Neither wins outright: Google: Gemini 4 Argon is the pick when cost matters at scale, OpenAI: GPT-5.5 Pro when you generate long outputs in one go.
| Spec | Google: Gemini 4 Argon | OpenAI: GPT-5.5 Pro |
|---|---|---|
| Provider | OpenAI | |
| Input price (per 1M tokens) | $2.00 | $30.00 |
| Output price (per 1M tokens) | $10.00 | $180 |
| Blended price (3:1 input/output) | $4.00 | $67.50 |
| Cheaper than … of all models we track | 15% | 0% |
| Context window | 1.0M tokens (~1,573 pages) | 1.1M tokens (~1,575 pages) |
| Larger context than … of all models | 81% | 92% |
| Max output per response | 66K tokens (~49,200 words) | 128K tokens (~96,000 words) |
| Input types | audio, file, image, text, video | file, image, text |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2026-10-13 | 2026-04-24 |
Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
| Workload (per month) | Google: Gemini 4 Argon | OpenAI: GPT-5.5 Pro | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $350 | $5,850 | Google: Gemini 4 Argon saves $5,500 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $340 | $5,400 | Google: Gemini 4 Argon saves $5,060 |
| Coding / tool-using agent 2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens) | $360 | $5,760 | Google: Gemini 4 Argon saves $5,400 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $220 | $3,480 | Google: Gemini 4 Argon saves $3,260 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $110 | $1,680 | Google: Gemini 4 Argon saves $1,570 |
| Capability | Google: Gemini 4 Argon | OpenAI: GPT-5.5 Pro | What it lets you do |
|---|---|---|---|
| Tool / function calling | ✓ Yes | ✓ Yes | call your APIs and run agent loops |
| Structured output (JSON schema) | ✓ Yes | ✓ Yes | return JSON that validates against your schema |
| Reasoning / thinking mode | ✓ Yes | ✓ Yes | spend extra tokens thinking before answering hard problems |
| Image input | ✓ Yes | ✓ Yes | read screenshots, charts and scanned pages |
| Audio input | ✓ Yes | — | take speech or audio directly |
| File / PDF input | ✓ Yes | ✓ Yes | accept documents without your own parsing step |
| Image output | — | — | generate images, not just text |
| Built-in web search | ✓ Yes | ✓ Yes | answer from live web results |
| Seed (reproducible sampling) | ✓ Yes | ✓ Yes | repeat a generation for tests and evals |
| Log-probabilities | — | — | read token confidence for classification and scoring |
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Neither wins outright: Google: Gemini 4 Argon is the pick when cost matters at scale, OpenAI: GPT-5.5 Pro when you generate long outputs in one go. In short: Google: Gemini 4 Argon when cost matters at scale, you need audio input, you want the newer model; OpenAI: GPT-5.5 Pro when you generate long outputs in one go.
Google: Gemini 4 Argon is cheaper: $4.00 vs $67.50 per 1M tokens at a typical 3:1 input/output mix, 94% less. For 50,000 support conversations a month that is $350 against $5,850.
Google: Gemini 4 Argon costs $2.00 per 1M input tokens and $10.00 per 1M output tokens; OpenAI: GPT-5.5 Pro costs $30.00 input and $180 output. Output tokens are billed separately, so long answers shift the gap.
OpenAI: GPT-5.5 Pro: 1.1M tokens, about 1,575 pages, against 1.0M (about 1,573 pages) for Google: Gemini 4 Argon.
Tool calling — Google: Gemini 4 Argon: yes, OpenAI: GPT-5.5 Pro: yes. JSON-schema structured output — Google: Gemini 4 Argon: yes, OpenAI: GPT-5.5 Pro: yes.
Yes, both accept image input.
No free variant is listed for either; both are pay-per-token.
Google: Gemini 4 Argon can write up to 66K tokens (about 49,200 words) per response; OpenAI: GPT-5.5 Pro up to 128K (about 96,000 words).
Google: Gemini 4 Argon was listed on 2026-10-13; OpenAI: GPT-5.5 Pro on 2026-04-24.
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...
Full Google: Gemini 4 Argon specs →GPT-5.5 Pro is OpenAI’s high-capability model optimized for deep reasoning and accuracy on complex, high-stakes workloads. It features a 1M+ token context window (922K input, 128K output) with support for...
Full OpenAI: GPT-5.5 Pro specs →