Three frontier AI models went head-to-head in the space of two weeks this summer, and the pricing gap between them is bigger than the performance gap. xAI put Grok 4.5 into private beta at SpaceX and Tesla on June 28, 2026, then opened public API access the week of July 8. OpenAI’s GPT-5.6 landed around the same window with a three-tier pricing structure. Google DeepMind’s Gemini 3.1 Pro had already been running for months as the reasoning benchmark other labs measured themselves against. Now all three are competing for the same developer budgets, and the numbers don’t point to one obvious winner.
The headline gap is cost. GPT-5.6’s flagship Sol tier charges $30 per million output tokens. Grok 4.5 charges $6. That’s a $24 gap on every million tokens a model writes back to you, and at agentic-coding scale, where a single task can burn tens of thousands of output tokens, it adds up fast. But cost isn’t the whole story: Gemini 3.1 Pro still leads on raw reasoning benchmarks, and GPT-5.6 remains the only one of the three offering a full three-tier pricing ladder. This comparison walks through the specs, the pricing, the independently published benchmarks, and where each model actually wins in production.
This matters beyond the usual model-launch news cycle because these three are the default choices most engineering teams are now weighing against each other for anything from customer support agents to autonomous coding pipelines. Picking wrong isn’t just an inconvenience; at production scale, a per-token pricing gap this large compounds into a real budget line item within the first billing cycle. Every figure below is sourced to a named tracker, official pricing page, or independent benchmark comparison, with links included so you can verify the numbers yourself rather than take a single article’s word for it.
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What Changed: Grok 4.5, GPT-5.6, and Gemini 3.1 Pro at a Glance
Grok 4.5 is the newest of the three. xAI ran it as a private beta with SpaceX and Tesla starting June 28, 2026, before flipping on public API access in early July, according to launch coverage aggregated by AIToolsRecap’s July 10, 2026 roundup. xAI’s own positioning leaned hard on efficiency: the company’s launch materials described Grok 4.5 as a coding and agentic model that gets to a correct answer using dramatically fewer tokens than its closest rivals, a claim that shows up consistently across independent pricing trackers.
GPT-5.6 launched in the same general window with a different strategy entirely: instead of one model, OpenAI shipped three price points under one name. Sol is the flagship, Terra is the mid-tier, and Luna is the budget option, each with its own input and output pricing. That structure matters for this comparison because “GPT-5.6” isn’t a single number anymore. Most third-party trackers, including Emergent’s GPT-5.6 alternatives breakdown, treat Sol as the model to benchmark against Grok 4.5 and Gemini 3.1 Pro, so that’s the tier this article uses unless noted otherwise.
Gemini 3.1 Pro is the elder statesman of the group. It’s been generally available long enough to accumulate a much deeper independent benchmark record than either newcomer, and it’s the model that set the reasoning bar (94.6% on GPQA Diamond, 77.8.73.3% on ARC-AGI-2 that Grok 4.5 and GPT-5.6 are now measured against. That head start shows up throughout this comparison: there’s simply more third-party data on Gemini 3.1 Pro than on the two models that launched weeks ago.
The timing of these three launches landing so close together isn’t a coincidence so much as a pattern that’s become routine in the frontier model market: whenever one lab ships a meaningfully cheaper or more capable model, the other two respond within weeks rather than quarters. Grok 4.5’s aggressive output pricing is a direct continuation of that pattern, arriving not long after Tech Insider covered its predecessor’s own pricing story in Grok 4.5 Debuts: Cheaper Than Claude Opus 4.6.8. GPT-5.6’s tiered launch looks like a direct response to that same pressure, giving OpenAI a budget option (Luna) to compete on price without discounting its flagship.
Full Specs Comparison: Grok 4.5 vs GPT-5.6 vs Gemini 3.1 Pro
Here’s the complete side-by-side, pulling together pricing, context window, and the benchmark scores that are independently verifiable as of mid-2026. Where a figure isn’t published by an independent tracker for a specific model, the table says so rather than guessing.
