMax-M2 Chat better than Nova Pro Max?

On the index average, Nova Pro Max leads MiniMax-M2 Chat by 1.0 points (81.2 versus 80.2). The better choice depends on price, context and your task. MiniMax-M2 Chat wins 5 benchmarks, Nova Pro Max wins 7.

Which is cheaper, MiniMax-M2 Chat or Nova Pro Max?

MiniMax-M2 Chat is cheaper on a blended 3:1 basis at about $8.50 per million tokens, versus $28.44 for Nova Pro Max.

Which has the larger context window?

Nova Pro Max has the larger context window at 1,000,000 tokens. MiniMax-M2 Chat has 200,000 tokens.

Are these models open weights?

MiniMax-M2 Chat is open weights; Nova Pro Max is proprietary.

Which should I choose for production?

If you need a single default, start with Nova Pro Max for quality-sensitive work and MiniMax-M2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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