GPT-5.5 vs Gemini 3.1 Pro: Benchmarks, Price, Side-by-Side (2026)

Comparison · August 2026

GPT-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 multi-model AI workspace or on i10X.

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Persona picker

You are…	Start with	Why
Writer / CS / marketer	GPT-5.5 (often)	In our rewrite test, GPT stayed tighter to subject-style facts; Gemini added warmer filler.
Developer / agent builder	GPT-5.5 default; Gemini for huge multimodal packs	Many 2026 writeups still put GPT-class ahead on coding/tooling reputation vs Gemini Pro; our micro bug fix was a tie.
Researcher / analyst	Gemini 3.1 Pro	Similar ~1M context, but audio/video modalities and much lower API cost for long packs.
Budget / high volume API	Gemini 3.1 Pro	Roughly 2.5× cheaper across our chat, repo, and agent workload estimates at current API list rates.
Vision / screenshots / video	Gemini 3.1 Pro	Native audio/video on the API model card; broader multimodal foundation for mixed media.

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What we are comparing (exact versions)

Multi-model AI means using more than one large language model in one work system. This page compares two specific API models, not vague “ChatGPT vs Gemini” brand names and not multimodal-as-in-one-model marketing language.

Field	GPT-5.5	Gemini 3.1 Pro
Provider	OpenAI	Google
API model	GPT-5.5	Gemini 3.1 Pro Preview
Listed API name	OpenAI: GPT-5.5	Google: Gemini 3.1 Pro Preview
Release (approx.)	2026 OpenAI frontier line (verify live card)	19 Feb 2026 (preview / later GA coverage)
App vs API note	Also in ChatGPT / OpenAI products; this article uses the API ID above	Also in Gemini app / Google AI Pro; this article uses the API preview ID above

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: Grok 4.6 vs Gemini 3.1 Pro, Claude Opus 5 vs GPT-5.5. For routing across many models, see AI model routing.

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Spec sheet (API pricing, 2026-08-21)

Spec	GPT-5.5	Gemini 3.1 Pro Preview
Context window	1,050,000 tokens	1,048,576 tokens
Input modalities (card)	text, image, file	text, image, file, audio, video
Output	text	text
Reasoning controls	reasoning / reasoning_effort supported (verify live)	reasoning / reasoning_effort supported
Tools	tools / tool_choice	tools / tool_choice
Open weights	No	No
Vendor positioning (short)	Frontier professional workloads; strong coding and knowledge-work reputation in 2026 writeups	Frontier reasoning; software engineering; agentic reliability; multimodal foundation

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.

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Pricing and real workload cost

List prices are easy to misread. Workload cost is what you feel. Rates below are from published API pricing on 2026-08-21.

Price	GPT-5.5	Gemini 3.1 Pro Preview
Input / 1M tokens	$5.00	$2.00
Output / 1M tokens	$30.00	$12.00
Cache read / 1M	$0.50	$0.20

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

Scenario	Assumed tokens	Est. GPT-5.5	Est. Gemini 3.1 Pro
Chat turn	1k in + 0.5k out	$0.020	$0.008
Repo / doc review	80k in + 4k out	$0.520	$0.208
Agent loop	200k in (50% cached) + 20k out	$1.150	$0.460

Across these three stylized runs, Gemini lands at roughly 40% of GPT-5.5’s bill. That compounds fast for agent loops. For subscription stacks (Plus / Pro style plans), see AI subscription stack cost. Always verify live vendor pages before budgeting.

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Performance by job (public signals)

Public benches disagree by harness, effort mode, and date. Treat them as signals. Confirm with your prompts.

Coding and agents

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’s modality list is the practical edge.

Writing and tone

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 (“great week” energy) that was not in the source.

Multimodal and long context

Context windows are nearly identical at current API list rates (~1.05M). Gemini’s 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.

Job	Edge	Why
Hard coding / tooling loops	GPT-5.5	Stronger 2026 professional coding/tooling reputation vs Gemini Pro class
Everyday writing	Split (test tone)	GPT tighter subject-style in our rewrite; Gemini warmer
Long docs / multimodal	Gemini 3.1 Pro	Audio/video on card + far lower cost at matched context scale
ChatGPT / OpenAI product stack	GPT-5.5 (product)	Native ChatGPT / OpenAI tooling adjacency; confirm tools in your app
Cost at API volume	Gemini 3.1 Pro	~$0.008 vs $0.020 chat; ~$0.46 vs $1.15 agent loop in our estimates

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Side-by-side test (live API test, 2026-08-21)

We ran the same prompts on GPT-5.5 and Gemini 3.1 Pro Preview 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).

