GPT-5.6 Terra charges $2.00 input, $12.00 output, $2.50 cache write and $0.20 cached read per million tokens [OURS]. The write is 1.25 times input — the same premium the Anthropic cards carry — which makes it the first OpenAI card in this batch with that shape. The gpt 5.6 terra price query is answered by that row. The card is served by OrcaRouter.
Where the premium sits
| Line | Rate per million | Against input |
| Input | $2.00 | 100% |
| Cache write | $2.50 | 125% |
| Cached read | $0.20 | 10% |
| Output | $12.00 | 600% |
The interesting number is not the 1.25× itself — Claude Opus 4.6 and Sonnet 5 both charge it — but that it appears here at all. A reader who learned “Anthropic charges a write premium, OpenAI does not” would be wrong on this card.
The other ratio is the one worth computing: writing a prefix costs 12.5 times what reading it costs. That is the widest write-to-read gap in this batch, and it makes the reuse question sharper than usual.
The break-even, worked
Writing one million tokens costs $0.50 more than sending them plainly. Reading that million back saves $1.80 per reuse. The premium is repaid in 0.28 reads, so the first reuse covers it.
Now the failure case. A 400,000-token document sent once, with no follow-up:
| Cost | |
| Uncached | $0.80 |
| Cached, written and never read | $1.00 |
Twenty-five percent more, on a request that never repeats [OURS arithmetic on the published rates]. The rule that follows is the same one that applies to the Anthropic cards, and it applies here whether or not the vendor publishes a write rate: do not write a cache entry speculatively.
Twelve rows, and where they sit
| Row | Score |
| terminalbench_v2_1 | 88.0 |
| τ²-Bench | 86.3 |
| Long-Context Recall | 83.0 |
| AA Coding | 76.7 |
| IFBench | 71.2 |
| TerminalBench Hard | 57.6 |
| SciCode | 55.0 |
| Humanity’s Last Exam | 42.9 |
| AA Intelligence | 42.1 |
| tau_banking | 40.2 |
| terminalbench_v4_0 | 35.4 |
| GPQA Diamond | 92.5 |
Everything here is from the independent evaluator [INDEPENDENT], which makes this card unusually comparable — no vendor rows to discount.
The profile is balanced. terminalbench_v2_1 at 88.0 is high; terminalbench_v4_0 at 35.4 puts it in the top group on the hardest generation without reaching the Fable level; τ²-Bench at 86.3 says tool use is solid. Nothing collapses, which is unusual in this batch — most cards have at least one row in the teens.
The weakest relative spot is tau_banking at 40.2, which is mid-pack on the domain variant.
Envelope and telemetry
A 1,050,000-token context and a 128,000-token output ceiling [OURS] — the context is slightly larger than the 1,048,576 standard elsewhere, which looks like a card rounding rather than a real difference.
Seven-day figures report 3,375 ms median to first token, a p95 at the 10,000 ms ceiling, an error rate of 0.32 percent, and 175.2 output tokens per second. Traffic was 8,504,458 tokens.
That is a healthy mid-volume route: fast generation, low errors, a start time in the middle of the pack.
What this card is for
Balanced agentic work. Every row between 35 and 88, no collapse anywhere, at $2.00 input. A team that wants one model for coding, tools and terminal work rather than three specialists.
Long-context reading with a repeated prefix. A million-token window with a 10 percent read is a good shape, provided the prefix is genuinely reused — the 1.25× write makes speculative caching a losing move.
Not the cheapest option for simple volume. At $12.00 per million output, the 6× ratio is steep. A chat workload with short answers pays 600 percent of input for every output token.
The takeaway
GPT-5.6 Terra prices at $2.00 input, $12.00 output, $2.50 cache write and $0.20 cached read per million tokens, with a 1,050,000-token context and twelve independent benchmark rows [OURS].
The 1.25× write premium is not vendor-specific — it is a property of how caching is billed, and it appears on an OpenAI card as readily as an Anthropic one. The 12.5× write-to-read gap is the number that decides whether caching pays.
Sourcing note: the four rates, the context and output ceilings, the twelve benchmark values with their `source` fields, the FAQ and section counts, and the seven-day p50, p95, error rate, output speed and token total are read from the OrcaRouter public model card for `openai/gpt-5.6-terra` on 2026-10-23 [OURS]. All rows are tagged artificialanalysis.ai [INDEPENDENT]. The break-even, the per-request comparison and the reading of the write premium as a billing shape rather than a vendor trait are our own. The impression figure is Search Console for the 28 days to 2026-10-21 [OURS]. No competing gateway or inference marketplace is named. All values checked 2026-10-23.