{"id":916,"date":"2026-09-17T07:23:08","date_gmt":"2026-09-17T07:23:08","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=916"},"modified":"2026-09-28T07:23:30","modified_gmt":"2026-09-28T07:23:30","slug":"gemini-3-1-pro-vs-gpt-6-astra","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/gemini-3-1-pro-vs-gpt-6-astra","title":{"rendered":"Gemini 3.1 Pro vs GPT-6 Astra: Benchmarks, Price & Which to Pick (2026)"},"content":{"rendered":"\n\n\n
\n\n

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

\nGemini 3.1 Pro (Google API) and GPT-6 Astra (OpenAI API) are two flagship chat models teams actually route in 2026. This is a decision guide, not a leaderboard dump: published API rates, three workload cost scenarios, live writing and coding snippets, and a pick matrix you can rerun. If you want both without juggling tabs, use a\nmulti-model AI workspace<\/a>\nor start on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick Gemini 3.1 Pro if:<\/strong> you want much cheaper list rates ($2\/$12 vs $10\/$50), audio and video input on the card, cheaper cache reads ($0.20 vs $1.00), and a firm false-premise refuse that redirects to real geology.<\/p>\n

Pick GPT-6 Astra if:<\/strong> you want OpenAI\u2019s flagship stack for long-horizon engineering and research, tighter short client email, or a coding style that prefers explicit ValueError guards over returning zero.<\/p>\n

Best default for many teams:<\/strong> route by task. Our three-prompt pack tied at 69\/75. Context is nearly tied (~1.05M both). Price and multimodal reach still split the week.<\/p>\n

Data checked: 2026-09-07. Prices and model cards change. Verify live API and vendor pages.<\/em><\/p>\n<\/div>\n\n

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

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

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

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

GPT-6 Astra context (API)<\/p><\/td>\n<\/tr>\n

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

Gemini 3.1 Pro input\/output per 1M tokens (API pricing, 2026-09-07)<\/p><\/td>\n<\/tr>\n

$10 \/ $50<\/strong><\/p><\/td>\n

GPT-6 Astra input\/output per 1M tokens (API pricing, 2026-09-07)<\/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 (context, output cost efficiency, multimodal reach, coding micro-test, cache-read price). Higher is stronger for that axis. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n

Persona picker<\/h2>\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 \/ marketer<\/p><\/td>\n

A\/B both; Astra if you want less greeting energy<\/p><\/td>\n

Both scored 23\/25. Gemini used a warm week greeting and name slots. Astra was shorter and tighter.<\/p><\/td>\n<\/tr>\n

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

Either for small bugs; Gemini for cost<\/p><\/td>\n

Both scored 24\/25. Gemini returned 0 on empty; Astra raised ValueError. Windows are nearly tied.<\/p><\/td>\n<\/tr>\n

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

Gemini when media is in the pack<\/p><\/td>\n

Both ~1.05M. Gemini card lists audio and video. Astra stays text\/image\/file.<\/p><\/td>\n<\/tr>\n

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

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

$2\/$12 vs $10\/$50. Gemini wins every stylized workload we priced, including the cached agent loop ($0.46 vs $2.10).<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

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

This page compares two specific API models, not vague \u201cGemini vs GPT\u201d brands and not GPT-5.6 Sol. See\nGrok 4.6 vs GPT-6 Astra<\/a>\nand\nGrok 4.6 vs Gemini 3.1 Pro<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

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

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

GPT-6 Astra<\/p><\/th>\n<\/tr>\n

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

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

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

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

Gemini 3.1 Pro (Google API)<\/code><\/p><\/td>\n

GPT-6 Astra (OpenAI API)<\/code><\/p><\/td>\n<\/tr>\n

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

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

OpenAI: GPT-6 Astra<\/p><\/td>\n<\/tr>\n

Family \/ tier<\/p><\/td>\n

Gemini 3.1 Pro (Preview card string)<\/p><\/td>\n

GPT-6 series flagship<\/p><\/td>\n<\/tr>\n

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

Also in Gemini apps; this article uses the API model above<\/p><\/td>\n

Also in ChatGPT-family apps; this article uses the API model above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The public short name is Gemini 3.1 Pro. The card we pulled still said Preview once in the listed name. If a page still compares Gemini 2.5 or GPT-5.6 Sol as if they were these IDs, treat it as historical. For routing across many models, see\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API card, 2026-09-07)<\/h2>\n\n\n\n\n\n\n\n\n

