GPT-5.6 Terra

Name: GPT-5.6 Terra
Price: 2.0 USD
Author: OpenAI

OpenAI

Inilabas 2026-07-09Na-update 14 Nov 2026, 14:39 UTC

Context window: 1M
Pinakamahabang output: 128K
Input / Output: teksto, larawan → teksto

Mga presyo ng API

USD / 1 milyong token

Input	Output	Naka-cache na input
$2.00 openrouter	$12.00 openrouter	$0.20 openrouter

Kuwentahin ang gastos ng paggamit

Takdang panahon

2026-07-09 · Magagamit sa API · link sa ebidensiya

Presyo ng input / output · USD / 1 milyong token

Modelo	Mga link sa ebidensiya	Presyo ng input / output
`gpt-5.6-terra` batch · long context	openai Hindi available ang ruta ng API	$2.00 (Input > 272,000 token) / $9.00 (Input > 272,000 token)
`gpt-5.6-terra` batch · short context	openai Hindi available ang ruta ng API	$1.00 (Input ≤ 272,000 token) / $6.00 (Input ≤ 272,000 token)
`gpt-5.6-terra` fast · long context	openai Hindi available ang ruta ng API	$8.00 (Input > 272,000 token) / $36.00 (Input > 272,000 token)
`gpt-5.6-terra` fast · short context	openai Hindi available ang ruta ng API	$4.00 (Input ≤ 272,000 token) / $24.00 (Input ≤ 272,000 token)
`gpt-5.6-terra` flex · long context	openai Hindi available ang ruta ng API	$2.00 (Input > 272,000 token) / $9.00 (Input > 272,000 token)
`gpt-5.6-terra` flex · short context	openai Hindi available ang ruta ng API	$1.00 (Input ≤ 272,000 token) / $6.00 (Input ≤ 272,000 token)
`gpt-5.6-terra` standard · long context	openai Hindi available ang ruta ng API	$4.00 (Input > 272,000 token) / $18.00 (Input > 272,000 token)
`gpt-5.6-terra` standard · short context	openai Hindi available ang ruta ng API	$2.00 (Input ≤ 272,000 token) / $12.00 (Input ≤ 272,000 token)
`openai/gpt-5.6-terra-pro` standard	openrouter	$2.00 / $12.00
`openai/gpt-5.6-terra-pro:batch` batch	openrouter	$1.00 / $6.00