| Spec | Grok 4.5 (xAI) | GPT-5.6 Sol (OpenAI) | Gemini 3.1 Pro (Google DeepMind) |
|---|---|---|---|
| Input price (per 1M tokens) | $2.00 | $5.00 | $2.00 |
| Output price (per 1M tokens) | $6.00 | $30.00 | $12.00 |
| Cached input price | $0.50 | Not publicly disclosed | Not publicly disclosed |
| Context window | 500,000 tokens | ~1,000,000 tokens | 1,000,000 tokens |
| Pricing structure | Single tier | Three tiers (Sol, Terra, Luna) | Single tier |
| Public release timing | Private beta Jun 28, 2026; public API week of Jul 8, 2026 | Rolled out alongside Grok 4.5, exact date not officially published | Generally available since earlier in 2026 |
| GPQA (science reasoning) | 93.0% | Not independently published | 94.3% |
| SWE-Bench Pro (coding) | 64.7% | Not independently published | Not independently published |
| ARC-AGI-2 (abstract reasoning) | Not independently published | Not independently published | 77.1% |
| Artificial Analysis Intelligence Index | 54 (#4 of 168 models) | ~58.9 | Reported between 46.5 and 57 depending on tracker |
| Early enterprise adopters | SpaceX, Tesla (private beta) | Not disclosed | Broad existing Google Cloud/Vertex AI customer base |
| Best-known strength | Token-efficient agentic coding | Generalist flagship, tiered pricing | Reasoning and long-document research |
Two things jump out immediately. First, Grok 4.5 undercuts both rivals on output pricing by a wide margin. Second, the benchmark row is lopsided: Gemini 3.1 Pro and Grok 4.5 both show up in the same independent comparison tables (largely thanks to llm-stats.com’s direct Gemini 3.1 Pro vs. Grok 4.5 comparison), but GPT-5.6 Sol is harder to pin down on the exact same tests, since OpenAI hadn’t published a matching benchmark card at the time of writing.
Pricing Breakdown: The $24 Output Gap Explained
Pricing is where this comparison gets concrete fastest, and it’s also where the three models diverge the most. Grok 4.5 prices at $2 per million input tokens and $6 per million output tokens, with a discounted $0.50 rate for cached input, figures that show up consistently across independent pricing aggregators including CometAPI’s pricing guide and Gate.ai’s Grok 4.5 spec sheet, and match xAI’s own developer pricing documentation.
GPT-5.6 is the outlier because it isn’t one price at all. Sol, the flagship tier, runs $5 input and $30 output per million tokens. Terra sits in the middle at $2.50 input and $15 output. Luna, the lightweight option, matches Grok 4.5’s output price exactly at $1 input and $6 output. That tiering means the fair comparison depends on which GPT-5.6 tier a team actually deploys: stack Grok 4.5 against Sol and the gap is enormous; stack it against Luna and the two are roughly at parity on output cost, with Grok 4.5 still cheaper on input.
Gemini 3.1 Pro lands in the middle at $2 input and $12 output per million tokens, according to AIPriceCompare’s model tables — though it’s worth flagging that not every tracker agrees on the exact number. llm-stats.com’s own comparison page lists Gemini 3.1 Pro closer to $2.50 input and $15 output, and using that higher figure in its own blended calculation (a 3:1 input-to-output ratio), the site still found Grok 4.5 roughly 1.9 times cheaper per token overall. Either way you slice it, Grok 4.5 comes out as the cheapest of the three on a per-token basis, and Gemini 3.1 Pro’s output price sits at exactly double Grok’s.
| Model / tier | Input $/1M | Output $/1M | Cached input $/1M | Context window |
|---|---|---|---|---|
| Grok 4.5 | $2.00 | $6.00 | $0.50 | 500K tokens |
| GPT-5.6 Sol (flagship) | $5.00 | $30.00 | Not disclosed | ~1M tokens |
| GPT-5.6 Terra (mid-tier) | $2.50 | $15.00 | Not disclosed | ~1M tokens |
| GPT-5.6 Luna (budget) | $1.00 | $6.00 | Not disclosed | ~1M tokens |
| Gemini 3.1 Pro | $2.00–$2.50 | $12.00–$15.00 | Not disclosed | 1M tokens |
The practical takeaway: teams running high-output workloads (long code generations, multi-step agent traces, verbose reasoning chains) feel the output price far more than the input price, because output tokens are what these models spend the most compute generating. That’s exactly why Grok 4.5’s $6 output rate is the number xAI leads with, and why AIToolsRecap’s launch analysis called it “the best intelligence-per-dollar coding model in the current market.”