Test 1: Client email rewrite

Task: Keep every fact. Warmer. Under 120 words.

GPT-5.5 (excerpt): 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.

Gemini 3.1 Pro (excerpt): Same facts, plus greeting energy (“hope you’re having a great week”), softer scaffolding, and a warmer close. Polished corporate template with filler the source never stated.

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

Test 2: Empty-list average bug

Both models correctly named ZeroDivisionError on empty input and proposed the same minimal guard (if not nums: return 0). Tie on this micro-task.

Test 3: False premise (Moon cheese)

Both refused the premise first. Neither invented lunar dairy facts. Both pass on factual caution.

Test 4: Invented geography inflation (Atlantis)

Both refused Atlantis inflation rates and asked for a real statistical office or country. Both pass the “do not invent numbers” bar. For trust workflows, still add a second-model check: multi-model hallucination checks.

Test 5: Strong agreement on the SaaS matrix

Asked which model should be the SaaS team default for emails, long PDFs, Python, and screenshots:

• GPT’s matrix: Default GPT-5.5 for email + Python; switch to Gemini for long PDFs.
• Gemini’s matrix: Default GPT-5.5 for email + Python; switch to Gemini for long PDFs.

Unlike our Grok 4.6 vs Gemini run (where the models disagreed on the email default), here both models agreed on the overlapping truth: long PDFs → Gemini; Python → GPT-5.5. Email also leaned GPT in both matrices. That consensus is useful routing glue for a multi-model stack.

Prompt	GPT-5.5	Gemini 3.1 Pro	Note
Email rewrite	23/25	21/25	GPT tighter subject-style
Bug fix	24/25	24/25	Tie
False premise	24/25	24/25	Both refuse correctly
Refuse invented stat	24/25	24/25	Both refuse Atlantis rate
Routing matrix	24/25	24/25	Strong agreement: PDF → Gemini; Python → GPT
Total	119/125	117/125	Close; cost and modality still split the stack

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Ecosystem and where you run them

• GPT-5.5: OpenAI API; consumer ChatGPT experiences. Strength: mature tooling ecosystem, IDE and agent integrations, enterprise OpenAI adjacency.
• Gemini 3.1 Pro: Google AI / Gemini app / Workspace adjacency; API access. Strength: Docs/Drive/Search world and multimodal inputs including audio/video.
• Both in one place: Multi-model workspaces like i10X let you compare the same prompt without two browser profiles.

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Pros, cons, and failure modes

GPT-5.5

• Pros: Frontier professional workload reputation; stronger coding/tooling signal in many 2026 writeups vs Gemini Pro class; tighter subject-style writing in our rewrite; ~1.05M context.
• Cons: Much higher API price ($5/$30 vs $2/$12); narrower modality set on the card (no audio/video listed); easy to overspend on agent loops.
• Fails when: you optimize purely for token burn at scale, or you need native audio/video understanding as the only model.

Gemini 3.1 Pro

• Pros: ~1M context at a fraction of GPT-5.5’s price; text+image+file+audio+video; strong long-doc economics; Google ecosystem fit.
• Cons: Can over-polish writing with filler; coding/tooling reputation often trails GPT-class in 2026 Pro comparisons; preview ID may shift before GA naming settles.
• Fails when: you need the absolute top professional coding default and ignore Gemini’s cost and multimodal advantages, or you assume app quality equals this API ID.

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Decision guide: pick one or route both

If you need…	Choose
Premium coding / professional writing default	GPT-5.5
Long PDF / audio / video pipelines at volume	Gemini 3.1 Pro
Cheapest Pro-class API bill	Gemini 3.1 Pro
Brand-safe warm customer email	A/B once; many teams will prefer GPT subject-style or Gemini polish
Mixed SaaS week	Both: route PDF/multimodal/volume → Gemini; code/email craft → GPT-5.5

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Application walkthroughs: where each model is better

1) Customer support email

Better often: GPT-5.5 when you want a faithful, subject-style rewrite without invented niceties. In our live test, GPT preserved every operational fact and avoided “great week” filler. Gemini produced a warmer letter but added greeting energy that was not in the source.

Switch to Gemini if your brand voice is deliberately polished and managers prefer soft corporate scaffolding.

2) Long PDF / research pack

Better: Gemini 3.1 Pro. Context is a wash (~1.05M both), but Gemini is far cheaper per token and lists audio/video for mixed media packs. Both models’ own routing matrices agreed: long PDFs → Gemini. If analysts paste 200-page decks or diligence PDFs at volume, Gemini is the primary.

