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OpenAI: GPT-5.5 (batch)

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...

Anyone in the Project can @-mention OpenAI: GPT-5.5 (batch) with the team's shared context - pooled credits, one chat, one memory.

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Verdict

GPT-5.5 Batch delivers frontier reasoning at 50% off standard pricing, trading real-time response for dramatic cost savings. The 1M+ token context window handles book-length documents and complex codebases without chunking. Best for teams running overnight analysis jobs, bulk content processing, or research workflows where 12-24 hour turnaround is acceptable. If you need instant responses, use the standard API; if you can wait, this is the most cost-effective way to access GPT-5.5's capabilities.

Best for

Overnight document analysis and summarization
Bulk content generation with budget constraints
Research synthesis across long papers
Large codebase refactoring and review
Cost-sensitive multi-turn reasoning tasks

Strengths

The batch pricing model cuts input costs to $2.50/Mtok — half the standard rate — making this the cheapest access point to GPT-5.5's reasoning capabilities. The 1M+ token context window eliminates preprocessing for most documents, letting you drop entire codebases or research papers into a single prompt. Batch jobs queue efficiently for workloads that don't need sub-second latency, and you get identical model quality to the real-time API.

Trade-offs

Batch jobs complete within 24 hours but typically take 12+ hours, making this unusable for interactive applications or time-sensitive workflows. You lose streaming responses and can't adjust mid-generation. The async architecture adds complexity: you submit jobs via API, poll for completion, then retrieve results. Teams accustomed to synchronous LLM calls will need to rework their pipelines. For latency-critical work — chatbots, live coding assistants, real-time analysis — the standard GPT-5.5 API is the only option despite costing twice as much.

Specifications

Provider: openai
Category: llm
Context length: 1,050,000 tokens
Max output: 128,000 tokens
Modalities: file, image, text
License: proprietary
Released: 2026-04-24

Pricing

Input: $2.50/Mtok
Output: $15.00/Mtok
Model ID: openai/gpt-5.5: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

$110.00

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

Codebase Architecture Review

Review this codebase for architectural issues, code duplication, and opportunities to improve modularity. Provide a prioritized list of refactoring recommendations with specific file references and estimated effort.

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Multi-Paper Literature Synthesis

Compare the methodologies and findings across these research papers. Identify consensus views, contradictions, and gaps in the literature. Produce a structured synthesis with citations to specific papers.

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Bulk Product Description Generation

Generate unique, SEO-optimized product descriptions for each item in this catalog. Maintain consistent brand voice while highlighting distinctive features for each product. Output as JSON with product_id and description fields.

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Contract Analysis and Risk Flagging

Analyze this contract for unfavorable terms, ambiguous language, and potential legal risks. Flag specific clauses with severity ratings and suggest alternative language where appropriate.

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Historical Data Pattern Analysis

Analyze this time-series dataset for patterns, anomalies, and correlations. Provide statistical summaries, visualize key trends in text format, and flag data points that warrant further investigation.

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