{"id":436,"date":"2026-08-21T07:48:57","date_gmt":"2026-08-21T07:48:57","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=436"},"modified":"2026-08-21T08:44:35","modified_gmt":"2026-08-21T08:44:35","slug":"grok-4-6-vs-gemini-3-1-pro","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/grok-4-6-vs-gemini-3-1-pro","title":{"rendered":"Grok 4.6 vs Gemini 3.1 Pro: Benchmarks, Price, Side-by-Side Tests (2026)"},"content":{"rendered":"\n
\n\n

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

\nGrok 4.6 and Gemini 3.1 Pro are two frontier chat models people actually argue about in 2026. This guide is a decision piece, not a leaderboard dump: live pricing caveats, three workload cost scenarios, public benchmark signals, 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 Grok 4.6 if:<\/strong> you want stronger agentic\/knowledge-work signals, cheaper output tokens at the same $2\/M input, and a sharp default for email + everyday coding.<\/p>\n

Pick Gemini 3.1 Pro if:<\/strong> you need 1M context, native audio\/video input, better vision\/OCR for screenshots and documents, or Google-ecosystem fit.<\/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

500K<\/strong><\/p><\/td>\n

Grok 4.6 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

$2 \/ $6<\/strong><\/p><\/td>\n

Grok 4.6 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 (context, modalities, output cost efficiency, agentic APEX signal, vision). 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

Grok 4.6 (often)<\/p><\/td>\n

In our rewrite test, Grok stayed tighter to the facts; Gemini added warmer filler.<\/p><\/td>\n<\/tr>\n

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

Grok 4.6 default; Gemini for huge repos\/docs<\/p><\/td>\n

Public agentic indexes currently favor Grok 4.6; coding bug fix was a tie in our micro-test.<\/p><\/td>\n<\/tr>\n

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

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

1M context + broader multimodal inputs (PDF\/audio\/video) for long packs.<\/p><\/td>\n<\/tr>\n

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

Grok 4.6<\/p><\/td>\n

Same $2\/M input, half the output price ($6 vs $12) at current API list rates.<\/p><\/td>\n<\/tr>\n

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

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

Stronger public vision evals; native audio\/video input on the API model card.<\/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 \u201cGrok 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

Grok 4.6<\/p><\/th>\n

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

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

xAI (listed as SpaceXAI in the API)<\/p><\/td>\n

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

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

Grok 4.6<\/code><\/p><\/td>\n

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

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

SpaceXAI: Grok 4.6<\/p><\/td>\n

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

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

12 Aug 2026 (press\/AA coverage)<\/p><\/td>\n

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

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

Also available in xAI \/ X 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 Grok 4 to Gemini 3 Pro without the 4.6 \/ 3.1 labels, treat them as older. For 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

Grok 4.6<\/p><\/th>\n

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

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

500,000 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<\/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 coding, knowledge work, STEM<\/p><\/td>\n

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

Grok 4.6<\/p><\/th>\n

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

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

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

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

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

$6.00<\/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

Same input sticker. Gemini costs 2\u00d7 on output<\/strong>. Gemini cache reads are cheaper if your stack actually hits cache.<\/p>\n\n\n\n\n\n\n

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

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

Est. Grok 4.6<\/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

$0.005<\/p><\/td>\n

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

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

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

$0.184<\/p><\/td>\n

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

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

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

$0.370<\/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

For subscription stacks (Plus \/ Pro \/ SuperGrok 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

\n\"Horizontal\n
Figure 3.<\/strong> Snapshot of widely cited public signals (AA Index, APEX-Agents, GPQA Diamond, Roboflow vision overall, context normalized). Sources below. Chart: i10X.<\/figcaption>\n<\/figure>\n\n\n\n\n\n\n\n\n\n

Signal<\/p><\/th>\n

Grok 4.6<\/p><\/th>\n

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

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

Artificial Analysis Intelligence Index<\/p><\/td>\n

~61 (high effort reporting)<\/p><\/td>\n

~48 (index snapshots around Aug 2026)<\/p><\/td>\n

Grok leads this composite in recent writeups<\/p><\/td>\n<\/tr>\n

APEX-Agents<\/p><\/td>\n

~57.5%<\/p><\/td>\n

~33.5%<\/p><\/td>\n

Grok stronger on long-horizon agent tasks in cited boards<\/p><\/td>\n<\/tr>\n

GPQA Diamond<\/p><\/td>\n

~93.2%<\/p><\/td>\n

~94.3-94.4%<\/p><\/td>\n

Near tie; Gemini often slight edge on science QA<\/p><\/td>\n<\/tr>\n

Vision overall (Roboflow Vision Evals, Aug 2026)<\/p><\/td>\n

~67.8%<\/p><\/td>\n

~83.1%<\/p><\/td>\n

Gemini clearer lead, especially object detection<\/p><\/td>\n<\/tr>\n

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

500K<\/p><\/td>\n

~1M<\/p><\/td>\n

Gemini for giant packs<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\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

Recent Artificial Analysis coverage places Grok 4.6 on the intelligence frontier with strong agentic scores (APEX-Agents, GDPval-AA style boards). Gemini 3.1 Pro remains a serious engineering model with tool use, but the Aug 2026 public agentic gap in those writeups favors Grok. Throughput\/speed measurements often favor Gemini\u2019s token streaming even when agent indexes do not.<\/p>\n\n

Writing and tone<\/h3>\n

Benchmarks barely measure voice. That is why we ran the email rewrite below. Expect Grok to sound more direct. Expect Gemini to sound more \u201cpolished corporate\u201d and sometimes to add soft filler.<\/p>\n\n

Multimodal and long context<\/h3>\n

This is Gemini\u2019s clearest structural win: larger context and audio\/video on the API model card, plus stronger public vision eval averages. If your day is PDFs, screenshots, and meeting video, Gemini is the safer 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 \/ agentic work<\/p><\/td>\n

Grok 4.6<\/p><\/td>\n

Stronger recent agentic index signals<\/p><\/td>\n<\/tr>\n

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

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

Grok tighter to facts in our rewrite; Gemini warmer<\/p><\/td>\n<\/tr>\n

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

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

1M context + audio\/video + vision lead<\/p><\/td>\n<\/tr>\n

Realtime \/ X-native workflows<\/p><\/td>\n

Grok family (product)<\/p><\/td>\n

xAI\/X product stack; confirm search tools in your app<\/p><\/td>\n<\/tr>\n

Cost at output-heavy volume<\/p><\/td>\n

Grok 4.6<\/p><\/td>\n

$6 vs $12 output \/ 1M at current API list rates<\/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 Grok 4.6<\/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

Grok 4.6 (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. Short, complete, no invented cheer.<\/p>\n

Gemini 3.1 Pro (excerpt):<\/strong> Same facts, plus greeting energy (\u201chope you\u2019re having a great week\u201d), name placeholders, and softer closing. Warmer, slightly more corporate template.<\/p>\n

Edge:<\/strong> Grok for fidelity and brevity. Gemini for polished warmth. If your brand voice hates filler, prefer Grok.<\/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. Grok was shorter. Gemini added a helpful redirect to real lunar resources. Both pass<\/strong>; Grok more concise, Gemini more pedagogical.<\/p>\n\n

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

Grok refused Atlantis inflation and asked for a real statistical office. Gemini (earlier run) also refused and asked for a real 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: They disagree on the SaaS default (useful!)<\/h3>\n

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