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OpenAI's efficient GPT-5.4 mini instruct model, built for coding and agentic workloads with optional reasoning depth and a 400K-token multimodal context window.
Overview. GPT-5.4 mini is OpenAI's efficient instruct model in the GPT-5 family, released in March 2026. It's a closed-weight, proprietary model with a 400K-token context window and text, image, and file input paired with text output. Reasoning is available but off by default, controlled through a reasoning-effort flag rather than being always-on, which makes it a practical pick for developers who want GPT-5-class capability without paying the latency cost of mandatory extended thinking.
Key strengths. Benchmark signals from Artificial Analysis place GPT-5.4 mini's coding index meaningfully ahead of its general intelligence index, indicating it punches above its tier on code-related tasks specifically. It supports tool calling, which fits its positioning as a workhorse for agentic pipelines rather than a pure chat model. The 400K-token context window and multimodal (text, image, file) input let it handle document-heavy or long-running agent sessions without external chunking.
When to use it. Reach for GPT-5.4 mini when you need interactive-latency responses for coding assistants, agent loops, or document-understanding tasks of medium complexity, and don't require the deepest reasoning tier in the GPT-5 lineup. Turn on its reasoning-effort flag (low through xhigh) when a specific step needs more deliberation, and leave it off for the bulk of routine tool-calling or drafting work where speed matters more. Its large context window also makes it a fit for workflows that mix long documents or images with an ongoing task.
Limitations. GPT-5.4 mini sits in the middle of the GPT-5 family's quality range — it is not the family's dedicated reasoning-tier model, so tasks demanding the highest intelligence scores are better served by other GPT-5 variants. It has no audio input or output, and its training data reaches only through August 2025. It also doesn't expose a temperature parameter; behavior is tuned instead through the reasoning-effort setting.
Sovereignty note. GPT-5.4 mini is built by OpenAI, a US-based creator subject to CLOUD Act exposure, so this model does not carry Stav's Sovereign designation.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.67 / 4.03+3 rates | Online |
15 use cases classified by the curator. The note says why.
Curated description explicitly names coding and agentic workloads as the design target, backed by tool-calling and extended-thinking depth.
Tool-calling is a first-class capability and the model is positioned for agentic/coding workloads, matching the structured function-calling use case directly.
Native tool-calling plus extended-thinking mode and a design explicitly targeting agentic workloads make this a strong fit for orchestrating multi-tool pipelines.
Tool-calling plus vision supports screen-aware automation loops, but no browser-specific benchmark or curation confirms class-leading performance here.
Extended-thinking mode aids root-cause reasoning over code, positioned as a cost-efficient alternative to flagship GPT-5 variants for this task.
Coding-focused instruct model with tool-calling; efficient mini tier suits high-volume completion tasks where full flagship reasoning isn't required.
400K-token context window allows reviewing large diffs or files in a single pass, though this is the efficiency-tier model rather than the flagship reasoning variant.
Efficient mini-tier positioning makes it a cost-effective choice for high-volume chatbot deployment, prompt-caching further reduces repeated-context cost.
400K-token context window comfortably fits long documents for single-pass Q&A, though reasoning depth is lighter than flagship GPT-5 tiers.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, ifbench, aa-multilingual-index, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-5.4-mini",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at