GPT-5.6 Terra vs Opus 4.8
OpenAI's cost-balanced reasoning model against Anthropic's frontier flagship — two vendors, two tokenizers, one price tier apart. On the only benchmark that scores both the same way, they finish within a point. The pick is about ecosystem and axis, not a capability gulf.
These two sit a price tier apart yet score within a point of each other on the one benchmark that runs both under a single methodology. On Artificial Analysis's cross-vendor suite, Opus 4.8 takes the overall Intelligence Index by a hair — 55.7 to GPT-5.6 Terra's 55 — while Terra leads the Coding Index 76.7 to 74.3 and the two are level on the Agentic Index (47.4 vs 47.2). For that near-tie Opus lists at $5 / $25 per million tokens against Terra's $2.50 / $15 — double the input price and two-thirds more on output. And they are not the same kind of model underneath: different vendors, different tokenizers (so a token count on one is not a token count on the other), knowledge cutoffs a few weeks apart (Terra February 16, 2026; Opus January 2026), and separate SDK and tool ecosystems. So the question is less who is smarter — on the comparable numbers, neither by much — and more which stack you are building in and whether your workload leans on the axis where each is marginally ahead. Coding and price point to Terra; a sliver of overall intelligence, fast mode, and Anthropic's frontier tooling point to Opus.
Benchmark head-to-head
Only one evaluator scores both of these models under a single published methodology: Artificial Analysis, whose Intelligence Index v4.1 runs the same nine evaluations (GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR) against every model on its board, then reports a Coding Index and an Agentic Index alongside. Those three are the rows below — GPT-5.6 Terra and Opus 4.8 as captured in our July 21, 2026 snapshot. We do not pair the vendors' own headline numbers, because Anthropic's System Card and OpenAI's model card are produced on different harnesses and are not directly comparable.
| Benchmark | GPT-5.6 Terra | Opus 4.8 | Margin |
|---|---|---|---|
| Intelligence Indexcomposite, 9 evals | 55 | 55.7 | Opus +0.7 |
| Coding Indexcoding | 76.7 | 74.3 | Terra +2.4 |
| Agentic Indextool use / agents | 47.4 | 47.2 | ≈ even (Terra +0.2) |
Artificial Analysis reports these for the max-effort configuration of each model, and the Intelligence Index is a rolled-up composite — a single number over nine evaluations — so it hides where within the suite each model pulls ahead or falls back. Read the three indices as a directional cross-vendor snapshot, not a task-by-task verdict.
Each vendor also publishes its own suite — Anthropic's Opus 4.8 System Card lists SWE-bench Verified at 88.6 and GPQA Diamond at 93.6, and OpenAI reports separate figures for Terra — but those runs use different harnesses, effort settings and prompt scaffolds, so setting them side by side would manufacture a precision that isn't there. We show only what a single evaluator produced for both. Our own LLM Index Score, built from the LiveBench task set, covers Opus 4.8 (79.67) but not GPT-5.6 Terra, which is absent from the snapshot we score — so there is no like-for-like LLM Index row to show here either.
The two models, side by side
| GPT-5.6 Terra | Opus 4.8 | |
|---|---|---|
| Vendor | OpenAI | Anthropic |
| Released | July 9, 2026 | May 28, 2026 |
| API model ID | gpt-5.6-terra | claude-opus-4-8 |
| Price (input / output) | $2.50 / $15 per MTok | $5 / $25 per MTok |
| Cached input | $0.25 per MTok | $0.50 per MTok |
| Context / max output | 1.05M in / 128K out | 1M in / 128K out |
| Knowledge cutoff | Feb 16, 2026 | Jan 2026 |
| Tokenizer | GPT (counts differ from Claude) | Claude (counts differ from GPT) |
| Reasoning control | Effort dial (none → max, default medium) | Adaptive thinking (configurable effort) |
| Fast mode | No — tune via effort dial instead | Yes ($10 / $50 per MTok) |
| Input modalities | Text, image | Text, image, file |
| Availability | OpenAI API — Chat Completions, Responses, Realtime, Batch | Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry |
| Self-host | No — proprietary, hosted only | No — proprietary, hosted only |
What the gap costs
Set the price gap against a steady load — say 10M input and 2M output tokens a day, the shape of a sustained agentic-coding workload:
| GPT-5.6 Terra | Opus 4.8 | |
|---|---|---|
| Per day (10M in / 2M out) | $55 | $100 |
| Per 30-day month | $1,650 | $3,000 |
| Vs Opus 4.8 each month | −$1,350 | — |
This assumes an identical token count on both, which is a simplification: GPT-5.6 Terra and Opus 4.8 use different tokenizers, so the same text splits into different numbers of tokens on each and the real gap moves a few percent either way. Both discount cache hits — Terra to $0.25 per 1M input, Opus to $0.50 — so a prefix-heavy workload shrinks both columns. One asymmetry to watch: Terra prompts above 272K input tokens move to a 2x input / 1.5x output tier, while Opus carries no long-context surcharge.
