{"id":496,"date":"2026-08-25T06:35:10","date_gmt":"2026-08-25T06:35:10","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=496"},"modified":"2026-08-25T06:35:11","modified_gmt":"2026-08-25T06:35:11","slug":"grok-4-6-vs-gpt-5-6-sol","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/grok-4-6-vs-gpt-5-6-sol","title":{"rendered":"Grok 4.6 vs GPT-5.6 Sol: Benchmarks, Price & Which to Pick (2026)"},"content":{"rendered":"\n
\n\n

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

\nGrok 4.6 (xAI API) and GPT-5.6 Sol (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 Grok 4.6 if:<\/strong> you want cheaper output at the same $2\/M input ($6 vs $10), a 500K default that is still large, and concise factual email that does not invent greeting energy.<\/p>\n

Pick GPT-5.6 Sol if:<\/strong> you need ~1.05M context, cheaper cache reads ($0.20 vs $0.50), tighter client-ready prose, or OpenAI\u2019s flagship positioning for command-line and multi-step coding work.<\/p>\n

Best default for many teams:<\/strong> route by task and keep both available. See the decision matrix below.<\/p>\n

Data checked: 2026-08-24. Prices and model cards change. Verify live API test and vendor pages.<\/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

GPT-5.6 Sol context (API)<\/p><\/td>\n<\/tr>\n

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

Grok 4.6 input\/output per 1M tokens (API pricing, 2026-08-24)<\/p><\/td>\n<\/tr>\n

$2 \/ $10<\/strong><\/p><\/td>\n

GPT-5.6 Sol input\/output per 1M tokens (API pricing, 2026-08-24)<\/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, writing tightness, coding micro-test, cache-read price). 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

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

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

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

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

GPT-5.6 Sol (often); Grok for terse ops email<\/p><\/td>\n

In our rewrite, GPT-5.6 Sol was more client-ready (name, two tight paragraphs, all facts). Grok kept every fact with less polish.<\/p><\/td>\n<\/tr>\n

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

Either for small bugs; GPT-5.6 Sol for huge repos<\/p><\/td>\n

Our empty-list bug fix was a tie. Sol\u2019s 1.05M context and cheaper cache matter more on long agent loops than this micro-test.<\/p><\/td>\n<\/tr>\n

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

GPT-5.6 Sol<\/p><\/td>\n

About 2\u00d7 the context (1.05M vs 500K) for long packs. Same text\/image\/file input set on the cards we pulled.<\/p><\/td>\n<\/tr>\n

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

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

Matched $2\/M input, $6 vs $10 output. Grok wins every stylized workload we priced, including the cached agent loop.<\/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 \u201cGrok vs GPT\u201d brands and not GPT-5.5. See\nGPT-5.5 vs Gemini 3.1 Pro<\/a>\nand\nClaude Opus 5 vs GPT-5.5<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

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

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

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

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

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

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

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

Grok 4.6 (xAI API)<\/code><\/p><\/td>\n

GPT-5.6 Sol (OpenAI API)<\/code><\/p><\/td>\n<\/tr>\n

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

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

OpenAI: GPT-5.6 Sol<\/p><\/td>\n<\/tr>\n

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

xAI flagship<\/p><\/td>\n

GPT-5.6 series flagship<\/p><\/td>\n<\/tr>\n

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

Also in xAI \/ X products; 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

If a page still compares Grok 4 or GPT-5.5 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, 2026-08-24)<\/h2>\n\n\n\n\n\n\n\n\n

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

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

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

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

500,000 tokens<\/p><\/td>\n

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

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

text, image, file<\/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

Smartest xAI model; frontier coding, knowledge work, and STEM<\/p><\/td>\n

GPT-5.6 flagship; complex reasoning, coding, agentic workflows; strong at command-line and multi-step coding<\/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 2026-08-24<\/strong>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

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

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

GPT-5.6 Sol<\/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

$10.00<\/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. Grok is cheaper on output ($6 vs $10). Sol is cheaper on cache reads ($0.20 vs $0.50). If your agent stack actually hits cache, that gap matters. If it does not, output price dominates.<\/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. GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

