{"id":444,"date":"2026-08-25T06:21:53","date_gmt":"2026-08-25T06:21:53","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=444"},"modified":"2026-08-25T06:31:57","modified_gmt":"2026-08-25T06:31:57","slug":"gpt-5-5-vs-gemini-3-1-pro","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/gpt-5-5-vs-gemini-3-1-pro","title":{"rendered":"GPT-5.5 vs Gemini 3.1 Pro: Benchmarks, Price, Side-by-Side (2026)"},"content":{"rendered":"\n
\n\n

Comparison \u00b7 August 2026<\/p>\n\n

\nGPT-5.5 and Gemini 3.1 Pro are two frontier Pro-class models teams actually route in 2026. This guide is a decision piece, not a leaderboard dump: live pricing caveats, three workload cost scenarios, public signal notes, charts, and our own side-by-side runs on writing, coding, false premises, and product routing. Multi-model AI means you can keep both. Start in a\nmulti-model AI workspace<\/a>\nor on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick GPT-5.5 if:<\/strong> you want a frontier default for professional writing and coding\/tooling loops, and you can absorb higher API rates for that quality bar.<\/p>\n

Pick Gemini 3.1 Pro if:<\/strong> you need native audio\/video input, the cheaper Pro-class bill, or long-document and multimodal pipelines at volume.<\/p>\n

Best default for many SaaS teams:<\/strong> route by task. Keep both. Do not crown a permanent overall winner.<\/p>\n

Data checked: 2026-08-21 via live side-by-side API tests. Prices and benches change. Verify live.<\/em><\/p>\n<\/div>\n\n

\n\n\n\n\n\n\n

1.05M<\/strong><\/p><\/td>\n

GPT-5.5 context (API)<\/p><\/td>\n<\/tr>\n

1.05M<\/strong><\/p><\/td>\n

Gemini 3.1 Pro Preview context (API)<\/p><\/td>\n<\/tr>\n

$5 \/ $30<\/strong><\/p><\/td>\n

GPT-5.5 input\/output per 1M tokens (API pricing, 2026-08-21)<\/p><\/td>\n<\/tr>\n

$2 \/ $12<\/strong><\/p><\/td>\n

Gemini 3.1 Pro Preview input\/output per 1M tokens (API pricing, 2026-08-21)<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n

\n\"Bar\n
Figure 1.<\/strong> Where each model wins on relative axes (professional coding\/writing signal, multimodal breadth, API cost efficiency, long-doc fit). Higher is stronger for that axis. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n\n

Persona picker<\/h2>\n\n\n\n\n\n\n\n\n

You are\u2026<\/p><\/th>\n

Start with<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Writer \/ CS \/ marketer<\/p><\/td>\n

GPT-5.5 (often)<\/p><\/td>\n

In our rewrite test, GPT stayed tighter to subject-style facts; Gemini added warmer filler.<\/p><\/td>\n<\/tr>\n

Developer \/ agent builder<\/p><\/td>\n

GPT-5.5 default; Gemini for huge multimodal packs<\/p><\/td>\n

Many 2026 writeups still put GPT-class ahead on coding\/tooling reputation vs Gemini Pro; our micro bug fix was a tie.<\/p><\/td>\n<\/tr>\n

Researcher \/ analyst<\/p><\/td>\n

Gemini 3.1 Pro<\/p><\/td>\n

Similar ~1M context, but audio\/video modalities and much lower API cost for long packs.<\/p><\/td>\n<\/tr>\n

Budget \/ high volume API<\/p><\/td>\n

Gemini 3.1 Pro<\/p><\/td>\n

Roughly 2.5\u00d7 cheaper across our chat, repo, and agent workload estimates at current API list rates.<\/p><\/td>\n<\/tr>\n

Vision \/ screenshots \/ video<\/p><\/td>\n

Gemini 3.1 Pro<\/p><\/td>\n

Native audio\/video on the API model card; broader multimodal foundation for mixed media.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

What we are comparing (exact versions)<\/h2>\n

Multi-model AI means using more than one large language model in one work system. This page compares two specific API models, not vague \u201cChatGPT vs Gemini\u201d brand names and not multimodal-as-in-one-model marketing language.<\/p>\n\n\n\n\n\n\n\n\n

Field<\/p><\/th>\n

GPT-5.5<\/p><\/th>\n

Gemini 3.1 Pro<\/p><\/th>\n<\/tr>\n

Provider<\/p><\/td>\n

OpenAI<\/p><\/td>\n

Google<\/p><\/td>\n<\/tr>\n

API model<\/p><\/td>\n

GPT-5.5<\/code><\/p><\/td>\n

Gemini 3.1 Pro Preview<\/code><\/p><\/td>\n<\/tr>\n

Listed API name<\/p><\/td>\n

OpenAI: GPT-5.5<\/p><\/td>\n

Google: Gemini 3.1 Pro Preview<\/p><\/td>\n<\/tr>\n

Release (approx.)<\/p><\/td>\n

2026 OpenAI frontier line (verify live card)<\/p><\/td>\n

19 Feb 2026 (preview \/ later GA coverage)<\/p><\/td>\n<\/tr>\n

App vs API note<\/p><\/td>\n

Also in ChatGPT \/ OpenAI products; this article uses the API ID above<\/p><\/td>\n

