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GPT-4o mini vs MiniMax M3.1 Flash

MiniMax M3.1 Flash leads the LLM Stats Score 48.5 to 3.3. GPT-4o mini and MiniMax M3.1 Flash cost the same.

OpenAI · MiniMax · Updated for 2026

Which is better?

MiniMax M3.1 Flash leads the overall LLM Stats Score 48.5 to 3.3, ranking #29 overall.

MiniMax M3.1 Flash also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GPT-4o mini

  • you want predictable pricing at $0.15/M input and $0.60/M output

Choose MiniMax M3.1 Flash

  • overall performance matters — it scores 48.5 and ranks #29 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you process long inputs — it offers a 1,000,000 token context window
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

GPT-4o mini
OpenAI
MiniMax M3.1 Flash
MiniMax
Core performance indexes
LLM Stats Score
3.3
#332
48.5
#29
Reasoning
3.7
#321
45.8
#34
Coding
-0.8
#267
37.8
#23
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.15 / M
$0.15 / M
Output price
$0.60 / M
$0.60 / M
Context window
128,000
1,000,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
GPT-4o mini
MiniMax M3.1 Flash
Vision
-0.4#204
28.8#42
Multimodal
2.4#164
30.2#38
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

9 reported for GPT-4o mini · 14 for MiniMax M3.1 Flash

No common benchmarks found

GPT-4o mini and MiniMax M3.1 Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

For input processing, GPT-4o mini ($0.15/1M tokens) costs the same as MiniMax M3.1 Flash ($0.15/1M tokens).

For output processing, GPT-4o mini ($0.60/1M tokens) costs the same as MiniMax M3.1 Flash ($0.60/1M tokens).

In conclusion, GPT-4o mini and MiniMax M3.1 Flash cost the same.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Oct 20 2026 • llm-stats.com
OpenAI
GPT-4o mini
Input tokens$0.15
Output tokens$0.60
Best providerAzure
MiniMax
MiniMax M3.1 Flash
Input tokens$0.15
Output tokens$0.60
Best providerMiniMax
Notice missing or incorrect data?

Context Window

Maximum input and output token capacity

MiniMax M3.1 Flash accepts 1,000,000 input tokens compared to GPT-4o mini's 128,000 tokens. MiniMax M3.1 Flash can generate longer responses up to 524,288 tokens, while GPT-4o mini is limited to 16,384 tokens.

OpenAI
GPT-4o mini
Input128,000 tokens
Output16,384 tokens
MiniMax
MiniMax M3.1 Flash
Input1,000,000 tokens
Output524,288 tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both GPT-4o mini and MiniMax M3.1 Flash support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

GPT-4o mini

Text
Images
Audio
Video

MiniMax M3.1 Flash

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

GPT-4o mini

Proprietary

Closed source

MiniMax M3.1 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

GPT-4o mini was released on 2024-07-18, while MiniMax M3.1 Flash was released on 2026-10-19.

MiniMax M3.1 Flash is 27 months newer than GPT-4o mini.

GPT-4o mini

Jul 18, 2024

2.3 years ago

MiniMax M3.1 Flash

Oct 19, 2026

0 days ago

2.3yr newer

Knowledge Cutoff

When training data ends

GPT-4o mini has a documented knowledge cutoff of 2023-10-01, while MiniMax M3.1 Flash's cutoff date is not specified.

We can confirm GPT-4o mini's training data extends to 2023-10-01, but cannot make a direct comparison without MiniMax M3.1 Flash's cutoff date.

GPT-4o mini

Oct 2023

MiniMax M3.1 Flash

—

Provider Availability

GPT-4o mini is available from Azure. MiniMax M3.1 Flash is available from MiniMax.

GPT-4o mini

azure logo
Azure
Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M

MiniMax M3.1 Flash

minimax logo
MiniMax
Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against GPT-4o mini and MiniMax M3.1 Flash side-by-side, then vote on the output you prefer.

GPT-4o mini
✓ Preferred
MiniMax M3.1 Flash
Open in Playground

FAQ

Common questions about GPT-4o mini vs MiniMax M3.1 Flash.

Which is better, GPT-4o mini or MiniMax M3.1 Flash?

MiniMax M3.1 Flash leads the LLM Stats Score 48.5 to 3.3. GPT-4o mini is made by OpenAI and MiniMax M3.1 Flash is made by MiniMax. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GPT-4o mini compare to MiniMax M3.1 Flash in benchmarks?

GPT-4o mini scores HumanEval: 87.2%, MGSM: 87.0%, MMLU: 82.0%, DROP: 79.7%, MATH: 70.2%. MiniMax M3.1 Flash scores BabyVision: 91.0%, CharXiv-R: 82.5%, CyberGym: 82.2%, BankerToolBench: 76.2%, DeepSWE 1.1: 73.7%.

Is GPT-4o mini cheaper than MiniMax M3.1 Flash?

Both models cost $0.15 per million input tokens.

What are the context window sizes for GPT-4o mini and MiniMax M3.1 Flash?

GPT-4o mini supports 128K tokens and MiniMax M3.1 Flash supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GPT-4o mini and MiniMax M3.1 Flash?

Key differences include LLM Stats Score (3.3 vs 48.5), context window (128K vs 1.0M). See the full comparison above for benchmark-by-benchmark results.

Who makes GPT-4o mini and MiniMax M3.1 Flash?

GPT-4o mini is developed by OpenAI and MiniMax M3.1 Flash is developed by MiniMax.

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