| Signal | MiniMax M2.7 | Delta | o1-pro |
|---|---|---|---|
Capabilities | 67 | -- | |
Benchmarks | 67 | -14 | |
Pricing | 1 | -99 | |
Context window size | 84 | +0 | |
Recency | 100 | +36 | |
Output Capacity | 85 | +2 | |
| Overall Result | 3 wins | of 6 | 2 wins |
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MiniMax
OpenAI
MiniMax M2.7 saves you $44910.00/month
That's $538920.00/year compared to o1-pro at your current usage level of 100K calls/month.
| Metric | MiniMax M2.7 | o1-pro | Winner |
|---|---|---|---|
| Overall Score | 68 | 81 | o1-pro |
| Rank | #70 | #22 | o1-pro |
| Quality Rank | #70 | #22 | o1-pro |
| Adoption Rank | #70 | #22 | o1-pro |
| Parameters | -- | -- | -- |
| Context Window | 205K | 200K | MiniMax M2.7 |
| Pricing | $0.30/$1.20/M | $150.00/$600.00/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | MiniMax M2.7 |
| Benchmarks | 67 | 82 | o1-pro |
| Pricing | 1 | 100 | o1-pro |
| Context window size | 84 | 84 | MiniMax M2.7 |
| Recency | 100 | 64 | MiniMax M2.7 |
| Output Capacity | 85 | 83 | MiniMax M2.7 |
Our score (0-100) is driven by benchmark performance (90%) from LMArena Elo, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 68/100 (rank #70), placing it in the top 76% of all 290 models tracked.
Scores 81/100 (rank #22), placing it in the top 93% of all 290 models tracked.
o1-pro has a 13-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
MiniMax M2.7 offers 100% better value per quality point. At 1M tokens/day, you'd spend $22.50/month with MiniMax M2.7 vs $11250.00/month with o1-pro - a $11227.50 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. MiniMax M2.7 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (205K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.20/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (81/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
o1-pro clearly outperforms MiniMax M2.7 with a significant 12.900000000000006-point lead. For most general use cases, o1-pro is the stronger choice. However, MiniMax M2.7 may still excel in niche scenarios.
Best for Quality
MiniMax M2.7
Marginally better benchmark scores; both are excellent
Best for Cost
MiniMax M2.7
100% lower pricing; better value at scale
Best for Reliability
MiniMax M2.7
Higher uptime and faster response speeds
Best for Prototyping
MiniMax M2.7
Stronger community support and better developer experience
Best for Production
MiniMax M2.7
Wider enterprise adoption and proven at scale
by MiniMax
| Capability | MiniMax M2.7 | o1-pro |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
MiniMax
OpenAI
MiniMax M2.7 saves you $988.02/month
That's 100% cheaper than o1-pro at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | MiniMax M2.7 | o1-pro |
|---|---|---|
| Context Window | 205K | 200K |
| Max Output Tokens | 131,072 | 100,000 |
| Open Source | Yes | No |
| Created | Mar 18, 2026 | Mar 19, 2025 |
o1-pro scores 81/100 (rank #22) compared to MiniMax M2.7's 68/100 (rank #70), giving it a 13-point advantage. o1-pro is the stronger overall choice, though MiniMax M2.7 may excel in specific areas like cost efficiency.
MiniMax M2.7 is ranked #70 and o1-pro is ranked #22 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from LMArena, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
MiniMax M2.7 is cheaper at $1.20/M output tokens vs o1-pro's $600.00/M output tokens - 500.0x more expensive. Input token pricing: MiniMax M2.7 at $0.30/M vs o1-pro at $150.00/M.
MiniMax M2.7 has a larger context window of 204,800 tokens compared to o1-pro's 200,000 tokens. A larger context window means the model can process longer documents and conversations.