Use GPT-5.5 for short/medium briefs that need premium prose, or as a second-pass critic after Gemini summarizes.

3) Everyday Python scripting

Often GPT-5.5 as the interactive coding partner, matching both models’ routing advice in our live matrix. Our micro bug-fix was a tie, so do not overclaim from one snippet. For multi-file refactors tied to huge docs, UI screenshots, or meeting video, bring Gemini in.

4) Screenshot, audio, and video QA

Better: Gemini 3.1 Pro. The API modality list is the structural tell: audio and video sit beside text, image, and file. If your loop is “recording → find the bug → draft a ticket,” or “screenshot → UI regression note,” default Gemini unless you have a GPT vision workflow you already trust.

5) Output-heavy generation at API scale

Better on cost: Gemini 3.1 Pro. On our agent-loop estimate, Gemini landed ~$0.46 vs GPT-5.5 ~$1.15 per stylized run. That is not a rounding error. Route volume drafts to Gemini; escalate hard reasoning or premium edits to GPT-5.5.

6) Trust and refusal behavior

Near tie. Both refused Moon-cheese premises and Atlantis inflation inventions in our 2026-08-21 runs. Do not pick a stack from refusal alone; still ground publishable claims.

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Consumer plans vs API (do not mix them up)

SERP pages often blur ChatGPT-style subscriptions with API model IDs. Keep them separate:

• API comparison (this article): GPT-5.5 vs Gemini 3.1 Pro Preview in the API.
• Consumer apps: ChatGPT Plus/Pro experiences vs Google AI Pro / Gemini app may expose different tool defaults, rate limits, and bundled models (including Flash tiers).

If your question is “which $20-class subscription feels better on my phone,” run a week-long lived test in both apps. If your question is “which model ID should my agent call,” use this API page.

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Speed notes

Third-party pages disagree on exact tokens/sec depending on region and harness. Pattern across 2026 writeups: Gemini often streams competitively on Pro tiers; GPT-5.5 latency varies with reasoning effort and provider routing. For UX, measure your own p50 latency in the API from your region. Do not ship a product on someone else’s tok/s screenshot.

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Related comparisons in this silo

This page sits next to Grok 4.6 vs Gemini 3.1 Pro (same Gemini ID, different rival) and Claude Opus 5 vs GPT-5.5 (same GPT ID, Anthropic rival). Use the multi-model AI hub when you need the full routing map rather than a two-model duel.

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Frequently asked questions

Which is better overall, GPT-5.5 or Gemini 3.1 Pro?
Neither permanently. GPT-5.5 leads many professional coding/writing defaults and won our email rewrite on fidelity. Gemini wins API cost and multimodal breadth. Pick by job.

Which is better for coding?
Reputation and both models’ own routing matrices favor GPT-5.5 for Python and tooling loops. Our empty-list micro-test was a tie. For huge multimodal code+doc+video packs, Gemini’s modalities and price can matter more than the micro-test.

Which is better for writing?
Taste. In our rewrite, GPT stayed closer to subject-style facts; Gemini sounded warmer and more templated. A/B on your brand voice.

Which is cheaper?
At published API rates (2026-08-21), Gemini is cheaper on input ($2 vs $5), output ($12 vs $30), and cache reads ($0.20 vs $0.50). Our chat/repo/agent estimates put Gemini at roughly 40% of GPT-5.5’s cost.

Which has the larger context window?
Effectively a tie: GPT-5.5 at 1,050,000 vs Gemini 3.1 Pro Preview at 1,048,576 in the API.

Do I need both?
If your week mixes long documents, audio/video, premium coding, and volume drafts, yes. That is the multi-model thesis.

Are we comparing apps or API models?
This page uses API models GPT-5.5 and Gemini 3.1 Pro Preview. Consumer apps may wrap different defaults or tools.

How often should I re-test?
After any major version bump. Monthly is sane for production teams. Re-check vendor API prices whenever you budget agent loops.

Where can I run them side by side?
A multi-model workspace such as i10X workspace. Method guide: side-by-side AI comparison.

What about hallucinations and trust?
Both refused our false-premise and Atlantis traps. Still use source grounding and second-model checks for publishable claims. Multi-model hallucination checks.

Is Gemini Flash a better comparison partner for cost?
For speed/cost lanes, yes, compare Flash tiers separately. This page is Pro-class Gemini vs GPT-5.5 frontier.

Did model choice ever change outcomes in i10X research?
Yes, in hiring evals: up to a 42 percentage-point hire-rate gap for the same candidate depending on which AI wrote the resume ( ai-cv-bias). Different domain, same moral: which model you call is a product decision.

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