OpenAI
OpenAI: GPT-4.1 Mini
openai/gpt-4.1-mini
GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost.
- Context window: 1,047,576 tokens
- Input: image, text, file
- Output: text
- Pricing: $0.4/M input tokens, $1.6/M output tokens
Community sentiment
Positive (+25) — 4 positive, 4 negative, 5 neutral across 13 community samples from X and the web, as of 2026-10-02.
Community reactions to GPT-4.1 Mini are mixed: users highlight inexpensive, accurate classification and strong benchmarked answering performance in some systems, while others call out high latency and note faster, lower-cost alternatives that outperform it on certain tasks. Discussions focus on cost/efficiency, latency for long-context queries, and comparative benchmarks against newer specialized
- “We improved our memory system Mnemon with a System 1 model: Jev from @typesafeai, which judges raw records better and 3–11× faster than LLMs. With Jev as its System 1, Mnemon scores 91.7% on LoCoMo, first of 15 systems answering with gpt-4.1-mini, at very high token efficiency. h” — X
- “On the same records, Jev separates gold evidence better than DeepSeek and gpt-4.1-mini (AUC 0.942 vs 0.900 and 0.853) and is 3–11× faster. Fast judging keeps raw records affordable: from 100K to 10M tokens on BEAM, cost per question grows only 1.11×. https://t.co/HGse49mTZf” — X
- “Same answering model, same questions: against the 14 systems OmniMemEval re-evaluated, all answering with gpt-4.1-mini, Mnemon is first on LoCoMo at 91.7%, with the lowest effective cost index (0.259). https://t.co/X6gXV5rxma” — X
- “Serving latency: Mem0 <0.1 s per query vs ~38 s for long-context on the same history (GPT-4.1-mini, 300 queries). Memory systems win on wall time when they work—but construction and write paths can erase that advantage before the first user asks.” — X
View on OpenRouter.
Model data sourced from OpenRouter.