Grok 4.6 vs GPT-5.6 Sol vs Gemini 3.1 Pro: Which AI Model Should You Use in 2026?
Three flagship models. Three different pricing philosophies. And benchmark scores that are a lot closer than the marketing pages let on.
This post walks through pricing, benchmarks, context windows, and real use cases, so you can pick a model based on what you actually need instead of whichever one has the loudest launch post.
Grok 4.6 vs GPT-5.6 Sol vs Gemini 3.1 Pro at a Glance
Grok 4.6 came out on August 12, 2026, with a 500,000 token context window through the API (though that shrinks to 256,000 inside Cursor). Standard pricing sits at $2 per million input tokens and $6 per million output tokens, as long as you stay under 200K tokens of context. Cross that line and the price jumps to $4 and $12. On Artificial Analysis's Intelligence Index, Grok scores 61 at high reasoning effort. A SuperGrok subscription starts at $10 a month.
GPT-5.6 Sol reached general availability on July 9, 2026, and it's the flagship of a three-model family that also includes Terra and Luna. Sol's API context window runs close to 1.05 million tokens, though the consumer ChatGPT app caps its "Thinking" mode at 256,000 tokens as of a mid-August update. Pricing lands at $5 input and $30 output per million tokens, noticeably higher than Grok. Its Intelligence Index score ranges from 56 to 61 depending on which reasoning effort level you're looking at. ChatGPT Plus costs $20 a month.
Gemini 3.1 Pro is still Google's Pro-tier flagship, not the newer Gemini 3.7 Flash. It runs about $2 input and $12 output per million tokens under 200K context, rising to $4 and $18 beyond that. Context tops out around 1 million tokens. Its Intelligence Index score comes in lower than the other two at 57.0, but it posts the strongest science-reasoning result in this whole comparison: 94.3% on GPQA Diamond. Google AI Pro starts at $19.99 a month.
What Changed in Each Model's Latest Release
Grok 4.6
xAI built this release around long-running agent tasks, agentic coding, and interactive visual work rather than a straightforward chat upgrade, according to both Tech Insider and DataCamp. It's also worth knowing that Grok's predecessor, version 4.5, briefly lost access in the EU in July 2026 over an AI Act compliance issue. That got resolved by the time 4.6 launched, so it's a footnote at this point rather than a live concern.
GPT-5.6 Sol
OpenAI split this generation into three tiers instead of shipping one model. Sol is the flagship, Terra is the balanced middle option at $2 input and $12 output, and Luna is the cheap, high-volume option at $0.20 input and $1.20 output. All three keep the same output-to-input price ratio of 6x, which makes the family easy to reason about once you know the pattern. OpenAI also previewed an "Ultrafast" mode that runs up to 14 times faster than standard Sol, though it hadn't been priced as of this writing.
Gemini 3.1 Pro vs. Gemini 3.7 Flash
This is where a couple of the existing comparison articles get sloppy, so it's worth being precise. Gemini 3.1 Pro remains the Pro-tier flagship as of mid-August 2026. Gemini 3.7 Flash, which shipped on August 14, is a separate and much cheaper Flash-tier model built for high-volume, lower-cost workloads. It does not replace 3.1 Pro. Tech Insider puts it plainly: "any reference to a 'Gemini 3.7 Pro' model should be treated as inaccurate or premature." Flash launched with intro pricing of $0.75 input and $3.75 output per million tokens, running through the end of 2026 before it doubles to $1.50 and $7.50 on January 1, 2027.
Benchmark Performance: What the Numbers Actually Show
Most of these comparisons lean on the Artificial Analysis Intelligence Index, a composite score built from a basket of reasoning, coding, and knowledge benchmarks. It's a genuinely useful shorthand, but it has a habit of moving around in ways that aren't obvious from a single headline number.
Here's the catch. These scores shift depending on how much "reasoning effort" a model is allowed to use when it's tested. On Artificial Analysis's own head-to-head page, Grok 4.6 at high effort scores 61, while GPT-5.6 Sol at medium effort scores only 56. Bump Sol up to a higher effort setting and it climbs into the same 59 to 61 range as Grok, based on figures reported separately by Tech Insider and DataCamp. So when you see a bare score with no effort level attached, treat it as incomplete information rather than a final verdict.
It gets messier still. explainx.ai points out that there are two separate "Code Arena" leaderboards, one run by Google DeepMind and one run through LMArena, and while they use a similar Elo-style scale, the results aren't directly comparable. Along the same lines, DataCamp notes that xAI's Terminal-Bench 3.0 isn't the same suite as the Terminal-Bench 2.1 that Artificial Analysis uses in its index. Two numbers with the same name, tested on different scales.