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

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

GPT-6 Astra<\/p><\/th>\n<\/tr>\n

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

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

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

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

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

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

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

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

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

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

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

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

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

Google frontier reasoning model; enhanced software engineering, improved agentic reliability, more efficient token usage across complex workflows; multimodal foundation<\/p><\/td>\n

OpenAI flagship for demanding end-to-end work; advanced analysis, software engineering, deep research, scientific work, and document creation, with long-horizon strengths<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We did not invent max-output, reasoning-mode, or realtime-search rows. Those fields were not on the cards we pulled for this pair. If your product depends on a specific tool or effort switch, confirm it on the live vendor page before you ship.<\/p>\n\n

\n\n

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

List prices are easy to game. Workload cost is what you feel. Numbers below use published per-million rates as of 2026-09-07<\/strong>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

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

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

GPT-6 Astra<\/p><\/th>\n<\/tr>\n

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

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

$10.00<\/p><\/td>\n<\/tr>\n

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

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

$50.00<\/p><\/td>\n<\/tr>\n

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

$0.20<\/p><\/td>\n

$1.00<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Gemini is far cheaper on input, output, and cache. Context is essentially tied, so Astra\u2019s case has to be vendor stack, tone preference, or a specific tool path, not window size.<\/p>\n\n\n\n\n\n\n

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

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

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

Est. GPT-6 Astra<\/p><\/th>\n<\/tr>\n

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

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

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

$0.035<\/p><\/td>\n<\/tr>\n

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

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

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

$1.000<\/p><\/td>\n<\/tr>\n

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

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

$0.460<\/p><\/td>\n

$2.100<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-09-07). Chat is 1k in + 0.5k out. Repo review is 80k in + 4k out. Agent loop is 200k in with 50% cache hits + 20k out. Chart: i10X.<\/figcaption>\n<\/figure>\n

Gemini wins chat ($0.008 vs $0.035), repo ($0.208 vs $1.00), and the cached agent loop ($0.46 vs $2.10). Re-run the math if your cache hit rate or output length differs. For subscription stacks, see\nAI subscription stack cost<\/a>.<\/p>\n\n

\n\n

Performance by job (not one score)<\/h2>\n

Public benches disagree by harness, effort mode, and date. We are not inventing index numbers we do not have for this pair. Treat vendor cards as positioning, treat our snippets as a small live pack, and prefer your own side-by-side on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Google\u2019s card sells enhanced software engineering and agentic reliability. OpenAI\u2019s card frames Astra for demanding end-to-end software engineering and long-horizon work. Our micro-test tied at 24\/25: both named ZeroDivisionError<\/code>. Gemini\u2019s minimal fix returned 0<\/code> with an inline guard. Astra raised a descriptive ValueError<\/code>. Windows are nearly identical, so cost and empty-input contract matter more than context here.<\/p>\n\n

Writing and tone<\/h3>\n

Both scored 23\/25. Gemini wrote a warmer letter with a week greeting and name slots. Astra compressed the same facts into a shorter follow-up. If your brand wants friendly client email, Gemini was closer. If you hate greeting filler, Astra was tighter.<\/p>\n\n

Research, math, reasoning<\/h3>\n

We did not run a science-QA pack, so we will not fake one. Both refused the green-cheese Moon and scored 22\/25. Gemini stopped hard and offered real lunar geology or Earth-side protein topics instead. Astra refused, then sketched bioreactors and recycled water. Both pass. Style differs. For publishable work, add\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Multimodal and long context<\/h3>\n

