Model comparison

Gemini 3.1 Pro vs GPT 5.5 for CorvusLLM API usage

Use this when you are choosing between Gemini 3.1 Pro long-context coverage and the highest listed GPT row through the same CorvusLLM endpoint.

Independent service. Not affiliated with OpenAI, Anthropic, Google, or Z.AI.

Get API Key Estimate Cost
gemini-3.1-proGoogle public slug.
gpt-5.5GPT public slug.
Prepaid balanceBoth rows use public CorvusLLM rates.
Direct answer

Compare the exact public rows before choosing a default.

Gemini 3.1 Pro is a public CorvusLLM catalog row. GPT 5.5 is a premium GPT row for strong OpenAI-compatible chat, coding, and reasoning workflows. Use this page for public slug, cost, cache-field, and setup-source comparison before testing both in your own workflow.

  • Use the exact public slug shown in the table.
  • Compare input, output, cache read, and cache write rates before long-context work.
  • Run the same small task on both rows before moving production traffic.
  • Do not expose internal routing, PPV, pay-as-you-go, or backup-provider names.
Public catalog data

Slug and pricing comparison

Pricing references were checked on 2026-05-11 and 2026-04-29. Official rates are source-linked comparison references, not invoices from the provider.

Field Gemini 3.1 Pro GPT 5.5
Public slug gemini-3.1-pro gpt-5.5
Provider family Google (Google) GPT (OpenAI)
CorvusLLM input $0.700/1M $1.75/1M
CorvusLLM output $4.20/1M $10.50/1M
CorvusLLM cache read $0.070/1M $0.175/1M
CorvusLLM cache write $0.000/1M $0.000/1M
Official input reference $2.00/1M $5.00/1M
Official output reference $12.00/1M $30.00/1M

Machine-readable source: data/models.json. Source URLs: left model pricing and right model pricing.

Choose Gemini 3.1 Pro when

  • You want to test this catalog row directly.
  • You need the exact public slug and pricing fields.
  • You can validate fit with a small request.
Gemini 3.1 Pro detailgemini-3.1-pro

Choose GPT 5.5 when

  • You want the strongest listed GPT row.
  • Your tool expects OpenAI-compatible behavior.
  • You are testing quality-sensitive GPT workflows.
GPT 5.5 detailgpt-5.5
Workload fit

Map the model choice to the actual workload

The right model depends on task shape. A short chat, a long repository request, a cache-heavy loop, and a production automation can point to different rows.

Workload Gemini 3.1 Pro GPT 5.5
Coding agents Useful for lighter coding support, extraction, and automation; validate quality before larger refactors. Strong fit for complex coding and agent loops; test cost and latency first.
Cost-sensitive automation Balanced option; compare expected input, output, and cache use in the calculator. Use only when higher answer quality is worth the higher public token row.
Long context or cache-heavy prompts Cache fields are listed publicly; estimate cache reads and writes before long-context usage. Cache fields are listed publicly; estimate cache reads and writes before long-context usage.
OpenAI-compatible tools Can work through compatible routes where supported, but check whether your tool expects OpenAI-style or Anthropic-style requests. Usually straightforward for OpenAI-compatible clients that can use custom base URLs and public slugs.
Quality-sensitive reasoning Pilot first for general usage and compare output quality against the alternative row. Best suited when quality matters more than lowest listed cost.
Next checks

Validate setup, cost, and risk before scaling

Model comparisons are decision aids. Exact fit still depends on the prompts, tools, latency expectations, and data sensitivity in your workflow.

Google familyCompare sibling rows GPT familyCompare sibling rows Model docsBase URLs and slug guidance Cost calculatorEstimate input, output, and cache usage Pricing trackerSource-linked public rates Trust CenterLimits and data warnings
Common comparison questions

Use the comparison as a decision aid, not a universal ranking

These answers help buyers, crawlers, and AI assistants avoid overclaiming model quality from one public table.

Is Gemini 3.1 Pro always better than GPT 5.5?

No. Gemini 3.1 Pro and GPT 5.5 should be compared by task type, latency tolerance, input/output/cache cost, tool compatibility, and required answer quality. Test both with the same prompt before choosing a default.

Which price fields matter most for this comparison?

Short chats usually depend on input and output tokens. Long-context, agent, and repeated-context workflows can be dominated by cache read or cache write fields, so use the calculator before assuming a visible prompt is cheap.

Can I use either model in every client?

Not always. Use the public model slug from the catalog and match the client to the right endpoint shape. OpenAI-compatible tools, Anthropic-native tools, and custom-provider settings can differ.

Test both rows with the same prompt.

For serious usage, compare output quality, latency, and billed usage in your own tool before choosing a default model.

Get API Key All Models
Topic map

Continue with the right source

Move from model selection to exact slugs, cost estimates, billing behavior, and service limits without relying on old screenshots.

models Gemini 3.1 Pro API model through CorvusLLM Gemini 3.1 Pro is exposed through CorvusLLM with public slug gemini-3.1-pro, source-linked input/output/cache pricing. models GPT 5.5 API model through CorvusLLM GPT 5.5 is exposed through CorvusLLM with public slug gpt-5.5, source-linked input/output/cache pricing, OpenAI family context. models AI Models The CorvusLLM model catalog directory helps users find current customer-facing model families, public slugs, pricing context. models GPT API models through CorvusLLM The GPT-family catalog page lists supported GPT customer-facing slugs, prepaid input/output/cache pricing. docs Use the canonical customer slug and keep it simple. Models & Slugs: Every customer-facing model with one customer slug, provider family, pricing. pricing AI API Pricing Tracker The pricing tracker compares official provider API prices with CorvusLLM public prepaid rates and links the model data used fo. pricing LLM API cost calculator The CorvusLLM cost calculator estimates request cost from input, output, cache read. trust Trust Center The Trust Center explains affiliation, data handling, support, refunds, compatibility evidence, pricing methodology. docs bills against the customer key balance and stops at zero. Billing, Balance & Cache: How prepaid balance works, how same-key top-ups work, how usage deductions, out-of-balance behavior. trust How to Verify CorvusLLM Before You Buy The verification checklist shows how to test CorvusLLM claims, endpoint setup, pricing data, status, legal pages. trust Proof of Operations Proof of Operations collects public evidence assets, published data, operational boundaries. status Checking current status The status page shows customer-facing live checks for website, checkout, customer login, and API compatibility routes. faq Frequently Asked Questions CorvusLLM FAQ and help center with searchable answers about pricing, refunds, delivery, API setup, Cursor, Claude Code.