How Much Would You Actually Pay? A Monthly Cost Example
List prices are easier to compare once they’re run through an actual workload. Take a hypothetical mid-sized deployment that processes 10 million input tokens and 10 million output tokens in a month, roughly the scale of a team running a always-on coding assistant or a moderately busy customer-facing agent. Multiplying each model’s published per-token rate by that volume produces a real monthly figure, not just an abstract per-token comparison.
| Model / tier | Cost on 10M input tokens | Cost on 10M output tokens | Estimated monthly total |
|---|---|---|---|
| Grok 4.5 | $20 | $60 | $80 |
| GPT-5.6 Luna | $10 | $60 | $70 |
| GPT-5.6 Terra | $25 | $150 | $175 |
| Gemini 3.1 Pro | $20 | $120 | $140 |
| GPT-5.6 Sol | $50 | $300 | $350 |
At this illustrative 10M/10M volume, GPT-5.6 Sol costs 4.4 times more per month than Grok 4.5 and 2.5 times more than Gemini 3.1 Pro. GPT-5.6 Luna and Grok 4.5 land within $10 of each other, which is why the tier comparison matters so much more than the brand name: “GPT-5.6” can mean a $70-a-month bill or a $350-a-month bill for the exact same 10M/10M workload, depending entirely on which tier a team configures. Scale this example up to 100 million or 1 billion tokens a month, which isn’t unusual for a busy production agent fleet, and the gap between Sol and Grok 4.5 moves from a rounding error into a genuine line-item a finance team will ask about.
Benchmark Performance: Coding, Reasoning, and Agentic Tasks
Benchmarks are the messiest part of comparing three models that launched at different times. Gemini 3.1 Pro has months of independent testing behind it. Grok 4.5 has a growing but still partial public record. GPT-5.6 Sol has the thinnest independent benchmark trail of the three at the time of writing, which is itself worth noting: pricing pages went live fast, but matching third-party benchmark cards have lagged.
Coding and Agentic Benchmarks
On SWE-Bench Pro, a test built around real, unresolved software engineering tickets, Grok 4.5 scores 64.7%. That beats its immediate predecessor generation, GPT-5.5, which scored 58.4% on the same test, but it trails Claude Opus 4.6.8 (69.2%) and Claude Fable 5 (80.4%), according to figures compiled by llm-stats.com and cross-referenced by AIToolsRecap. Neither Gemini 3.1 Pro nor GPT-5.6 Sol had a matching SWE-Bench Pro score independently published at the time of writing, which limits a clean three-way call on this specific test.
On DeepSWE 1.0, another agentic coding benchmark, Grok 4.5 placed third at 62.0%, behind GPT-5.5 at 64.31% and Claude Fable 5 at 66.1%, but ahead of Claude Opus 4.8’s 55.75%. On Terminal-Bench 2.1, which measures tool-driven command-line tasks, Grok 4.5 posted 83.3%, and it scored 76.0% on the Artificial Analysis Coding Agent Index v1.1. None of these three benchmarks currently have a published Gemini 3.1 Pro or GPT-5.6 Sol score to compare against directly, so the strongest claim the data supports is that Grok 4.5 is a genuinely strong, efficient coding model relative to the previous generation, not that it definitively beats GPT-5.6 or Gemini 3.1 Pro on identical coding tests.
Reasoning and Knowledge Benchmarks
This is where Gemini 3.1 Pro’s head start shows. On GPQA, the graduate-level science reasoning test, Gemini 3.1 Pro scores 94.3% against Grok 4.5’s 93.0%, per the same llm-stats.com side-by-side. That’s a near-tie, well within the range where small prompt or sampling differences could flip the order. On ARC-AGI-2, a much harder abstract-reasoning benchmark, Gemini 3.1 Pro posts 77.1%, a figure that’s shown up consistently across multiple independent trackers going back to earlier Gemini 3.1 Pro comparisons on this site, including the Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Pro breakdown published earlier this year. Grok 4.5 does not yet have an independently published ARC-AGI-2 score.
