Grok 4.5 at $2/$6: We Tested the Cheap-Frontier Claim Against Opus 5 and GPT-5.6 Sol
By Promptster Team · 2026-08-08
xAI shipped Grok 4.5 in July at $2 per million input tokens and $6 per million output. That's aggressive against the flagships it invites comparison with:
| Model | Input | Output |
|---|---|---|
| Grok 4.5 | $2.00 | $6.00 |
| Claude Opus 5 | $5.00 | $25.00 |
| GPT-5.6 Sol | $5.00 | $30.00 |
"Frontier performance at a fraction of the price" is the oldest claim in this market, and it's usually doing a lot of work with the word "performance." So we ran Grok 4.5 through the same battery we used on Opus 5 vs Fable 5 — three deliberately hard tasks, each graded by script against a known answer.
It passed everything the flagships passed
| Task | Grok 4.5 | Opus 5 | GPT-5.6 Sol |
|---|---|---|---|
| Interval-merge bug (only fires on a contained interval) | PASS | PASS | PASS |
| Letter counting ("r" in strawberry raspberry refrigerator = 10) | PASS | PASS | PASS |
| 4 sentences × exactly 5 words, never the word "rain" | PASS | PASS | PASS |
These are the tasks we built specifically because our easier battery had stopped separating anything. The interval bug is invisible on typical input. The letter-counting task has been embarrassing frontier models for two years. The third requires holding two independent constraints while generating.
Grok 4.5 handled all three, same as the two models costing several times more.
The saving is much larger than the price list suggests
| Model | Cost (3 tasks) | Output tokens | Latency |
|---|---|---|---|
| Grok 4.5 | $0.003272 | 286 | 21,414 ms |
| GPT-5.6 Sol | $0.022060 | 707 | 23,864 ms |
| Claude Opus 5 | $0.037225 | 1,440 | 22,479 ms |
Compare output rates and you'd expect Opus 5 to cost about 4.2× what Grok does ($25 vs $6 per million). We measured 11.4×.
The gap is verbosity. Grok used 286 output tokens to Opus 5's 1,440 — five times fewer — for verdicts graded identically. You pay per token, so terseness compounds directly into the bill.
This is the same mechanism we found in the GPT-5.6 tiers, running the other direction. There, Luna's 5× list discount shrank to a real 3.3× because the budget tier was more verbose. Here, Grok's 4.2× list discount grows to 11.4× because it's dramatically less verbose.
Neither number was on the pricing page. If you size a budget from list rates alone, you will be wrong in whichever direction your model's verbosity happens to run.
Grok was also, narrowly, the fastest of the three.
It also won the constraint test
Five runs each of "three sentences about the ocean, no letter e":
| Model | Clean runs | Letter-"e" per trial |
|---|---|---|
| Grok 4.5 | 5/5 | 0, 0, 0, 0, 0 |
| Opus 5 | 4/5 | 0, 0, 0, 1, 0 |
| GPT-5.6 Sol | 4/5 | 1, 0, 0, 0, 0 |
A clean sweep — but we'd rather you didn't over-read it, for a reason our own data makes plain.
We have now run this exact test against Opus 5 three separate times: 1/5, then 3/5, then 4/5. Eight clean runs out of fifteen, on identical prompts. A five-trial sample cannot separate a 50% model from a 90% one, so Grok's 5/5 is encouraging rather than a ranking. What it does establish is that Grok 4.5 belongs in the same conversation on constraint adherence — which is not something a budget model usually survives.
What we're not claiming
Every task here is single-turn and finishes in seconds. Grok 4.5's published benchmark position is strongest on agentic tool use — long chains of calls where per-step differences accumulate. Nothing in this battery measures that, and a model that matches the frontier on one-shot tasks may still diverge over a fifty-step agent run.
We also aren't testing the things that often decide procurement: rate limits under load, regional availability, data-handling terms. Those matter, and they aren't a benchmark.
The practical read
For one-shot production work, the cheap-frontier claim holds. On everything we could construct that grades objectively, Grok 4.5 matched two models costing 6.7× and 11.4× more, and did it slightly faster.
Measure your own token counts before budgeting. The list price told us 4.2×. The invoice said 11.4×. That difference is not a rounding error on a high-volume workload — at a million calls shaped like our debug task, it's roughly $3,300 against $37,000.
Validate hard constraints anyway. Grok went 5/5 here, but both flagships missed one run in five, and Opus 5 has ranged from 1/5 to 4/5 across our runs. No model on this list is dependable enough on a checkable rule to skip checking it.
Run it yourself
No account, no API key:
- The letter-counting task — the answer is 10
- The constraint test — Grok's clean sweep
To compare these models side by side on your own prompts and see the token counts that drive the bill, that's Promptster.
Run 2026-08-07 against grok-4.5, claude-opus-5, and gpt-5.6-sol via the Promptster API, max_tokens: 4000. Costs computed from list pricing: Grok 4.5 $2/$6, Opus 5 $5/$25, Sol $5/$30 per million input/output tokens. The three graded tasks were run once each — enough for pass/fail against a deterministic answer, not enough to characterise latency or token variance, so treat timings as indicative. The constraint task was run five times per model; as discussed, that is not enough to rank models on it.