With all that said, one benchmark result stands out as clean and well sourced: Gemini 3.1 Pro's 94.3% on GPQA Diamond, a graduate-level science reasoning test. Nobody else in this comparison comes close. On the coding side, GPT-5.6 Sol pulls ahead of Grok 4.6 by a clear margin, 7.1 points on DeepSWE and 8.6 points on Terminal-Bench, according to DataCamp. And on Google's own launch benchmarks, Gemini 3.7 Flash actually beats both Claude Sonnet 5 and GPT-5.6 Terra on three out of four headline tests, though it loses to Terra on DeepSWE V1.1, and Grok 4.6 doesn't appear on Google's charts at all, which is worth noting rather than glossing over.
Pricing Breakdown: API Costs and What They Mean for Real Usage
List prices only tell part of the story. Grok 4.6 charges $2 per million input tokens and $6 per million output tokens under 200K context, which makes it the cheapest of the three on paper. But there's a catch buried in how that pricing works. Once your request crosses the 200K token line, the entire request gets billed at the higher rate, $4 and $12, not just the tokens past the threshold. That's a cliff, not a slope, so it pays to know exactly where your typical request size lands.
There's a second wrinkle with Grok worth flagging. DataCamp found that time to first answer jumped from 14.62 seconds on Grok 4.5 to 40.44 seconds on Grok 4.6, and the actual cost of completing a task rose from $0.36 to $0.84, even though the list price per token didn't change. The model just uses more output tokens to work through the same problem. This is the single clearest example in the whole comparison of why sticker price and real cost aren't the same thing.
GPT-5.6 Sol runs $5 input and $30 output per million tokens, the most expensive of the three by list price. But OpenAI's caching discount is steep: a cached input read costs a tenth of the standard input price, per CODERCOPS. If your workload reuses a lot of the same context (a long system prompt, a big reference document), that discount can close the gap with cheaper competitors fast. Say you're processing 10 million output tokens a month. On Grok 4.6 that's roughly $60 in output costs alone, before input. On GPT-5.6 Sol it's roughly $300. On Gemini 3.1 Pro it lands around $120. Input costs and caching will move those numbers, but it gives you a sense of scale.
Gemini 3.1 Pro sits in the middle at $2 input and $12 output under 200K context, rising to $4 and $18 beyond it. If you only need a cheap, high-volume Gemini option and don't need Pro-level reasoning, Gemini 3.7 Flash at $0.75 input and $3.75 output is the better fit, at least until that intro rate doubles on January 1, 2027.
Context Window and Long-Document Handling
GPT-5.6 Sol wins on raw numbers, with roughly 1.05 million tokens of context through the API. Gemini 3.1 Pro is close behind at around 1 million. Grok 4.6 offers 500,000 tokens through its API, though that number drops to 256,000 the moment you're working inside Cursor.
The gap between API and consumer app matters here too. Even though Sol's API window is huge, the ChatGPT front end caps "Thinking" mode at 256,000 tokens as of mid-August. So if you're a developer working directly with the API, Sol's context advantage is real. If you're a non-technical user working inside the ChatGPT app, that advantage mostly disappears.
Consumer App Features (Search, Voice, Multimodal)
If you're not writing code and just want a subscription, here's roughly what each one gets you. SuperGrok, starting at $10 a month for the Lite tier, gives you access to Grok's chat and image tools, with SuperGrok Heavy at $300 a month unlocking the most agentic, longest-running task capabilities. ChatGPT Plus at $20 a month gets you Sol's chat interface, voice mode, and multimodal input, though capped at that 256K context ceiling mentioned above. Google AI Pro at $19.99 a month bundles Gemini access with Google's broader Workspace and Search integrations, and AI Ultra at $99.99 or $199.99 a month adds higher usage limits and priority access to new model releases.
Which Model Fits Which Job
High-volume customer support bot: Grok 4.6 or GPT-5.6 Luna are the ones to look at here. Grok's low list price works if your average request stays under the 200K token cliff. Luna, at $0.20 input and $1.20 output, is built specifically for this kind of high-throughput, lower-complexity workload.
Long-document or legal review: GPT-5.6 Sol's near-1.05 million token context window, combined with its steep caching discount, makes it a strong fit for reviewing large contracts or document sets repeatedly against the same reference material.