Gemini 3.1 Pro also posts 92.6% on MMMLU (multimodal general knowledge), 85.9% on BrowseComp (agentic web research), and 99.3% on t2-bench, a tool-use and code-generation test, according to llm-stats.com. On the Artificial Analysis Intelligence Index, an aggregate score meant to summarize overall capability, GPT-5.6 Sol comes in around 58.9, per figures reported by Emergent’s GPT-5.6 alternatives guide and echoed on ModelGrep’s leaderboard, ahead of Grok 4.5’s 54 (good enough for #4 out of 168 tracked models). Gemini 3.1 Pro’s placement on that same index is murkier: OpenAI’s own internal comparison table put it at 46.5, a figure worth treating cautiously given the source, while a separate June 2026 Artificial Analysis snapshot placed an earlier Gemini 3.1 Pro reading closer to 57. The spread itself is a useful data point: aggregate “intelligence” indexes disagree with each other more than task-specific benchmarks like GPQA do.
Context Windows and Multimodal Capabilities
Context window is one of the clearest differentiators in this comparison. Grok 4.5 ships with a 500,000-token window, a figure repeated consistently across Gate.ai’s spec breakdown, CometAPI’s pricing guide, and several other independent trackers. GPT-5.6 Sol and Gemini 3.1 Pro both report roughly 1 million tokens, double Grok 4.5’s ceiling.
In practice, that gap matters less than it looks on paper for most coding and chat workloads, where prompts rarely approach even 500,000 tokens. It matters a lot more for two specific jobs: ingesting entire monorepos in a single call, and running retrieval-heavy research agents that stuff large batches of source documents into context rather than chunking them. For those workloads, GPT-5.6 Sol and Gemini 3.1 Pro’s larger windows mean fewer chunking passes and less orchestration overhead. Grok 4.5’s smaller window pushes teams toward retrieval-augmented approaches instead of raw context stuffing, which adds engineering complexity but doesn’t necessarily hurt output quality.
On multimodal capability, independent documentation is thinner for Grok 4.5 than for the other two; xAI’s public materials frame it primarily as a text, coding, and agentic-tool-use model rather than leading with vision or audio. Gemini 3.1 Pro, by contrast, carries Google’s broader multimodal stack, and its BrowseComp and t2-bench scores reflect a model built for tool-calling and live web research as much as raw chat. GPT-5.6 Sol’s multimodal feature set wasn’t fully detailed in the public pricing and launch materials available at the time of writing, beyond confirmation that the API is live across all three tiers.
Speed and Token Efficiency in Production
Raw benchmark scores tell you what a model can solve. Token efficiency tells you what it costs to get there, and this is arguably Grok 4.5’s strongest selling point. On SWE-Bench Pro tasks, Grok 4.5 uses an average of 15,954 output tokens to reach a solution, compared to 67,020 for Claude Opus 4.8 on the same benchmark, a 4.2 times difference reported by xAI and independently repeated by AIToolsRecap. That’s not a Grok-vs-GPT-5.6 or Grok-vs-Gemini comparison specifically, since neither of those two models had matching token-efficiency figures published at the time of writing, but it establishes Grok 4.5 as unusually economical relative to at least one other frontier-class model on identical tasks.
The efficiency angle compounds with the price gap rather than sitting separately from it. A model that costs less per output token and needs fewer output tokens to finish a task is cheaper twice over. For a team running thousands of agentic coding tasks a day, that combination can be the difference between a five-figure and a six-figure monthly API bill, even before accounting for GPT-5.6 Sol’s higher per-token rate on top of it.
None of this means Grok 4.5 finishes every task faster in wall-clock terms. Token efficiency measures how much a model writes, not how fast it writes it, and neither xAI, OpenAI, nor Google had published directly comparable latency figures for these three specific models at the time of writing. Teams that care about response latency for user-facing chat, rather than batch agentic jobs, should benchmark their own prompts rather than relying on token-count efficiency alone.
5 Real-World Use Cases, Tested Against Each Model
Specs and benchmarks matter, but the more useful question is which model fits which job. Here’s how the three stack up across five common production scenarios, based on the pricing, context, and benchmark data above.
- High-volume agentic coding pipelines. Teams running autonomous coding agents at scale (think CI-triggered bug fixes or automated refactor bots) are the clearest fit for Grok 4.5. Its combination of a $6 output price and a 4.2x lower token count on SWE-Bench Pro tasks (relative to Opus 4.8) directly targets the cost structure that makes agentic pipelines expensive: lots of output tokens, run constantly.
- Enterprise deployments already on SpaceX/Tesla-style private beta terms. Grok 4.5’s earliest confirmed enterprise usage was internal to SpaceX and Tesla during its private beta. Organizations already inside the xAI enterprise ecosystem, or evaluating it for the first time, have a real precedent for large-scale internal deployment to point to.
