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OpenAI: GPT-5.1 (batch)

GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. It uses adaptive reasoning...

Anyone in the Project can @-mention OpenAI: GPT-5.1 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

GPT-5.1 batch is OpenAI's cost-optimized async inference option, delivering the same model quality as standard GPT-5.1 at 50% lower pricing in exchange for 24-hour turnaround. The 400K context window handles book-length documents comfortably, and multimodal support covers text, images, and file uploads. Best for teams running large-scale analysis jobs, content pipelines, or research workflows where overnight processing is acceptable and budget matters more than real-time response.

Best for

Overnight document processing pipelines
Cost-sensitive large-scale analysis
Batch content generation workflows
Research tasks with flexible deadlines
High-volume data transformation jobs

Strengths

The batch API cuts inference costs in half compared to synchronous GPT-5.1 while maintaining identical model performance. The 400K token context window accommodates entire codebases, research papers, or transcripts in a single call. Multimodal input support means you can mix screenshots, PDFs, and text without preprocessing. For teams processing hundreds or thousands of requests daily, the cost savings compound quickly — a 10M token job runs for $6.30 input instead of $12.60.

Trade-offs

The 24-hour processing window makes this unsuitable for any user-facing or real-time application. You lose the ability to stream responses or adjust mid-generation. Debugging is slower since you wait a full day to see output. Teams accustomed to interactive prompt refinement will find the feedback loop frustrating. If your workflow requires sub-minute latency or conversational back-and-forth, standard GPT-5.1 or a faster model is the only option.

Specifications

Provider: openai
Category: llm
Context length: 400,000 tokens
Max output: 128,000 tokens
Modalities: image, text, file
License: proprietary
Released: 2025-11-13

Pricing

Input: $0.63/Mtok
Output: $5.00/Mtok
Model ID: openai/gpt-5.1:batch

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$34.10

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider-level routing data is not available yet for this model.

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Batch Document Summarization

Read the attached documents and produce a 200-word executive summary for each, highlighting key findings, risks, and recommendations. Format each summary with the document title as a heading.

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Code Review at Scale

Review the provided code diffs for security vulnerabilities, logic errors, and style violations. For each issue found, cite the line number and suggest a fix with rationale.

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Research Paper Analysis

Extract the research question, methodology, key findings, limitations, and full citation list from this paper. Structure the output as JSON with those fields.

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Multimodal Data Extraction

Extract all line items, totals, dates, and vendor information from these invoice images. Return a JSON array where each object represents one invoice with normalized field names.

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Content Localization Pipeline

Translate this marketing content into Spanish, French, and German. Adapt idioms and cultural references for each market while preserving brand voice and call-to-action clarity.

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