Agentic coding assistant: This one's closer than it looks. Grok 4.6 was built with agentic coding in mind, but GPT-5.6 Sol actually beats it on both DeepSWE and Terminal-Bench by a solid margin. If coding accuracy matters more than speed or cost, Sol is the safer pick. If you're running a lot of coding agent tasks and cost adds up fast, Grok is worth testing first.
Research assistant on hard reasoning or science questions: Gemini 3.1 Pro, no contest, thanks to that 94.3% GPQA Diamond score. If your work involves technical or scientific accuracy checks, this is the one model in the group with a clean, sourced edge.
Budget-conscious startup: Start with Grok 4.6 for general use and GPT-5.6 Luna or Gemini 3.7 Flash for high-volume, lower-stakes tasks. Save Gemini 3.1 Pro or GPT-5.6 Sol for the specific tasks that actually need the extra reasoning power, rather than running everything through the most expensive model by default.
Pros and Cons of Each Model
Grok 4.6 is the cheapest of the three under normal usage and comes with a genuinely large context window for an API-first workflow. The tradeoff is that pricing cliff at 200K tokens and a real-world cost increase that doesn't show up in the list price, since the model now uses more output tokens to finish the same tasks it used to handle faster.
GPT-5.6 Sol has the strongest coding benchmark results and the largest raw context window in this comparison, plus a caching discount that can meaningfully lower real-world costs for repetitive workloads. It's also the most expensive by list price, and its context advantage shrinks a lot if you're using the consumer ChatGPT app instead of the API directly.
Gemini 3.1 Pro has the standout science-reasoning score and pricing that lands in the middle of the pack. Its overall Intelligence Index score is the lowest of the three, and Google's own decision to ship a similarly named Flash model the same month created real confusion about which Gemini model is actually the flagship. If you go this route, be specific about which tier you're comparing.
The Verdict: Which Should You Choose
If you're a developer working with a tight budget, start with Grok 4.6, but build in a check for request size so you're not accidentally tripping the 200K token pricing cliff on a regular basis.
If you're an enterprise team processing large documents or running workloads that reuse the same context repeatedly, GPT-5.6 Sol is worth the higher list price once you factor in the caching discount and the coding benchmark lead.
If you're a researcher or analyst working on tasks where getting the answer right matters more than getting it cheap or fast, Gemini 3.1 Pro's GPQA Diamond score makes it the safer choice, just be sure you're actually pointing at 3.1 Pro and not the newer, cheaper 3.7 Flash sibling.
None of these are permanent rankings. Pricing on Gemini 3.7 Flash doubles on January 1, 2027, and the Artificial Analysis Intelligence Index gets recalculated as new evaluations land, so a score you see today can shift by a few points next month.
Frequently Asked Questions
Is Grok 4.6 cheaper than GPT-5.6 Sol?
Yes. Grok 4.6 charges $2 input and $6 output per million tokens under 200K context, compared to $5 input and $30 output for GPT-5.6 Sol. Grok's price rises once you cross 200K tokens, so the gap narrows for very long requests.
What is the difference between Gemini 3.1 Pro and Gemini 3.7 Flash?
Gemini 3.1 Pro is Google's Pro-tier flagship model. Gemini 3.7 Flash, released August 14, 2026, is a separate, cheaper Flash-tier model built for high-volume use. Flash does not replace or outrank 3.1 Pro.
Which model has the biggest context window?
GPT-5.6 Sol, with roughly 1.05 million tokens through the API. Gemini 3.1 Pro follows at around 1 million, and Grok 4.6 offers 500,000 through its API, dropping to 256,000 inside Cursor.
Which AI model is best for coding in 2026?
GPT-5.6 Sol currently leads on coding-specific benchmarks, beating Grok 4.6 by 7.1 points on DeepSWE and 8.6 points on Terminal-Bench. Grok 4.6 was still built with agentic coding as a priority and remains a strong, cheaper option.
Are Grok, GPT-5.6, and Gemini available for free?
All three offer limited free access through their consumer apps, but full capabilities require a paid plan. SuperGrok starts at $10 a month, ChatGPT Plus is $20 a month, and Google AI Pro is $19.99 a month.
Comparing Flagship AI Models for Your Workflow
With these three models converging on similar benchmark scores while diverging widely on pricing and context windows, the right choice depends entirely on your specific task. Alternates.ai helps you compare AI models side-by-side based on real-world use cases, so you can test pricing, benchmarks, and feature tradeoffs against the exact work your team does most before locking in a model.