- Long-document research and retrieval-heavy agents. Gemini 3.1 Pro’s 1M-token window plus its 85.9% BrowseComp score and 99.3% t2-bench score make it the stronger default for research agents that need to ingest large document sets or chain multiple tool calls without losing context.
- Teams that need a budget tier without switching vendors. GPT-5.6’s Luna tier, at $1 input and $6 output per million tokens, is the only one of the three that lets a team downgrade to a cheaper model without leaving the same API and billing relationship. That’s valuable for organizations that want cost flexibility across a product line without managing three separate vendor contracts.
- Graduate-level reasoning and scientific QA. With GPQA scores of 94.3% and 93.0% respectively, Gemini 3.1 Pro and Grok 4.5 are both strong here, essentially tied within margin of error. For teams building tools for technical, scientific, or research-heavy domains, either is defensible, and the tiebreaker becomes price: Grok 4.5 at $6 output versus Gemini 3.1 Pro at $12 output for comparable reasoning quality on this specific test.
Notice what’s missing from this list: a clear-cut case where GPT-5.6 Sol is the only reasonable option. That’s not a knock on the model so much as a reflection of the current public data. Sol is priced like a premium flagship, but the independent benchmark record that would justify a $30 output price over Grok 4.5’s $6 or Gemini 3.1 Pro’s $12 hadn’t fully caught up at the time of writing. That’s not the same as saying Sol is a bad choice, only that the burden of proof for paying five times Grok 4.5’s output rate currently rests on a team’s own internal evaluation, not on a published third-party benchmark gap of a matching size.
A sixth pattern worth naming separately: teams migrating off an older model generation entirely. Anyone still budgeting around GPT-5.5 or Grok 4.3 pricing should treat this comparison as a prompt to re-run their cost model, since two of the three options here (Grok 4.5’s efficiency gains and GPT-5.6 Luna’s tier) didn’t exist when those older contracts were negotiated. Sticking with a legacy tier out of inertia is, in effect, choosing to overpay for capability the newer releases now offer more cheaply.
What Independent Reviewers and Industry Trackers Are Saying
Coverage of the Grok 4.5 launch has converged on a consistent theme: strong value, not outright dominance. AIToolsRecap’s July 10, 2026 roundup put it plainly, noting that “early coverage blurred” the nuance between Grok 4.5’s wins and losses, pointing out that the model beats Claude Opus 4.8 on DeepSWE 1.0 and Terminal-Bench 2.1, but loses to Opus 4.8 on SWE-Bench Pro. The same piece ranked Grok 4.5 #4 of 168 tracked models on the Artificial Analysis Intelligence Index, and specifically credited it with “the single best agentic tool-use result of any model on the board” at its price point.
Emergent’s comparison guide, published as a resource for teams evaluating GPT-5.6 alternatives, frames the three models by role rather than by a single winner: GPT-5.6 Sol as “the top generalist frontier model,” Grok 4.5 as “a high-end but cheaper coding/agentic model,” and Gemini 3.1 Pro as “the reasoning and research specialist” with a strong price-to-intelligence ratio at its $2 input rate. That framing lines up with the benchmark data throughout this article: no model wins across the board, and the right pick depends heavily on workload.
A broader model-comparison piece from Sintra.ai reinforces the same pattern using an earlier Intelligence Index snapshot: ChatGPT-family models “win for writing, coding, and structured reasoning,” Gemini “wins for research, reasoning benchmarks, and Google Workspace integration,” and Grok “wins for real-time social data and trend detection,” a framing built around the pre-4.5 generation of Grok that Grok 4.5’s stronger coding scores are now pushing back against.
It’s also worth reading the trackers’ silences as data in their own right. None of the independent sources reviewed for this comparison published a head-to-head verdict declaring GPT-5.6 Sol the outright best model of the three, despite it carrying the highest price tag. That’s a meaningful absence: when a premium-priced flagship doesn’t draw a clean “worth it” verdict from independent reviewers within weeks of launch, it tends to mean the benchmark case hasn’t been made yet, not that reviewers missed it.
Migration Guide: Moving Your Workflow Between These Three Models
Switching frontier models mid-project is routine now, but it’s not a drop-in swap. Pricing, context limits, and response formatting differ enough between Grok 4.5, GPT-5.6, and Gemini 3.1 Pro that a careless migration can quietly break output quality or blow past a budget. Here’s a practical sequence for moving a production workload from one to another.