Also in Gemini app \/ Google AI Pro; this article uses the API preview ID above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

If you still see posts comparing GPT-5 or Gemini 3 Pro without the 5.5 \/ 3.1 labels, treat them as older. Sibling routing pieces:\nGrok 4.6 vs Gemini 3.1 Pro<\/a>,\nClaude Opus 5 vs GPT-5.5<\/a>.\nFor routing across many models, see\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API pricing, 2026-08-21)<\/h2>\n\n\n\n\n\n\n\n\n\n\n

Spec<\/p><\/th>\n

GPT-5.5<\/p><\/th>\n

Gemini 3.1 Pro Preview<\/p><\/th>\n<\/tr>\n

Context window<\/p><\/td>\n

1,050,000 tokens<\/p><\/td>\n

1,048,576 tokens<\/p><\/td>\n<\/tr>\n

Input modalities (card)<\/p><\/td>\n

text, image, file<\/p><\/td>\n

text, image, file, audio, video<\/p><\/td>\n<\/tr>\n

Output<\/p><\/td>\n

text<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Reasoning controls<\/p><\/td>\n

reasoning \/ reasoning_effort supported (verify live)<\/p><\/td>\n

reasoning \/ reasoning_effort supported<\/p><\/td>\n<\/tr>\n

Tools<\/p><\/td>\n

tools \/ tool_choice<\/p><\/td>\n

tools \/ tool_choice<\/p><\/td>\n<\/tr>\n

Open weights<\/p><\/td>\n

No<\/p><\/td>\n

No<\/p><\/td>\n<\/tr>\n

Vendor positioning (short)<\/p><\/td>\n

Frontier professional workloads; strong coding and knowledge-work reputation in 2026 writeups<\/p><\/td>\n

Frontier reasoning; software engineering; agentic reliability; multimodal foundation<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Context is effectively a wash (~1.05M both). The structural splits are price and modality breadth: Gemini lists audio and video; GPT-5.5 lists text, image, and file.<\/p>\n\n

\n\n

Pricing and real workload cost<\/h2>\n

List prices are easy to misread. Workload cost is what you feel. Rates below are from published API pricing on 2026-08-21<\/strong>.<\/p>\n\n\n\n\n\n\n

Price<\/p><\/th>\n

GPT-5.5<\/p><\/th>\n

Gemini 3.1 Pro Preview<\/p><\/th>\n<\/tr>\n

Input \/ 1M tokens<\/p><\/td>\n

$5.00<\/p><\/td>\n

$2.00<\/p><\/td>\n<\/tr>\n

Output \/ 1M tokens<\/p><\/td>\n

$30.00<\/p><\/td>\n

$12.00<\/p><\/td>\n<\/tr>\n

Cache read \/ 1M<\/p><\/td>\n

$0.50<\/p><\/td>\n

$0.20<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Gemini is cheaper on every sticker line here: input, output, and cache reads. GPT-5.5 prices like a premium frontier call.<\/p>\n\n\n\n\n\n\n

Scenario<\/p><\/th>\n

Assumed tokens<\/p><\/th>\n

Est. GPT-5.5<\/p><\/th>\n

Est. Gemini 3.1 Pro<\/p><\/th>\n<\/tr>\n

Chat turn<\/p><\/td>\n

1k in + 0.5k out<\/p><\/td>\n

$0.020<\/p><\/td>\n

$0.008<\/p><\/td>\n<\/tr>\n

Repo \/ doc review<\/p><\/td>\n

80k in + 4k out<\/p><\/td>\n

$0.520<\/p><\/td>\n

$0.208<\/p><\/td>\n<\/tr>\n

Agent loop<\/p><\/td>\n

200k in (50% cached) + 20k out<\/p><\/td>\n

$1.150<\/p><\/td>\n

$0.460<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using Published API list rates (2026-08-21). Chart: i10X.<\/figcaption>\n<\/figure>\n

Across these three stylized runs, Gemini lands at roughly 40%<\/strong> of GPT-5.5\u2019s bill. That compounds fast for agent loops. For subscription stacks (Plus \/ Pro style plans), see\nAI subscription stack cost<\/a>. Always verify live vendor pages before budgeting.<\/p>\n\n

\n\n

Performance by job (public signals)<\/h2>\n

Public benches disagree by harness, effort mode, and date. Treat them as signals. Confirm with your prompts.<\/p>\n\n

