OpenAI Releases GPT-5.4 mini and nano: Major Performance Gains at 3-4x Price

OpenAI has launched two new compact models—GPT-5.4 mini and GPT-5.4 nano—designed for coding assistants, subagents, and computer control tasks. Both models deliver substantial capability improvements over their GPT-5 predecessors but come with steep price increases.

Pricing and Specifications

GPT-5.4 mini is available via API, Codex, and ChatGPT at $0.75 per million input tokens and $4.50 per million output tokens—a 3x increase on input and 2.25x on output versus GPT-5 mini's $0.25/$2.00 pricing.

GPT-5.4 nano is API-only at $0.20 per million input tokens and $1.25 per million output tokens—a 4x increase on input and 3.125x on output compared to GPT-5 nano's $0.05/$0.40 pricing.

Both models support a 400,000-token context window.

Performance Benchmarks

GPT-5.4 mini demonstrates substantial gains across multiple benchmark categories:

Coding Tasks:

• SWE-Bench Pro: 54.4% (vs. 45.7% for GPT-5 mini, 57.7% for GPT-5.4)
• Terminal-Bench 2.0: 60.0% (vs. 38.2% for GPT-5 mini)

Computer Control:

• OSWorld-Verified: 72.1% (vs. 42.0% for GPT-5 mini, 75.0% for GPT-5.4)

Tool Usage:

• Toolathlon: 42.9% (vs. 26.9% for GPT-5 mini)
• MCP Atlas: 57.7% (vs. 47.6% for GPT-5 mini)

Reasoning:

• GPQA Diamond: 88.0% (vs. 81.6% for GPT-5 mini, 93.0% for GPT-5.4)

GPT-5.4 nano, the smallest option, achieves 52.4% on SWE-Bench Pro and 82.8% on GPQA Diamond, making it suitable for classification, data extraction, and simpler coding subtasks.

Subagent Architecture

OpenAI showcases a multi-tiered approach in its Codex platform: GPT-5.4 handles planning, coordination, and final evaluation, while delegating parallel subtasks to GPT-5.4 mini or nano agents. These subtasks include codebase searching, large file scanning, and document processing.

According to OpenAI, this architecture allows GPT-5.4 mini to consume only 30% of GPT-5.4's quota in Codex deployments, reducing costs for simpler tasks to approximately one-third of full-model pricing.

Both models run significantly faster than their predecessors, with GPT-5.4 mini running more than twice as fast as GPT-5 mini.

What This Means

OpenAI is pursuing a deliberate strategy of performance-per-dollar at smaller model sizes rather than aggressive price competition. The 3-4x pricing increases reflect the engineering required to pack near-full-model performance into compact form factors. For developers building agentic systems and coding tools, GPT-5.4 mini's 72.1% OSWorld score represents a watershed moment—computer control capability has historically been the exclusive domain of large models.

The subagent pattern OpenAI demonstrates has immediate practical applications: orchestrating larger models for complex reasoning while routing simple tasks to cheaper compact models can reduce total API costs below full-model approaches. However, adopters should carefully benchmark against their specific use cases before upgrading from GPT-5 mini, as the pricing delta is substantial and may not justify the capability gain for all workloads.