Which should you pick?
Reach for GPT-5.6 Terra when…
- Cost is the constraint: at $2.50 / $15 it is half the input price and two-thirds the output price of Opus 4.8, for an overall-intelligence gap of well under a point on the one shared benchmark.
- The work is coding-heavy — Terra leads the Artificial Analysis Coding Index 76.7 to 74.3, the single index where the margin points its way.
- You are already in the OpenAI ecosystem — the Responses API, Realtime, Batch, the OpenAI tool and function-calling stack — and want to stay on one vendor.
- You want a tunable reasoning-effort dial (none through max) to trade latency against depth on a per-request basis.
Step up to Opus 4.8 when…
- You want Anthropic's frontier line and its tooling — Claude Code, computer use, the Claude agent surface — and the ceiling matters more than the per-token bill.
- The job leans on where Opus's own suite is strongest — hard multi-file refactors, long-horizon agents — and you would rather pay for the top configuration than a cost-balanced tier.
- You need fast mode, the premium high-throughput tier that only the Opus line offers.
- A sliver of extra overall intelligence (55.7 vs 55 on the shared index) is worth double the price because the task's value dwarfs its token cost.
Before you commit
- The only cross-vendor benchmark that scores both — Artificial Analysis — puts them within a point on overall intelligence, but it is a rolled-up composite. A task that leans hard on one specific capability can diverge from what the index implies.
- The two use different tokenizers, so cost and context comparisons are approximate: '1M tokens' of your text is not the same amount of text on each model.
- Terra is an OpenAI model and Opus 4.8 an Anthropic one — SDKs, tool interfaces, data-handling terms and regional availability differ, and those often decide the pick more than a benchmark point.
- Neither ships open weights — both are proprietary and hosted-only, so neither is an option if you need to self-host, run offline, or pin a fully frozen version.
- Fast mode (premium high-throughput) exists on the Opus tier; Terra instead exposes a reasoning-effort dial, so the latency-versus-quality trade is tuned differently on each.
The full read
A within-family comparison asks which trim to buy; this one asks which vendor to bet on. GPT-5.6 Terra and Opus 4.8 are not two settings of one model — they are OpenAI's cost-balanced reasoning tier and Anthropic's frontier flagship, built by different labs on different tokenizers with different tool ecosystems. That makes the usual head-to-head instinct — line up each lab's own benchmark numbers — quietly unsafe, because those numbers come off different harnesses. The only honest capability read is a third party that runs both the same way.
That third party is Artificial Analysis, and its verdict is a near-draw. Opus 4.8 takes the overall Intelligence Index 55.7 to 55, a gap you would struggle to feel; Terra returns the favour on the Coding Index, 76.7 to 74.3; and on the Agentic Index the two are level, 47.4 against 47.2. Three indices, three results inside a few points of each other. Set beside that the price sheet — Opus at $5 / $25 per million tokens, Terra at $2.50 / $15 — and the shape of the decision is clear: you are being asked to pay double for a capability difference that, on the comparable evidence, is close to noise.
