o4-mini vs MiniMax-M2
Head-to-head specifications
| Metric | o4-mini | MiniMax-M2 | Difference |
|---|---|---|---|
| Intelligence Index | 29.0 | 30.0 | -3.3% |
| Context window | 256K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.64 | $0.36 | +77.8% |
| Output speed (tokens/s) | 167 | 77 | +116.9% |
| Access | Proprietary API | Open weights | — |
- MiniMax-M2 leads overall capability (Intelligence Index 30.0 vs 29.0).
- MiniMax-M2 is the cheaper model to run at $0.36/1M blended tokens — about 1.8× cheaper.
- MiniMax-M2 offers the larger context window (262K tokens), useful for long documents and codebases.
Verdict: o4-mini or MiniMax-M2?
o4-mini advantages
- Output speed (+54%)
MiniMax-M2 advantages
- Affordability (+44%)
Which should you choose?
- Choose the o4-mini if low latency and fast generation matter for your application.
- Choose the MiniMax-M2 if you want the lowest cost per token at scale.
Value for money
MiniMax-M2 offers more intelligence per dollar (1.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
o4-mini vs MiniMax-M2: which should you choose?
o4-mini — OpenAI multimodal model with an Intelligence Index of 29, a 256K-token context window and a blended price of $0.64/1M tokens.
MiniMax-M2 — MiniMax multimodal model with an Intelligence Index of 30, a 262K-token context window and a blended price of $0.36/1M tokens (open weights).
o4-mini vs MiniMax-M2: MiniMax-M2 scores higher on the Intelligence Index. MiniMax-M2 leads overall capability (Intelligence Index 30.0 vs 29.0). MiniMax-M2 is the cheaper model to run at $0.36/1M blended tokens — about 1.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiniMax-M2 scores 30.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiniMax-M2 accepts up to 262K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, o4-mini generates faster (167 vs 77 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiniMax-M2 is the cheaper model to run ($0.36 vs $0.64 per 1M tokens). o4-mini is proprietary api and MiniMax-M2 is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the o4-mini better than the MiniMax-M2?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. MiniMax-M2 leads overall capability (Intelligence Index 30.0 vs 29.0).
What is the main difference between the o4-mini and the MiniMax-M2?
MiniMax-M2 leads overall capability (Intelligence Index 30.0 vs 29.0). MiniMax-M2 is the cheaper model to run at $0.36/1M blended tokens — about 1.8× cheaper.
Which is better value?
MiniMax-M2 offers more intelligence per dollar (1.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the o4-mini if low latency and fast generation matter for your application. Choose the MiniMax-M2 if you want the lowest cost per token at scale.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.