Grok 4.5 vs GPT-5.6 Sol: What the Benchmarks Say

Grok 4.5 and GPT-5.6 Sol landed a day apart, and the quote-tweet benchmark wars started before either lab's numbers could be independently reproduced. Grok 4.5 shipped July 8. Sol, along with its Terra and Luna siblings, went public July 9 after a limited government-and-enterprise preview. That timing matters, because almost every side-by-side chart circulating on X right now is built from vendor-reported scores, not third-party runs.

So here is the honest state of the comparison, judged on the five things that actually decide which model you would wire into a product: coding and agentic benchmarks, the Artificial Analysis intelligence index, price, throughput, and context. On three of the five the answer is clear. On the two that get the most airtime, the headline coding numbers, it is closer than either camp is presenting, and the reason is that nobody has reproduced them yet.

If you remember one line: throughput to Grok, ceiling to Sol

Grok 4.5 is the cheaper, faster model that xAI is positioning as "Opus-class" on coding. GPT-5.6 Sol is OpenAI's flagship reasoning tier, the one it pushed into health and life-sciences verticals at launch. If your workload is high-volume agentic loops where per-token cost and latency compound across dozens of tool calls, Grok 4.5's economics are hard to argue with. If you are bottlenecked on the hardest single-shot reasoning, the tasks where a wrong answer costs more than a slow one, Sol is the safer pick and, on the current public numbers, holds the intelligence-index lead.

Everything below is why, and where the numbers get soft.

The coding scores are the whole fight, and they overlap

Both labs led their launch materials with coding. xAI's Grok 4.5 announcement leans on SWE-Bench-style agentic coding and terminal tasks, framing the model as competitive with the top Anthropic and OpenAI tiers at a fraction of the price. OpenAI's Sol materials emphasize sustained multi-step reasoning and tool use across long agent runs.

Here is the problem with putting those two on the same bar chart today: they are measured against vendor-run harnesses, often with different scaffolding, different numbers of attempts, and different definitions of "solved." A model reported at the top of an internal SWE-Bench Pro run can slide several points once a neutral party like Artificial Analysis or the SWE-Bench maintainers re-runs it with a fixed agent. That gap is not misconduct. It is what always happens in the first week, and it happened with Grok 4.3 and GPT-5.5 too.

What I would actually watch, once independent numbers exist:

• SWE-Bench Pro and Verified under a common agent. This is the closest thing to a real-world "fix this repo" signal, and it is where the two are reportedly nearest.
• Terminal-Bench. Agentic command-line tasks punish models that reason well but call tools badly. A model can top SWE-Bench and still stumble here.
• DeepSWE-style long-horizon runs, where the failure mode is drift over dozens of steps, not a single wrong line.

My read: on the coding claim specifically, treat any July 9-through-14 chart showing a decisive winner with suspicion. The launch-week deltas are inside the noise that neutral re-runs routinely erase.

Where the intelligence index gives Sol the edge

The Artificial Analysis intelligence index rolls a spread of reasoning, math, and knowledge benchmarks into one composite. It is the number worth trusting most, because it is third-party and applied uniformly across models. On the pre-launch and early-launch composites, OpenAI's top reasoning tier has generally sat at or near the top of this index, and Sol is positioned as an increment on that line.

Grok 4.5's pitch is not that it beats Sol on raw index. It is that it gets close enough while costing and latency-ing far less. That is a defensible strategy, and it is the same one xAI ran with Grok 4.3. Whether the index gap is two points or eight will decide how persuasive it is, and that is exactly the figure that will move as more evals land.

Positioning at a glance

I have deliberately left exact per-token prices, context sizes, and benchmark scores out of that table. The precise figures on the pricing and model pages are the ones most likely to be updated in the days after launch, and I would rather point you to the source than freeze a number that moves. Check x.ai's model page and OpenAI's pricing page for the live values before you budget against them.

Price and throughput is the one-sided part

This is where the comparison stops being close. xAI's entire commercial thesis for the Grok 4-series has been aggressive pricing and fast inference, backed by the SpaceX-adjacent compute buildout that keeps its cost floor low. Grok 4.5 continues that. For an application making many calls per task, an agent that loops twenty times over tool calls, the cumulative bill and the wall-clock time are dominated by per-call cost and latency, not by whether the model is two index points smarter.

Sol is priced as a frontier reasoning model. You are paying for the ceiling. That is the right trade for a research assistant, a clinical-reasoning tool, or anything where one hard question per session justifies the spend. It is the wrong trade for a chatbot fielding thousands of routine requests, where Grok 4.5's economics win outright.

The useful mental model: Sol is priced like you ask it few, hard things. Grok 4.5 is priced like you ask it many, cheap things. Match that to your traffic shape before you match it to a leaderboard.

Context and the agentic-loop tax

Both models ship large context windows, and both labs quote figures well past the point where the raw number stops being the interesting question. What matters more for agent work is effective use of that context under load: retrieval accuracy deep into the window, and how gracefully the model handles a long tool-call history without losing the thread.

Neither lab has published the kind of independent long-context degradation curve that would settle this, and I would not trust a launch-day claim of "perfect recall at full context" from either side. If long-horizon agents are your use case, this is worth your own evaluation on your own prompts rather than a spec-sheet comparison.

The numbers to distrust until they are reproduced

Concretely, treat these as unsettled as of mid-July:

• Any coding benchmark delta under about five points. Different harnesses produce swings that large. Wait for a common-agent re-run.
• Vendor-selected benchmark subsets. Both launch posts pick the boards where they look best. That is normal marketing, not a full picture.
• Latency and cost claims stated without a workload. "Faster" and "cheaper" only mean something attached to a request pattern.
• Health and life-science claims for Sol. OpenAI's vertical push is real and interesting, but domain evals like the life-sciences task suites are new and thin on independent validation. Promising is not the same as proven.

How to actually choose this week

If you are shipping something now and cannot wait for the neutral leaderboards to stabilize: prototype on both, on your real prompts, and measure cost-per-completed-task rather than benchmark score. That single metric collapses intelligence, price, and latency into the number your finance team cares about, and it will tell you more than any July chart.

If you can wait a week or two, do. The Artificial Analysis composites and independent SWE-Bench re-runs will have settled by then, and the picture that emerges will be more durable than launch-week screenshots. My expectation, and it is a prediction, not a result: Sol keeps a modest intelligence-index lead, Grok 4.5 keeps a decisive cost-and-speed lead, and the coding gap narrows to near-parity once everyone runs the same harness. If that holds, the choice was never really about which model is smarter. It is about which axis your workload is bottlenecked on.