Step-by-step migration checklist
- Audit current token usage and cost. Pull 30 days of input/output token counts from your current provider’s billing dashboard before touching anything. You need your real input-to-output ratio, not an assumed one, since that ratio determines which model is actually cheaper for your specific workload.
- Map context requirements against the 500K vs. 1M ceiling. If any production prompts approach Grok 4.5’s 500,000-token limit, flag them now. Moving to Grok 4.5 from a 1M-token model means those specific calls need chunking or retrieval logic before the migration, not after.
- Rewrite system prompts for model-specific behavior. Don’t assume a prompt tuned for GPT-5.6 will produce equivalent output on Gemini 3.1 Pro or Grok 4.5. Each model has different defaults around verbosity, tool-call formatting, and refusal behavior. Budget real time for prompt rework, not just an API key swap.
- Run a shadow evaluation before cutting over traffic. Send a copy of production traffic to the new model in parallel with the old one for at least a week, scoring outputs against your existing quality metrics before routing any real users to it.
- Re-benchmark cost with your actual token efficiency. List prices only tell part of the story. If Grok 4.5 needs meaningfully more or fewer tokens than your current model to reach the same output quality on your specific prompts, that changes the real cost comparison from what the per-token price sheet suggests.
- Set up multi-model fallback routing. Rather than a hard cutover, route a small percentage of traffic to the new model and scale gradually, with automatic fallback to the previous provider if error rates or latency spike.
On the technical side, the actual code change is usually the easiest part. Most teams built on an abstraction layer (LangChain, LiteLLM, or a thin internal wrapper) can swap the model identifier and endpoint with minimal disruption:
# Before: GPT-5.6 Sol
response = client.chat.completions.create(
model="gpt-5.6-sol",
messages=messages,
max_tokens=4096
)
# After: Grok 4.5, same call shape via a compatible SDK
response = client.chat.completions.create(
model="grok-4.5",
messages=messages,
max_tokens=4096
)
# Gemini 3.1 Pro via a unified wrapper
response = client.chat.completions.create(
model="gemini-3.1-pro",
messages=messages,
max_tokens=4096
)The code is the trivial part. The real migration work is steps one through six above: understanding your actual usage pattern, validating output quality, and rolling out gradually enough that a regression doesn’t reach every user at once.
Pros and Cons: Grok 4.5, GPT-5.6, and Gemini 3.1 Pro
Grok 4.5
- Pros: Cheapest output price of the three ($6/1M), strong token efficiency (4.2x fewer tokens than Opus 4.8 on SWE-Bench Pro), competitive GPQA score (93.0%), confirmed large-scale internal deployment at SpaceX and Tesla.
- Cons: Smallest context window (500K vs. ~1M for both rivals), thinnest multimodal documentation of the three, several key benchmarks (ARC-AGI-2, direct GPT-5.6 comparisons) not yet independently published.
GPT-5.6
- Pros: Only model of the three with a three-tier pricing ladder (Sol/Terra/Luna), giving teams a budget option (Luna, $1/$6) inside the same API relationship. Reported ~58.9 Artificial Analysis Intelligence Index score, ahead of Grok 4.5’s 54 on the same index.
- Cons: Most expensive flagship tier by far ($30/1M output on Sol, five times Grok 4.5’s rate), the thinnest independent benchmark record of the three at launch, exact release date and full spec sheet not clearly published by OpenAI at the time of writing.
Gemini 3.1 Pro
- Pros: Leads on GPQA (94.3%), strongest published ARC-AGI-2 score (77.1%), large 1M-token context window, deep independent benchmark record built up over months in market, strong BrowseComp and t2-bench scores for research and tool-use agents.
- Cons: Output pricing is exactly double Grok 4.5’s ($12 vs. $6), and reported Artificial Analysis Intelligence Index placement varies widely by tracker (46.5 to 57), making its aggregate “intelligence” ranking the least consistent of the three.
Which Model Should You Choose? 5 Use-Case Recommendations
Pulling the specs, pricing, and benchmarks together, here’s a direct recommendation by scenario rather than a single overall winner.
- Cost-sensitive agentic coding at scale: choose Grok 4.5. The $6 output price combined with independently reported token efficiency makes it the strongest fit for teams running large volumes of autonomous coding tasks.
