| Signal | GPT-5.4 Nano | Delta | MiniMax-01 |
|---|---|---|---|
Capabilities | 100 | +67 | |
Benchmarks | 90 | +90 | |
Pricing | 99 | 0 | |
Context window size | 89 | -6 | |
Recency | 100 | +55 | |
Output Capacity | 85 | -15 | |
| Overall Result | 3 wins | of 6 | 3 wins |
Score History
79.3
current score
GPT-5.4 Nano
right now
40
current score
OpenAI
MiniMax
MiniMax-01 saves you $7.50/month
That's $90.00/year compared to GPT-5.4 Nano at your current usage level of 100K calls/month.
| Metric | GPT-5.4 Nano | MiniMax-01 | Winner |
|---|---|---|---|
| Overall Score | 79 | 40 | GPT-5.4 Nano |
| Rank | #42 | #298 | GPT-5.4 Nano |
| Quality Rank | #42 | #298 | GPT-5.4 Nano |
| Adoption Rank | #42 | #298 | GPT-5.4 Nano |
| Parameters | -- | -- | -- |
| Context Window | 400K | 1000K | MiniMax-01 |
| Pricing | $0.20/$1.25/M | $0.20/$1.10/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 33 | GPT-5.4 Nano |
| Benchmarks | 90 | -- | GPT-5.4 Nano |
| Pricing | 99 | 99 | MiniMax-01 |
| Context window size | 89 | 95 | MiniMax-01 |
| Recency | 100 | 45 | GPT-5.4 Nano |
| Output Capacity | 85 | 100 | MiniMax-01 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 79/100 (rank #42), placing it in the top 86% of all 290 models tracked.
Scores 40/100 (rank #298), placing it in the top -2% of all 290 models tracked.
GPT-5.4 Nano has a 39-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
GPT-5.4 Nano offers 10% better value per quality point. At 1M tokens/day, you'd spend $19.50/month with MiniMax-01 vs $21.75/month with GPT-5.4 Nano - a $2.25 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
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. MiniMax-01 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (79/100) 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
GPT-5.4 Nano clearly outperforms MiniMax-01 with a significant 39.3-point lead. For most general use cases, GPT-5.4 Nano is the stronger choice. However, MiniMax-01 may still excel in niche scenarios.
Best for Quality
GPT-5.4 Nano
Marginally better benchmark scores; both are excellent
Best for Cost
MiniMax-01
10% lower pricing; better value at scale
Best for Reliability
GPT-5.4 Nano
Higher uptime and faster response speeds
Best for Prototyping
GPT-5.4 Nano
Stronger community support and better developer experience
Best for Production
GPT-5.4 Nano
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-5.4 Nano | MiniMax-01 |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Searchdiffers | ||
| Image Output |
OpenAI
MiniMax
MiniMax-01 saves you $0.1800/month
That's 10% cheaper than GPT-5.4 Nano 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 | GPT-5.4 Nano | MiniMax-01 |
|---|---|---|
| Context Window | 400K | 1.0M |
| Max Output Tokens | 128,000 | 1,000,192 |
| Open Source | No | Yes |
| Created | Mar 17, 2026 | Jan 15, 2025 |
The identical scores suggest performance parity on core coding tasks, but MiniMax-01's higher rank likely reflects its 2.5x larger context window (1M vs 400K tokens) which matters more for large-scale code analysis. However, GPT-5.4 Nano compensates with exclusive capabilities like Function Calling and JSON Mode that MiniMax-01 lacks, making the score equivalence justified despite the ranking gap.
At $0.20/M input (identical for both) and $1.25/M vs $1.10/M output, you'd save $3,000/month ($16,000 + $25,000 = $41,000 for GPT-5.4 Nano vs $16,000 + $22,000 = $38,000 for MiniMax-01). This 7.3% cost reduction comes with the tradeoff of losing Function Calling, JSON Mode, and Web Search capabilities that many coding workflows depend on.
MiniMax-01's open-source nature allows self-hosting and fine-tuning for specialized coding domains, which could push performance beyond the baseline 61/100 score. Combined with its 1M token context window (2.5x GPT-5.4 Nano's 400K), teams working with large codebases can process entire repositories in single passes while maintaining data sovereignty.
GPT-5.4 Nano's unique file->text modality means it can directly process PDFs, images of architecture diagrams, and binary documentation that MiniMax-01 cannot handle. Despite the $0.15/M higher output cost, teams save engineering hours by avoiding preprocessing steps, though they sacrifice 600K tokens of context window that could be critical for analyzing monolithic applications.
GPT-5.4 Nano's Reasoning capability enables multi-step debugging and architectural decisions that go beyond pattern matching, while Web Search allows real-time API documentation lookups and library updates. For a coding assistant processing 50M output tokens monthly, the extra $7,500/month ($62,500 vs $55,000) buys capabilities that can reduce debugging time by catching logical errors MiniMax-01's 61/100 performance might miss.