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

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

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

$0.007<\/p><\/td>\n<\/tr>\n

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

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

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

$0.200<\/p><\/td>\n<\/tr>\n

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

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

$0.370<\/p><\/td>\n

$0.420<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-08-24). 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

Grok still wins the cached agent loop ($0.37 vs $0.42) because output outweighs Sol\u2019s cheaper cache in this recipe. Re-run the math if you cache harder. 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

xAI\u2019s card calls Grok 4.6 its smartest model for coding, knowledge work, and STEM. OpenAI\u2019s card calls Sol the GPT-5.6 flagship and singles out command-line and multi-step coding. Those are positioning lines, not benches. Our micro-test was a tie: both named ZeroDivisionError<\/code> and wrote the same empty-list guard. For huge repos, Sol\u2019s 1.05M window is the structural advantage. For volume loops, Grok\u2019s output rate is.<\/p>\n\n

Writing and tone<\/h3>\n

Grok stayed complete and slightly telegram-like (no name slot, every operational fact). Sol compressed the same facts into a cleaner two-paragraph letter. If your brand wants send-ready client email, Sol was closer. If you hate filler, both beat the warmer Gemini-style templates on other pairs.<\/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. Grok stopped at \u201cno mining plan exists.\u201d Sol refused, then listed polar ice, water split, habitat protein, and recycle loops. Usefulness split, not a math proof. For publishable work, add\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Multimodal and long context<\/h3>\n

Both cards list text, image, and file in, text out. No audio or video in this pull. The live gap is 500K vs 1.05M. Giant packs go to Sol. Short threads can stay on Grok. For audio\/video, see\nGrok 4.6 vs Gemini 3.1 Pro<\/a>\nand\nGPT-5.6 Sol vs Gemini 3.1 Pro<\/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. Sol: context + cheaper cache + command-line positioning. Grok: cheaper output.<\/p><\/td>\n<\/tr>\n

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

GPT-5.6 Sol<\/p><\/td>\n

Tighter client email in our rewrite. Grok still complete, less polished.<\/p><\/td>\n<\/tr>\n

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

GPT-5.6 Sol<\/p><\/td>\n

1.05M vs 500K. Same text\/image\/file set. Neither is the audio\/video specialist.<\/p><\/td>\n<\/tr>\n

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

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

Grok family is strong in xAI\/X product contexts. Confirm tools in your app. Not measured here.<\/p><\/td>\n<\/tr>\n

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

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

$6 vs $10 output. Cheaper on chat, repo, and our cached agent recipe.<\/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, 2026-08-24)<\/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

Grok 4.6 (excerpt, sanitized):<\/strong> Q3 deck last Tuesday. Finance still missing after Friday. Push stakeholders to next week, perhaps Wednesday. Competitive slide needs new Acme pricing. \u201cThank you!\u201d No name slot.<\/p>\n

GPT-5.6 Sol (excerpt, sanitized):<\/strong> Same facts in two tight paragraphs, [Name]<\/code> slot, a single Wednesday ask, complete Acme line, \u201cThanks!\u201d<\/p>\n

Edge:<\/strong> Sol for send-ready tightness. Grok for fidelity with zero extra cheer. Neither added Gemini-style week-greeting filler.<\/p>\n\n

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

Both named ZeroDivisionError<\/code> and proposed if not nums: return 0<\/code>. Sol added print(average([]))<\/code>. Same fix. Tie.<\/strong><\/p>\n\n

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

Both refused first. Grok: silicate rock, no protein, no mining plan exists. Sol: same refuse, then polar ice, water split, habitat protein, recycle. Both pass.<\/strong> Grok shorter. Sol more pedagogical.<\/p>\n\n\n\n\n\n\n\n\n

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

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

GPT-5.6 Sol<\/p><\/th>\n

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

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

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

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

Sol tighter and more send-ready. Grok complete, less polished.<\/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. Tie.<\/p><\/td>\n<\/tr>\n

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

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

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

Both refuse. Grok shorter. Sol adds a useful redirect.<\/p><\/td>\n<\/tr>\n

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

68\/75<\/strong><\/p><\/td>\n

72\/75<\/strong><\/p><\/td>\n

Close. Jobs still split on cost and context.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Sol wins this pack on writing tightness. That does not erase Grok\u2019s output-price win. Route the next step.<\/p>\n\n

\n\n

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