gpt-4.1

GPT-4.1 OpenAI का long-context, instruction-following workhorse — official docs में "smartest non-reasoning" GPT-4 class model, 1,047,576-token context window, 32,768 tokens output (platform.openai.com/docs/models/gpt-4.1)। CallMissed customer-facing id `gpt-4.1`, OpenAI API naming match। Existing OpenAI client CallMissed point, `"model": "gpt-4.1"` set।

Headline feature context: entire codebases, contract bundles, research corpora, multi-day agent transcripts single request बिना aggressive chunking। OpenAI real-world software tasks GPT-4o vs coding/tool use gains report; multimodal image input GPT-4.x models जैसा (text + image in, text out)। Knowledge cutoff June 2024 model card per। Fine-tuning GPT-4.1 listed supported नहीं — domain adaptation prompt engineering/retrieval plan।

Pricing $2.00/M input, $8.00/M output, eligible cached input $0.50/M — RAG/agent systems resend large static prefixes attractive। Chain-of-thought reasoning model नहीं — temperature/top_p control retain, GPT-4o migration simplify: model string switch, sampling params keep।

GPT-4.1 use when explicit "thinking" phase के बिना latency-sensitive reasoning enough: repository-wide refactors, hundreds pages policy analysis, log triage, long JSON structured extraction, tools repeatedly call orchestrator agents। "Read everything, then act" workflows best price-performance daily driver; hardest tasks GPT-5 family prefer — competition math absolute frontier GPT-4.1 नहीं।

CallMissed Azure-hosted OpenAI deployment same OpenAI-compatible chat completions schema — streaming, tools, vision। System + user messages, images attach, JSON mode/function definitions OpenAI जैसे। Megabyte-scale prompts token usage linear billing — latency acceptable हो तब भी। Static instructions caching-friendly layouts + retrieval costs predictable।

Limitations: proprietary weights, self-hosting नहीं, multimodal images only model card (native audio/video नहीं)। Very long outputs time — client timeouts set। Reasoning traces/guaranteed internal sampling GPT-5 mini चाहिए reasoning model pick। Voice GPT-4.1 text turns `gpt-4o-mini-tts`/realtime speech pair, audio chat completions expect न करें।

Engineering workflows: "whole-repo" prompts shine — tree summaries, key files, error logs paste, patch plan ask। Two-pass common: structured issue list, per-file diffs output limits within। 32K max output substantial modules one completion, splitting reviewability improve।

Azure Foundry alignment: Microsoft GPT-4.1 family same OpenAI ids (`gpt-4.1`, `gpt-4.1-mini`, `gpt-4.1-nano`) Foundry cards। CallMissed flagship id Azure prefixes बिना portable OpenAI SDK config survive provider changes। Deployment region/quota our infrastructure — API call Azure region select नहीं।

Cost modeling: 500K-token input (large, within context) $2/M ~$1.00/request output before। Cached static prefixes $0.50/M recurring system prompt cost halve। Output $8/M 4K-token reply ~$0.032 add। Ten GPT-4o retrieval chunking compare — engineer time GPT-4.1 often wins token spend higher हो तब भी।

Prompting: long inputs explicit section headers (`## Logs`, `## Contract`) reliable navigation। Line number/clause id citations ask। Long documents JSON extraction system schema + `response_format` JSON where supported।

When not: ultra-low-latency chat widgets huge contexts first token slow; retrieval pre-filter। Pure audio GPT-4.1 force न — speech models। Hard competition math GPT-5 class reasoning despite GPT-4.1 coding gains।

Reliability: client timeouts input size proportional — million-token requests minutes। Idempotent read-only 502/503 retry; partial writes blind retry avoid।