\nHow to read this<\/strong>\n

Indexes mix coding, science, and agentic tasks. When third-party pages disagree on SWE-bench style coding numbers, do not force a fake permanent coding champion. Run your repo. Method:\nside-by-side AI comparison<\/a>.<\/p>\n<\/div>\n\n

Coding and agents<\/h3>\n

Across many 2026 comparison writeups, GPT-5.5 (and the broader GPT-5.x line) still carries a stronger coding and tooling reputation than Gemini Pro-class models for interactive engineering and agent scaffolds. That is a reputation signal, not a permanent law. Our empty-list micro-test was a clean tie. For multi-file refactors tied to video, screenshots, or mixed media packs, Gemini\u2019s modality list is the practical edge.<\/p>\n\n

Writing and tone<\/h3>\n

Benchmarks barely measure voice. That is why we ran the email rewrite below. Expect GPT-5.5 to sound tighter and more subject-style. Expect Gemini to sound warmer and sometimes to add soft filler (\u201cgreat week\u201d energy) that was not in the source.<\/p>\n\n

Multimodal and long context<\/h3>\n

Context windows are nearly identical at current API list rates (~1.05M). Gemini\u2019s clearer structural win is modality breadth: text, image, file, audio, and video on the card, plus a much lower token bill for long-document work. If your day is PDFs, screenshots, meeting audio, and video clips, Gemini is the safer volume default.<\/p>\n\n\n\n\n\n\n\n\n\n

Job<\/p><\/th>\n

Edge<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Hard coding \/ tooling loops<\/p><\/td>\n

GPT-5.5<\/p><\/td>\n

Stronger 2026 professional coding\/tooling reputation vs Gemini Pro class<\/p><\/td>\n<\/tr>\n

Everyday writing<\/p><\/td>\n

Split (test tone)<\/p><\/td>\n

GPT tighter subject-style in our rewrite; Gemini warmer<\/p><\/td>\n<\/tr>\n

Long docs \/ multimodal<\/p><\/td>\n

Gemini 3.1 Pro<\/p><\/td>\n

Audio\/video on card + far lower cost at matched context scale<\/p><\/td>\n<\/tr>\n

ChatGPT \/ OpenAI product stack<\/p><\/td>\n

GPT-5.5 (product)<\/p><\/td>\n

Native ChatGPT \/ OpenAI tooling adjacency; confirm tools in your app<\/p><\/td>\n<\/tr>\n

Cost at API volume<\/p><\/td>\n

Gemini 3.1 Pro<\/p><\/td>\n

~$0.008 vs $0.020 chat; ~$0.46 vs $1.15 agent loop in our estimates<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

Side-by-side test (live API test, 2026-08-21)<\/h2>\n

We ran the same prompts on GPT-5.5<\/code> and Gemini 3.1 Pro Preview<\/code> side by side in a multi-model workspace (temperature 0.2-0.3). Scores are editorial 1-5 across instruction following, depth, factual caution, style, and usefulness (max 25 per prompt).<\/p>\n\n

Test 1: Client email rewrite<\/h3>\n

Task:<\/strong> Keep every fact. Warmer. Under 120 words.<\/p>\n

GPT-5.5 (excerpt):<\/strong> Follow-up on the Q3 deck from last Tuesday; finance numbers still missing after the Friday promise; ask to move stakeholders to next week (Wednesday); update competitive slide with Acme pricing. Polished, subject-style, no invented cheer.<\/p>\n

Gemini 3.1 Pro (excerpt):<\/strong> Same facts, plus greeting energy (\u201chope you\u2019re having a great week\u201d), softer scaffolding, and a warmer close. Polished corporate template with filler the source never stated.<\/p>\n

Edge:<\/strong> GPT for fidelity and subject-style brevity (~23 vs ~21). Gemini for polished warmth. If your brand voice hates filler, prefer GPT-5.5.<\/p>\n\n

Test 2: Empty-list average bug<\/h3>\n

Both models correctly named ZeroDivisionError<\/code> on empty input and proposed the same minimal guard (if not nums: return 0<\/code>). Tie<\/strong> on this micro-task.<\/p>\n\n

Test 3: False premise (Moon cheese)<\/h3>\n

Both refused the premise first. Neither invented lunar dairy facts. Both pass<\/strong> on factual caution.<\/p>\n\n

Test 4: Invented geography inflation (Atlantis)<\/h3>\n

Both refused Atlantis inflation rates and asked for a real statistical office or country. Both pass<\/strong> the \u201cdo not invent numbers\u201d bar. For trust workflows, still add a second-model check:\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Test 5: Strong agreement on the SaaS matrix<\/h3>\n

Asked which model should be the SaaS team default for emails, long PDFs, Python, and screenshots:<\/p>\n