benchrPricing breakdown
| Tier | Rate / 1M tokens |
|---|---|
| Standard input | $5.00 |
| Standard output | $30.00 |
| Cached input | $0.50 |
| Batch input | $2.50 |
| Batch output | $15.00 |
| Context window | 1,050,000 tokens |
| Max output | 128,000 tokens |
The 400K vs 1M context question
GPT-5 caps at 400,000 tokens — useful for most tasks, but a hard ceiling for large repository analysis, extended agent sessions, or processing full legal or technical documents without truncation. GPT-5.5 extends this to 1,050,000 tokens, matching Anthropic and DeepSeek. If you're regularly approaching 300K tokens per call, the context ceiling is a practical constraint, not a theoretical one.
The practical test: check your 90th-percentile context length in production. If it's under 300K, GPT-5's limit isn't your problem and the upgrade doesn't help you.
Computer use and agentic reasoning
GPT-5.5 is positioned for agent and computer-use tasks. Its published ARC-AGI-2 figure can be one screening signal, but no public benchmark establishes how many retries it will save in a particular agent loop. Measure completion rate, retry count, latency, and review cost on representative tasks before changing routing.
The caching economics
Cached input costs $0.50/1M — 90% off the standard rate. For agents with large, repeated system prompts, a hypothetical 90% cache-hit rate drops GPT-5.5's effective input cost from $5.00 to approximately $0.95 per million. Output at $30/1M does not receive that cached-input rate, so include output and the observed cache-hit rate in the total.
For the narrative version of this rate card — including how the long-context reprice hits whole sessions — read the GPT-5.5 pricing explainer.
Batch API: the 50% route
GPT-5.5 via the OpenAI Batch API: $2.50/1M input, $15/1M output. Batch jobs run asynchronously with up to 24-hour turnaround. For evals, document classification, synthetic data generation, or any non-real-time processing pipeline, the batch rate makes GPT-5.5 cost-competitive with real-time GPT-5. This is the clearest case for GPT-5.5 in cost-sensitive environments.
Cost scenarios
At 10M input + 3M output per month, standard pricing is $50 + $90 = $140/month. GPT-5 at the same recorded token rates totals $42.50. GPT-5.5 batch pricing totals $70/month; a hypothetical 90% cache-hit rate on standard input gives about $99.50/month. These are rate-card scenarios, not evidence of a quality or completion-rate difference.
Use-case fit
Shortlist for testing: Tasks that exceed GPT-5's listed 400K context limit, computer-use pipelines, and batch processing that can use the listed discount.
Compare before switching: If requests fit within 400K tokens, run both models on the same sample and include completion, retry, latency, and review costs. The published price difference does not establish which route is economically better.
Decision checklist
Before routing to GPT-5.5, measure your actual 90th-percentile context length in production. Then run an A/B test on a representative sample of your hardest tasks; the provider page does not supply a comparable coding score that can replace that test.
Consider the batch API first: if your workload tolerates 24-hour turnaround, the batch pricing brings GPT-5.5 to $2.50/$15 — a different cost conversation than standard pricing.