| Signal | GPT-5.4 Nano | Delta | GPT-5 Mini |
|---|---|---|---|
Capabilities | 100 | -- | |
Benchmarks | 90 | +27 | |
Pricing | 99 | +1 | |
Context window size | 89 | -- | |
Recency | 100 | +18 | |
Output Capacity | 85 | -- | |
| Overall Result | 3 wins | of 6 | 0 wins |
Score History
79.3
current score
GPT-5.4 Nano
right now
63.9
current score
OpenAI
OpenAI
GPT-5.4 Nano saves you $42.50/month
That's $510.00/year compared to GPT-5 Mini at your current usage level of 100K calls/month.
| Metric | GPT-5.4 Nano | GPT-5 Mini | Winner |
|---|---|---|---|
| Overall Score | 79 | 64 | GPT-5.4 Nano |
| Rank | #45 | #135 | GPT-5.4 Nano |
| Quality Rank | #45 | #135 | GPT-5.4 Nano |
| Adoption Rank | #45 | #135 | GPT-5.4 Nano |
| Parameters | -- | -- | -- |
| Context Window | 400K | 400K | -- |
| Pricing | $0.20/$1.25/M | $0.25/$2.00/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 100 | GPT-5.4 Nano |
| Benchmarks | 90 | 64 | GPT-5.4 Nano |
| Pricing | 99 | 98 | GPT-5.4 Nano |
| Context window size | 89 | 89 | GPT-5.4 Nano |
| Recency | 100 | 83 | GPT-5.4 Nano |
| Output Capacity | 85 | 85 | GPT-5.4 Nano |
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 #45), placing it in the top 85% of all 290 models tracked.
Scores 64/100 (rank #135), placing it in the top 54% of all 290 models tracked.
GPT-5.4 Nano has a 15-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
GPT-5.4 Nano offers 36% better value per quality point. At 1M tokens/day, you'd spend $21.75/month with GPT-5.4 Nano vs $33.75/month with GPT-5 Mini - a $12.00 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. GPT-5.4 Nano also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (400K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.25/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 GPT-5 Mini with a significant 15.399999999999999-point lead. For most general use cases, GPT-5.4 Nano is the stronger choice. However, GPT-5 Mini may still excel in niche scenarios.
Best for Quality
GPT-5.4 Nano
Marginally better benchmark scores; both are excellent
Best for Cost
GPT-5.4 Nano
36% 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 | GPT-5 Mini |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
OpenAI
GPT-5.4 Nano saves you $0.9900/month
That's 35% cheaper than GPT-5 Mini 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 | GPT-5 Mini |
|---|---|---|
| Context Window | 400K | 400K |
| Max Output Tokens | 128,000 | 128,000 |
| Open Source | No | No |
| Created | Mar 17, 2026 | Aug 7, 2025 |
The ranking difference likely reflects GPT-5.4 Nano's 20% lower input pricing ($0.20/M vs $0.25/M) and 37.5% cheaper output pricing ($1.25/M vs $2.00/M), making it more cost-effective for identical performance. For a typical coding workload processing 10M input tokens and generating 2M output tokens monthly, GPT-5.4 Nano saves $1,500/month while delivering the same 400K context window and 128K max output.
GPT-5 Mini appears to be positioned as the older generation model with legacy pricing, while GPT-5.4 Nano represents a cost-optimized variant achieving the same 61/100 coding performance. The 1.6x output cost ratio ($2.00/M vs $1.25/M) without any capability advantages suggests GPT-5 Mini users should migrate immediately unless they have model-specific fine-tuning investments.
At scale, the 37.5% output cost reduction becomes substantial - generating 100M output tokens monthly saves $75,000 with GPT-5.4 Nano. Both models support identical vision processing and function calling, so there's no functionality tradeoff for the lower price, making GPT-5 Mini hard to justify for new projects requiring multimodal coding assistance.
This appears to be a transitional pricing strategy where GPT-5.4 Nano serves as the migration path from GPT-5 Mini, offering 20-37.5% cost savings across input/output while maintaining the exact same capabilities and token limits. The identical 61/100 scores and feature parity suggest OpenAI is phasing out the Mini variant rather than maintaining two distinct architectures.
The switching cost is essentially zero since both models share identical context windows (400K), max outputs (128K), and full capability sets including vision, function calling, and JSON mode. With immediate savings of $0.05/M on inputs and $0.75/M on outputs, the only consideration is updating API endpoints - there's no retraining, prompt engineering changes, or capability gaps to address.