OpenAI
OpenAI: GPT-5 Mini
openai/gpt-5-mini
GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost.
- Context window: 400,000 tokens
- Input: text, image, file
- Output: text
- Pricing: $0.25/M input tokens, $2/M output tokens
Community sentiment
Negative (-45) — 4 positive, 14 negative, 3 neutral across 21 community samples from X and the web, as of 2026-10-02.
Conversation skews negative: many posts compare GPT-5 Mini unfavorably to cheaper/faster classifiers (noting higher latency, cost, and stability issues). Some users still report transfer/distillation gains and recommend GPT-5 Mini for low-budget or fallback use cases.
- “We want to learn from more of the agent traces we see in production. That means evaluation has to be fast and affordable enough to run at scale. Across 10,000 decisions per judge, Jev was 6.2× cheaper and 34× faster at median latency than GPT-5 Mini. Read the full blog: https://t” — X
- “A lot of useful signal sits in agent traces and can help make agents more reliable. But most of it never gets reviewed because evaluation is expensive. We tested Jev as a cheaper first pass: 6.2× cheaper and about 34× faster than GPT-5 Mini, with more failures caught! https://t.c” — X
- “If your budget/credits are low and you can’t code manually, use this setup: Claude Code: Haiku 4.5 GitHub Copilot: GPT-5 mini Manual work: Focused context + detailed prompts + validation This can still get the job done. As your budget increases, switch to Sonnet + GPT-5.3 Codex” — X
- “Agentick releases a unified benchmark for RL/LLM/VLM agents, 37 tasks, five modalities, live leaderboard. GPT-5 mini leads overall at 0.309 oracle-normalized score; PPO wins planning & multi-agent. Shows harness and data matter more than raw model size.” — X
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Model data sourced from OpenRouter.