It is worth being explicit about why we do not simply quote SWE-bench or GPQA for both. Anthropic's Opus 4.8 System Card reports strong figures — SWE-bench Verified at 88.6, GPQA Diamond at 93.6 — and OpenAI publishes its own for Terra, but the two labs run these under different effort settings, scaffolds and harnesses. Placing them in one table would invent a decimal-point precision the underlying runs do not support. So the benchmark section shows the three Artificial Analysis indices and nothing else, and our own LiveBench-based LLM Index Score sits this comparison out because Terra is not in the snapshot we score.
The practical rule follows from the price-to-capability ratio rather than a winner. Route cost-sensitive and coding-leaning work — high-volume assistants, batch pipelines, everyday agent turns, and coding specifically, where Terra's one index lead lives — to GPT-5.6 Terra, and keep it if you are already standardised on the OpenAI stack. Escalate to Opus 4.8 when you want Anthropic's frontier ceiling and its agent tooling, when you need fast mode, or when the task's value so exceeds its token cost that a fractional intelligence edge is worth twice the price. For many teams the deciding factor is not on either scoreboard at all: it is which SDK, data terms and support relationship you already live inside.
Two things the indices are silent on. First, the tokenizer split is not cosmetic — because Terra counts tokens on a GPT tokenizer and Opus on a Claude one, the same document costs and fills context differently on each, so treat every cost and context figure here as close-but-approximate rather than exact. Second, none of these numbers speak to rate limits, regional availability, latency under sustained load, or data-handling terms — the factors that most often decide whether a model is usable for you in production. The scores narrow the field; your own traffic and constraints settle it. These are vendor-reported and third-party figures, not our hands-on runs, so read them as a well-sourced starting point and confirm on the workload you actually have.
Full index pages
FAQ
Is GPT-5.6 Terra or Opus 4.8 the better model?
On the one benchmark that scores both under a single methodology — Artificial Analysis — they finish within a point. Opus 4.8 edges the overall Intelligence Index 55.7 to 55, Terra leads the Coding Index 76.7 to 74.3, and the two are level on the Agentic Index. Since Opus costs about double, the honest answer is that neither is decisively better; it is a routing decision, not a ranking.
Which is cheaper, and by how much?
GPT-5.6 Terra, by a wide margin. It lists at $2.50 per million input tokens and $15 output, against Opus 4.8's $5 / $25 — half the input price and two-thirds the output. On a sustained 10M-input / 2M-output day that is roughly $55 versus $100, or about $1,650 versus $3,000 a month. Because the two use different tokenizers, treat that as close rather than exact.
For coding, should I pick GPT-5.6 Terra or Opus 4.8?
On the comparable data, Terra. It leads the Artificial Analysis Coding Index 76.7 to 74.3 and costs less, so it is the sensible default for coding-heavy work. Escalate to Opus 4.8 for the hardest multi-file, long-horizon jobs where its own System Card figures are strongest and where its agent tooling — Claude Code, computer use — earns its keep.
Why don't you compare their SWE-bench or GPQA scores directly?
Because Anthropic and OpenAI run those evaluations on different harnesses, effort settings and prompt scaffolds, so their numbers are not directly comparable. Pairing them would imply a precision the runs don't support. We only place figures side by side when a single evaluator produced both — here, Artificial Analysis's Intelligence, Coding and Agentic indices.
Do GPT-5.6 Terra and Opus 4.8 share a context window and cutoff?
They are close but not identical. Terra has a 1.05M-token context window and a February 16, 2026 knowledge cutoff; Opus 4.8 has a 1M window and a January 2026 cutoff. Both cap output near 128K tokens. And because they use different tokenizers, the same text consumes a different number of tokens on each, so '1M tokens' is not the same amount of text on the two models.
Spec and pricing for GPT-5.6 Terra from OpenAI's model card; for Opus 4.8 from the Claude Opus 4.8 System Card and Anthropic's published pricing. Cross-vendor benchmark figures from Artificial Analysis (snapshot July 21, 2026). Last verified 2026-07-24. Vendor-reported and third-party data, not our own hands-on runs.