This is the clearest non-price split. Gemini\u2019s card lists audio, file, image, text, and video in. Astra lists text, image, and file. Context is essentially tied (~1.05M). Media-heavy packs favor Gemini on modalities alone. For open-weight multimodal options nearby, see\nKimi K3 vs GPT-6 Astra<\/a>.<\/p>\n\n

Speed<\/h3>\n

No tok\/s in this pack. Measure p50 from your API region. Do not ship on a third-party screenshot.<\/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 \/ agents<\/p><\/td>\n

Split<\/p><\/td>\n

Micro-test tie. Both sell agentic reliability. Gemini wins price. Empty-list fix philosophy differs.<\/p><\/td>\n<\/tr>\n

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

Split \/ tone-dependent<\/p><\/td>\n

Same 23\/25. Gemini warmer. Astra tighter.<\/p><\/td>\n<\/tr>\n

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

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

Near-tied context. Gemini adds audio and video on the card.<\/p><\/td>\n<\/tr>\n

Realtime \/ conversational<\/p><\/td>\n

Product-dependent<\/p><\/td>\n

Confirm tools in Gemini apps vs ChatGPT apps. Not measured here.<\/p><\/td>\n<\/tr>\n

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

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

$2\/$12 vs $10\/$50. Cheaper on chat, repo, and our cached agent recipe by a wide margin.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

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

Leaderboards mix harnesses, tool settings, and effort modes. When we do not have a primary table for a pair, we do not invent one. Re-test on your workload.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (i10X pack, 2026-09-07)<\/h2>\n

Same three prompts, scored 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25). Writing, coding, and false premise only. No invented extra scores.<\/p>\n\n

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

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

Gemini 3.1 Pro (excerpt, sanitized):<\/strong> Name slot, great-week greeting, Q3 deck last Tuesday, finance still missing after Friday, Wednesday stakeholder ask, Acme pricing note, flexibility thanks.<\/p>\n

GPT-6 Astra (excerpt, sanitized):<\/strong> Same facts in a shorter follow-up, Finance capitalized, Wednesday ask, Acme line, \u201cThanks for your help!\u201d<\/p>\n

Edge:<\/strong> Soft tie on score (23\/25). Gemini for warmth. Astra for brevity.<\/p>\n\n

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

Both named ZeroDivisionError<\/code> and scored 24\/25. Gemini\u2019s minimal fix: return 0<\/code> with an inline if nums else 0<\/code> guard and a print check. Astra\u2019s minimal fix: raise ValueError<\/code>, with None<\/code> as an alternate. Tie on quality; pick the contract you want.<\/strong><\/p>\n\n

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

Both refused first and scored 22\/25. Gemini: folklore, rock and dust, no cheese mining, offer to talk real geology or Earth protein tech. Astra: same refuse, then starter cultures, bioreactors, solar power. Both pass.<\/strong> Gemini stricter redirect. Astra more pedagogical plan.<\/p>\n\n\n\n\n\n\n\n\n

Prompt type<\/p><\/th>\n

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

GPT-6 Astra<\/p><\/th>\n

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

Client email rewrite<\/p><\/td>\n

23\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Gemini warmer. Astra tighter.<\/p><\/td>\n<\/tr>\n

Bug explain + minimal fix<\/p><\/td>\n

24\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Both catch ZeroDivisionError. Return 0 vs raise ValueError.<\/p><\/td>\n<\/tr>\n

Logic + false premise<\/p><\/td>\n

22\/25<\/p><\/td>\n

22\/25<\/p><\/td>\n

Both refuse. Gemini redirects to geology. Astra adds a protein plan.<\/p><\/td>\n<\/tr>\n

Total<\/strong><\/p><\/td>\n

69\/75<\/strong><\/p><\/td>\n

69\/75<\/strong><\/p><\/td>\n

Pack tie. Jobs still split on cost and modalities.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A tied pack does not erase Gemini\u2019s price and multimodal wins, or Astra\u2019s vendor-stack reasons. Route the next step.<\/p>\n\n

\n\n

Ecosystem and where you run them<\/h2>\n