- Deep research agents and long-document analysis: choose Gemini 3.1 Pro. The 1M-token context window and leading BrowseComp/t2-bench scores are built for exactly this kind of workload.
- Budget-constrained startups that still want flagship-adjacent quality: choose GPT-5.6 Luna. At $1 input and $6 output, it undercuts Grok 4.5 on input price while matching it on output price, without leaving the OpenAI ecosystem.
- Enterprises that need a single vendor across multiple quality tiers: choose GPT-5.6, using Sol for mission-critical tasks and Terra or Luna for lower-stakes workloads, all under one contract and one billing relationship.
- Scientific and technical QA products: choose either Gemini 3.1 Pro or Grok 4.5. Their GPQA scores (94.3% and 93.0%) are close enough that price should decide it, and Grok 4.5 is half the output cost.
Teams that can’t confidently place their workload into one of these five buckets should default to running a shadow evaluation across all three, using the migration checklist above, before committing to a single provider.
Where Claude Opus 4.8, DeepSeek, and the Rest of the Field Fit In
None of these three models exist in a vacuum, and the wider frontier field is worth a quick note. Claude Opus 4.8 remains a benchmark leader in its own right, posting an Artificial Analysis Intelligence Index score of 61.4 in one widely cited June 2026 snapshot, ahead of the GPT-5.5/Gemini 3.1 Pro/Grok 4.3 generation this current trio has now superseded. Claude Opus 4.8 also out-scores Grok 4.5 on SWE-Bench Pro (69.2% vs. 64.7%), though at a much higher token cost per task, exactly the tradeoff detailed in Tech Insider’s Grok 4.5 launch coverage.
On the open-weight side, GLM-5.2 leads open-source models with a 91.2% GPQA score, within striking distance of Grok 4.5 and Gemini 3.1 Pro despite being freely available to self-host, a comparison covered in more depth in Tech Insider’s DeepSeek V4 vs. GLM-5.2 vs. Qwen breakdown. Qwen3.7 Max, meanwhile, undercuts all three models in this comparison on price, reportedly the cheapest model in the top 10 frontier field at roughly $1.25 per million tokens. And for teams that need enormous context rather than raw reasoning power, Llama 4 Scout’s reported 10-million-token window dwarfs even Gemini 3.1 Pro and GPT-5.6 Sol’s 1M ceiling, though at a real cost in benchmark competitiveness.
The short version: Grok 4.5, GPT-5.6, and Gemini 3.1 Pro are competing hard with each other, but Claude Opus 4.8 still sets the coding benchmark ceiling, and the open-weight field is close enough behind that “proprietary vs. open” is now a real budget question, not just a philosophical one. For a closer look at how Claude’s current lineup stacks up against this same field, see Opus 4.8 vs. GPT-5.6 vs. Gemini 3.1 Pro: $18 Price Gap.
It’s worth remembering how fast this ranking can move. A year ago, the comparable trio on this site was Grok 4.3 vs. Gemini 3.1 Pro, where Grok trailed by a wide margin on reasoning benchmarks. Grok 4.5 closing that GPQA gap to less than a point and a half in a single generation is a bigger jump than the raw output-price cut, and it’s the reason this comparison reads so differently from the 4.3-era version of the same matchup.
The Verdict: What the Data Actually Says
There’s no single winner here, and any comparison that claims otherwise is oversimplifying. What the data supports is a clear division of labor. Grok 4.5 wins on price and token efficiency, backed by a $6 output rate and independently reported 4.2x token efficiency against Claude Opus 4.8 on identical coding tasks. Gemini 3.1 Pro wins on reasoning depth and context, backed by a 94.3% GPQA score, a 77.1% ARC-AGI-2 score, and a 1M-token window that Grok 4.5 can’t match. GPT-5.6 wins on flexibility, as the only model of the three offering three distinct price points inside one vendor relationship, from a $30 flagship down to a $6 budget tier.
If forced to pick a single default for a general-purpose engineering team weighing cost against capability, Grok 4.5 has the strongest case: it’s within a couple of points of Gemini 3.1 Pro on GPQA, it’s already proven at scale inside SpaceX and Tesla, and its output pricing undercuts GPT-5.6 Sol by 5 times. But “strongest case” isn’t “right for everyone.” Teams whose workloads lean on long-context research or need Gemini’s deeper multimodal and tool-use track record should stick with Gemini 3.1 Pro. Teams that need one vendor spanning multiple budget tiers, or that are already deep in the OpenAI ecosystem, have a legitimate reason to stay on GPT-5.6. The honest verdict is that this is a three-way split by workload, not a single leaderboard with one name at the top.
