gpt-4.1

GPT-4.1 is OpenAI's long-context, instruction-following workhorse — positioned in official docs as the "smartest non-reasoning" GPT-4 class model with a 1,047,576-token context window and up to 32,768 tokens of output (platform.openai.com/docs/models/gpt-4.1). On CallMissed the customer-facing id is simply `gpt-4.1`, matching OpenAI's API naming. Point your existing OpenAI client at CallMissed and set `"model": "gpt-4.1"`.

The headline feature is context: you can feed entire codebases, contract bundles, research corpora, or multi-day agent transcripts in a single request without aggressive chunking. OpenAI reports strong gains in coding and tool use versus GPT-4o for real-world software tasks, and the model supports the same multimodal image input path as other GPT-4.x models (text + image in, text out). Knowledge cutoff is June 2024 per the model card. Fine-tuning is not listed as supported on GPT-4.1 — plan on prompt engineering and retrieval for domain adaptation.

Pricing is $2.00 per million input tokens, $8.00 per million output tokens, with cached input at $0.50 per million when eligible — attractive for RAG and agent systems that resend large static prefixes. Because GPT-4.1 is not a chain-of-thought reasoning model, you retain control of temperature and top_p, which simplifies migration from GPT-4o: many teams switch the model string only and keep sampling parameters.

Use GPT-4.1 when latency-sensitive reasoning without an explicit "thinking" phase is enough: repository-wide refactors, policy analysis over hundreds of pages, log triage, structured extraction from long JSON, and orchestrator agents that call tools repeatedly. It is often the best price-performance point for "read everything, then act" workflows. OpenAI's own docs note that for the hardest tasks you may still prefer GPT-5 family models — treat GPT-4.1 as the daily driver for large-context engineering rather than the absolute frontier on competition math.

On CallMissed, GPT-4.1 routes through our Azure-hosted OpenAI deployment with the same OpenAI-compatible chat completions schema — streaming, tools, and vision included. Send system + user messages, attach images when needed, and use JSON mode or function definitions as with OpenAI. Watch token usage on megabyte-scale prompts: billing scales linearly with context length even if latency is acceptable. For repeated static instructions, combine caching-friendly prompt layouts with retrieval to keep costs predictable.

Limitations: proprietary weights, no self-hosting, and multimodal input limited to images on the model card (no native audio/video). Very long outputs may take time — set client timeouts accordingly. If you require guaranteed reasoning traces or fixed internal sampling like GPT-5 mini, pick a reasoning model instead. For voice, pair GPT-4.1 text turns with `gpt-4o-mini-tts` or a realtime speech model rather than expecting audio in chat completions.

Engineering workflows: GPT-4.1 shines in "whole-repo" prompts — paste tree summaries, key files, and error logs together, then ask for a patch plan. Many teams run a two-pass pattern: first pass extracts a structured issue list, second pass generates diffs per file to stay within output limits. With 32K max output tokens, you can emit substantial modules in one completion, but splitting still improves reviewability.

Azure Foundry alignment: Microsoft lists GPT-4.1 family models with the same OpenAI ids (`gpt-4.1`, `gpt-4.1-mini`, `gpt-4.1-nano`) on Foundry model cards. CallMissed exposes the flagship id without Azure prefixes so your portable OpenAI SDK configuration survives provider changes. Deployment region and quota are managed on our infrastructure — you do not select an Azure region in the API call.

Cost modeling example: a 500K-token input prompt (large but within context) at $2/M input costs about $1.00 per request before output. Cached static prefixes at $0.50/M can halve recurring system prompt cost. Output at $8/M means a 4K-token reply adds ~$0.032. Compare to chunking into ten GPT-4o calls with retrieval overhead — often GPT-4.1 wins on engineer time even when token spend is higher.

Prompting tips: use explicit section headers in long inputs (`## Logs`, `## Contract`) so the model navigates context reliably. Ask for citations by line number or clause id. For JSON extraction over long documents, provide a schema in the system message and set `response_format` to JSON where supported.

When not to use: ultra-low-latency chat widgets may feel slow on first token with huge contexts; pre-filter with retrieval. Pure audio pipelines should not force GPT-4.1 — use speech models. Hard competition math may still favor GPT-5 class reasoning models despite GPT-4.1's coding gains.

Reliability: implement client-side timeouts proportional to input size — million-token requests can take minutes. Retry idempotent read-only calls on 502/503; avoid blind retries on partial writes.