# GPT-5.6 Terra

GPT-5.6 Terra is an AI model released by OpenAI on Jun 26 2026. Tracked results include 54% on BullshitBench v2, 84.3% on Terminal-Bench 2.1 and 20.8% on Frontier-Bench v0.1.

## Facts

| Field | Value |
| --- | --- |
| Model | GPT-5.6 Terra |
| Developer | OpenAI |
| Release date | Friday, Jun 26 2026 |
| Licensing | Closed |

## API pricing

All rates in USD per 1,000,000 tokens, pay-as-you-go.

| Tier | Input | Cached input | Cache write | Output |
| --- | --- | --- | --- | --- |
| Up to 272K input tokens | $2.00 | $0.20 | $2.50 | $12.00 |
| Over 272K input tokens | $4.00 | $0.40 | $5.00 | $18.00 |

Verified August 18, 2026 against the first-party source: https://developers.openai.com/api/docs/pricing

## Tracked benchmark scores

| Benchmark | Score | Source | What it measures |
| --- | --- | --- | --- |
| BullshitBench v2 | 54% | [BullshitBench](https://github.com/petergpt/bullshit-benchmark) | Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. |
| Gray Swan IPI (k = 1) | 5.4% | Lab | Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. Gray Swan's indirect prompt injection benchmark measures how often such an attack succeeds when the attacker gets a single try. Lower is better. |
| Gray Swan IPI (k = 10) | 26% | Lab | Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. This variant gives the attacker 10 tries and counts an attack as successful if any of them works. Lower is better. |
| Gray Swan IPI (k = 15) | 30.4% | Lab | Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. This variant gives the attacker 15 tries and counts an attack as successful if any of them works. Lower is better. |
| Frontier-Bench v0.1 | 20.8% | Lab | A hard, ever-evolving set of real computer tasks — coding, system administration, data work, and more — that an AI agent has to complete on its own. Run by the Harbor / Laude Institute team as the successor to Terminal-Bench (v0.1 is the first release of the task set). The score is the share of tasks solved. Higher is better. |
| Terminal-Bench 4.0 | 21.52% | [Terminal-Bench](https://github.com/harbor-framework/terminal-bench), retrieved 2026-08-30 | Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. |
| Terminal-Bench 2.1 | 84.3% | Lab | Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. |
| BrowseComp | 87.5% | [BenchLM](https://benchlm.ai), retrieved 2026-08-24 | Can the AI browse the web and track down hard-to-find answers? Higher is better. |
| ARC-AGI-2 | 83.9% | [BenchLM](https://benchlm.ai), retrieved 2026-08-24 | Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. |
| threejseval | 1438 | [threejseval](https://threejseval.com/ranking) | Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better. |

## Questions and answers

### When was GPT-5.6 Terra released?

GPT-5.6 Terra was released by OpenAI on Friday, Jun 26 2026.

### Who made GPT-5.6 Terra?

GPT-5.6 Terra was built by OpenAI. Creators of ChatGPT and the GPT series of models. Pioneered large-scale language model research.

### How much does GPT-5.6 Terra cost?

GPT-5.6 Terra costs $2.00 per million input tokens and $12.00 per million output tokens through the OpenAI API. Cached input is $0.20 per million tokens. Those are the rates for the “Up to 272K input tokens” tier; 1 other pricing tier is published for this model. Rates are pay-as-you-go API prices verified against OpenAI's published pricing on August 18, 2026.

### What benchmark scores did GPT-5.6 Terra get?

GPT-5.6 Terra reports 10 tracked benchmark scores — BullshitBench v2: 54%; Gray Swan IPI (k = 1): 5.4%; Gray Swan IPI (k = 10): 26%; Gray Swan IPI (k = 15): 30.4%; Frontier-Bench v0.1: 20.8%; Terminal-Bench 4.0: 21.52%; Terminal-Bench 2.1: 84.3%; BrowseComp: 87.5%; ARC-AGI-2: 83.9%; threejseval: 1438. Tracked scores may come from lab reports or independent benchmarks; source details accompany the benchmark data.

### Is GPT-5.6 Terra open source?

No. GPT-5.6 Terra is a closed model. The weights are not published — it is available only through the provider's own API, apps, or partner platforms.

### What came before and after GPT-5.6 Terra?

OpenAI's previous tracked release was GPT-5.6 Sol on Jun 26 2026. It was followed by GPT-5.6 Luna on Jun 26 2026.


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Canonical page: https://aireleasetracker.com/model/openai/gpt-5.6-terra
Full dataset: https://aireleasetracker.com/llms-full.txt · JSON: https://aireleasetracker.com/models.json
Source: AI Release Tracker (https://aireleasetracker.com). Most benchmark scores come from lab launch material; gathered results identify the leaderboard that published them.