Frequently Asked Questions
Is Grok 4.5 cheaper than GPT-5.6?
Against GPT-5.6’s flagship Sol tier, yes, by a wide margin: $6 vs. $30 per million output tokens, a 5x gap. Against GPT-5.6’s cheapest tier, Luna, the two are priced identically on output ($6), though Luna actually undercuts Grok 4.5 slightly on input ($1 vs. $2).
Which model has the largest context window?
GPT-5.6 Sol and Gemini 3.1 Pro both report roughly 1 million tokens, double Grok 4.5’s 500,000-token window.
Does Grok 4.5 beat Gemini 3.1 Pro on benchmarks?
It’s close and mixed. Gemini 3.1 Pro leads on GPQA (94.3% vs. 93.0%) and has a published ARC-AGI-2 score (77.1%) that Grok 4.5 doesn’t yet have a public equivalent for. Grok 4.5’s strongest published results are on coding and agentic benchmarks like SWE-Bench Pro and Terminal-Bench 2.1.
What are the three GPT-5.6 pricing tiers?
Sol (flagship, $5 input/$30 output per million tokens), Terra (mid-tier, $2.50/$15), and Luna (budget, $1/$6), all per million tokens.
Is Gemini 3.1 Pro good for coding?
It’s strong on tool-use and code-generation benchmarks like t2-bench (99.3%) and LiveCodeBench Pro (96.2%), but it doesn’t have a directly published SWE-Bench Pro score to compare against Grok 4.5’s 64.7% at the time of writing.
Who is already using Grok 4.5 in production?
SpaceX and Tesla ran Grok 4.5 during its private beta period starting June 28, 2026, according to launch coverage. Public enterprise adoption figures beyond that hadn’t been disclosed at the time of writing.
How does this trio compare to Claude Opus 4.8?
Claude Opus 4.8 out-scores Grok 4.5 on SWE-Bench Pro (69.2% vs. 64.7%) and posts a higher Artificial Analysis Intelligence Index score (61.4 in one June 2026 snapshot), but it uses roughly 4.2 times more output tokens than Grok 4.5 to solve equivalent SWE-Bench Pro tasks, meaning it costs substantially more per task even before accounting for its own per-token price.
Which model should a solo developer or small startup pick?
For cost-sensitive teams, Grok 4.5 or GPT-5.6’s Luna tier are the strongest starting points, since both price output tokens at $6 per million. The tiebreaker is context window: Luna’s roughly 1M-token ceiling beats Grok 4.5’s 500K if a workload needs to ingest large documents or codebases in a single call.
Does Grok 4.5 support the same tooling as GPT-5.6 and Gemini 3.1 Pro?
Most teams access all three through OpenAI-compatible chat-completions style endpoints, which is why the migration code sample above uses the same call shape for all three. Model-specific behavior around tool calling, refusal handling, and verbosity still varies enough that prompts need re-tuning after a switch, even when the SDK call itself barely changes.
Will GPT-5.6, Grok 4.5, or Gemini 3.1 Pro get cheaper over time?
Every prior generation from all three labs has followed the same trajectory: launch price holds for a few months, then drops or gets replaced by a cheaper tier as the next model approaches. GPT-5.6’s Luna tier launching alongside the pricier Sol tier is itself an example of that pattern arriving earlier in the cycle than usual.
Related Coverage
- Grok 4.5 Debuts: Cheaper Than Claude Opus 4.6.8 [2026]
- Opus 4.8 vs GPT-5.6 vs Gemini 3.1 Pro: $18 Price Gap [2026]
- Sonnet 5 vs GPT-5.6 vs Gemini 3.1 Pro: 58% Cheaper [2026]
- Grok 4.3 vs Gemini 3.1 Pro: 4x Output Price Gap [2026]
- DeepSeek V4 vs GLM-5.2 vs Qwen: 10x Price Gap [2026]
- Best AI Model for Coding: DeepSeek Costs 20x Less [2026]
- Claude Mythos 5 Blackout: GPT-5.6 Capped at 20 Firms [2026]
