Three frontier models launched within 48 hours in September 2026: Grok 4.7 on the 21st, then Claude Opus 5.5 and GPT-6 Sol on the 22nd. They are close enough on price and capability that the usual shortcuts do not work. Here is what actually separates them, and how to pick without guessing.
The specifications, side by side
| GPT-6 Sol | Claude Opus 5.5 | Grok 4.7 | |
|---|---|---|---|
| Input, per million tokens | $2 | $4 | $2 |
| Output, per million tokens | $10 | $20 | $6 |
| Context window | 1,050,000 | 1,000,000 | 500,000 |
| Max output | 128,000 | 128,000 | 450,000 |
| Inputs | Text, image, file | Text, image, file | Text, image, file |
Grok 4.7's pricing carries a threshold: under 200,000 prompt tokens it bills $2 and $6, but at or above that the entire request bills $4 and $12. Not just the excess, the whole call.
What each vendor claims
Claude Opus 5.5. Anthropic reports 89.9% on SWE-bench Pro and 66.4% on Terminal-Bench 4.0, topping its own table, plus a practical test porting a legacy HAProxy codebase from C to Rust in 9.5 hours against 12 for Claude Fable 5.1.
GPT-6 Sol. OpenAI positions it for complex work including coding, built with the same methods as the more expensive GPT-6 Astra and aimed at the tier Astra cannot serve on price.
Grok 4.7. xAI reports improvement over Grok 4.6 across every benchmark in its own comparison, leading on EEBench and the Harvey legal agent benchmark. It openly trails Claude Fable 5.1 on coding.
All three sets of numbers come from the companies selling the models, measured on tasks they selected. The directions are probably right. The margins are worth much less than a morning of your own testing.
Where each one has a genuine edge
Grok 4.7 for long-form generation
The 450,000-token output ceiling is three and a half times what either rival will write in one response. For bulk translation, large structured generation, or anything where chunking would cost you coherence at every seam, that is not a spec-sheet curiosity, it is the whole reason to pick it. It is also the cheapest of the three on output.
GPT-6 Sol for breadth at a middling price
The largest context window of the three, no pricing cliff, and half Opus's rate. If you want one default model and do not have a strong reason to specialise, this is the least likely to be wrong.
Claude Opus 5.5 for hard agentic work
The strongest published coding and terminal numbers of the three, and the one to reach for when errors compound across many steps. It is also the one whose price you should justify per task rather than adopt as a default.
The honest answer: test them together
Three models within a factor of three on price, all accepting the same inputs, each leading on a different published benchmark. That is not a situation a comparison table resolves. What resolves it is your own prompts.
The variables that actually decide it are the unglamorous ones: which model follows your formatting instructions without being reminded, which handles your domain vocabulary, which fails in ways you can detect rather than plausible-sounding ways you cannot. None of that appears on a leaderboard, and all of it shows up within about ten prompts.
This is what Deepest does. Send one prompt to all three, read the three answers side by side, and the question answers itself in a minute rather than an afternoon of copying between tabs. When they agree, you can trust the answer more than any single model's confidence. When they disagree, the disagreement is the most useful thing on the screen, and it is invisible if you only ever asked one.
Frequently Asked Questions
Which is best for coding?
On published benchmarks, Claude Opus 5.5 leads, with 89.9% on SWE-bench Pro. xAI concedes Grok 4.7 trails Claude on coding. That said, SWE-bench measures resolving issues in specific public repositories, which may or may not resemble your codebase. Test all three on your own code before committing.
Which is cheapest?
Grok 4.7, on both input and output, provided you stay under 200,000 prompt tokens per request. Cross that line and the whole request bills at double, which can make it more expensive than GPT-6 Sol for long-context work.
Do I need the biggest context window?
Usually not. Sending a million tokens costs a million tokens on every turn, and models attend unevenly across very long prompts. Retrieval that finds the relevant 10,000 tokens is normally cheaper and produces better answers. The large window matters when you genuinely cannot predict what is relevant in advance.
Can I use all three at once?
Yes, and for anything important it is the approach we would recommend. Deepest sends one prompt to several models and shows the answers together, with an optional synthesis that reconciles them. Multiplying your cost by three on a decision that matters is usually a good trade against being confidently wrong.