2026-11-14T08:45:27.686Z · Kumpirmadong paggamit · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
Sipi
After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data
2026-11-14T08:23:25.536Z · Kumpirmadong paggamit · OpenAI · official articles · How we detect frontier model misalignment
2026-11-12T18:45:20.401Z · Pagkakalista sa katalogo · GitHub · openai/openai-node · code
Sipi
ttps://developers.openai.com/api/docs/deprecations for details and * recommended replacements. */ model: | 'gpt-6-astra' | 'gpt-6.1-sol' | 'gpt-6.1-terra' | 'gpt-6-sol' | 'gpt-6-luna' | 'gpt-5.6-sol' | 'gpt-5.6-terra' | 'gpt-5.6-luna' | 'gpt-5.5' | 'gpt-5.5-2026-04-23' | 'gpt-5.4' | 'gpt-5.4-mini' | 'gpt-5.4-nano' | 'gpt-5.4-mini-2026-03-17' | 'gpt-5.4-nano-2026-03-17' | 'gpt-5.3-chat-latest' | 'gpt-5.2' | 'gpt-5.2-2025-12-11' | 'gpt-5.2-chat-latest' | 'gpt-5.2-pro' | 'gpt-5.2-pro-2025-12-11' | 'gpt-5.1' | 'gpt-5.1-2025-11-13' | 'gpt-5.1-codex' | 'gpt-5.1-mini' | 'gpt-5.1-chat-latest' | 'gpt-5' | 'gpt-5-mini' | 'gpt-5-nano' | 'gpt-5-2025-08-07' | 'gpt-5-mini-2025-08-07' | 'gpt-5-nano-2025-08-07' | 'gpt-5-chat-latest' | 'gpt-4.1' | 'gp
2026-11-10T18:00:44.369Z · Pagkakalista sa katalogo · GitHub · openai/codex · code
Sipi
cfg = ConfigToml { agents: Some(AgentsToml { enabled: Some(false), max_depth: Some(2), default_subagent_model: Some("gpt-5.6-terra".to_string()), default_subagent_reasoning_effort: Some(ReasoningEffort::High), interrupt_message: Some(false), ..Default::default() }), ..Default::default() }; let config = Config::load_from_base_config_with_overrides( cfg, ConfigOverrides::d
Hindi pinatutunayan ng pagbanggit na ito sa code ang isang ginagamit na identifier ng modelo.
2026-11-10T18:00:44.369Z · Pagkakalista sa katalogo · GitHub · openai/codex · code
Sipi
se; use codex_protocol::openai_models::WebSearchToolType; use super::BedrockEndpoint; const GPT_5_BEDROCK_CONTEXT_WINDOW: i64 = 272_000; const GPT_5_6_SOL_OPENAI_MODEL_ID: &str = "gpt-5.6-sol"; const GPT_5_6_TERRA_OPENAI_MODEL_ID: &str = "gpt-5.6-terra"; const GPT_5_6_LUNA_OPENAI_MODEL_ID: &str = "gpt-5.6-luna"; const GPT_6_1_SOL_OPENAI_MODEL_ID: &str = "gpt-6.1-sol"; const GPT_6_1_TERRA_OPENAI_MODEL_ID: &str = "gpt-6.1-terra"; const GPT_6_SOL_OPENAI_MODEL_ID: &str = "gpt-6-sol"; const GPT_6_LUNA_OPENAI_MODEL_ID: &str = "gpt-6-luna"; const GPT_6_ASTRA_OPENAI_MODEL_ID: &str = "gpt-6-astra"; const GPT_5_5_OPENAI_MODEL_ID: &str = "gpt-5.5"; pub(crate) fn static_model_catalog() -> ModelsResponse { let catalog = ModelsResponse { models: vec![ bedrock_model( bundled_openai_model(GPT_6_1_SOL_OPENAI_MODEL_ID), AMAZON_BEDROCK_GPT_6_
2026-11-10T17:59:50.472Z · Pagkakalista sa katalogo · GitHub · openai/openai-agents-python · code
Sipi
"gpt-5.4-pro", "gpt-5.4-pro-2026-03-05", "gpt-5.5", "gpt-5.5-2026-04-23", "gpt-5.5-pro", "gpt-5.5-pro-2026-04-23", "gpt-5.6", "gpt-5.6-sol", "gpt-5.6-terra", "gpt-5.6-luna", "gpt-4.1", "gpt-4.1-2025-04-14", "gpt-4.1-mini", "gpt-4.1-mini-2025-04-14", "gpt-4.1-nano", "gpt-4.1-nano-2025-04-14", ), 1_047_576, ), **_model_context_windows( ( "gpt-5", "gpt-5-2025-08-07", "gpt-5-codex", "gpt-5-mini", "gpt-5-mini-2025-08-07", "gpt-5-nano", "gpt-5-nano-2025-08-07", "gpt-5-pro", "gpt-5-pro-2025-10-06", "gpt-5.1", "gpt-5.1-2025-11-13", "gpt-
2026-11-10T17:58:16.002Z · Pagkakalista sa katalogo · GitHub · openai/openai-python · code
Sipi
gth of 512 characters. """ model: Union[ Literal[ "gpt-6-astra", "gpt-6.1-sol", "gpt-6.1-terra", "gpt-6-sol", "gpt-6-luna", "gpt-5.6-sol", "gpt-5.6-terra", "gpt-5.6-luna", "gpt-5.5", "gpt-5.5-2026-04-23", "gpt-5.4", "gpt-5.4-mini", "gpt-5.4-nano", "gpt-5.4-mini-2026-03-17", "gpt-5.4-nano-2026-03-17", "gpt-5.3-chat-latest", "gpt-5.2", "gpt-5.2-2025-12-11", "gpt-5.2-chat-latest", "gpt-5.2-pro", "gpt-5.2-pro-2025-12-11", "gpt-5.1", "gpt-5.1-2025-11-13", "gpt-5.1-codex", "gpt-5.1-mini", "gpt-5.1-chat-latest", "gpt-5", "gpt-5-mini",
2026-11-10T17:58:10.980Z · Pagkakalista sa katalogo · GitHub · openai/openai-agents-js · code
Sipi
.replace(/-\d{4}-\d{2}-\d{2}$/u, ''); return [...new Set([normalized, withoutPinnedDate])]; } const SANDBOX_CONTEXT_WINDOWS_1M = new Set([ 'gpt-6-astra', 'gpt-6-sol', 'gpt-6-luna', 'gpt-6.1-sol', 'gpt-5.6', 'gpt-5.6-sol', 'gpt-5.6-terra', 'gpt-5.6-luna', 'gpt-5.4', 'gpt-5.4-2026-03-05', 'gpt-5.4-pro', 'gpt-5.4-pro-2026-03-05', 'gpt-4.1', 'gpt-4.1-2025-04-14', 'gpt-4.1-mini', 'gpt-4.1-mini-2025-04-14', 'gpt-4.1-nano', 'gpt-4.1-nano-2025-04-14', ]); const SANDBOX_CONTEXT_WINDOWS_400K = new Set([ 'gpt-5', 'gpt-5-codex', 'gpt-5-mini', 'gpt-5-nano', 'gpt-5-pro', 'gpt-5.1', 'gpt-5.1-codex', 'gpt-5.1-codex-max', 'gpt-5.1-codex-mini', 'gpt-5.2', 'gpt-5.2-codex', 'gpt-5.2-codex-cyber', 'gpt-5.2-pro', 'gpt-5.3-codex', 'gpt-5.3-codex-spark', 'gpt-5.4-mini', 'gpt-5.4-nano', ]); const SANDBOX_CONTEXT_WINDOWS_200K = new
2026-09-16T15:54:37.278Z · Mga post ni Alex · Alex Getman
Sipi
Jev is out, a new AI model from startup TypeSafe AI It is built for fast decision making inside apps. You give it context and possible actions, and it picks the best option with probability and confidence. TypeSafe says Jev reaches GPT 5.6 Terra level on these tasks, responds in 70 to 500ms, and costs $0.042 per 1M input tokens. Output is free. It can route support tickets, score risk, and check AI agent actions.
2026-09-02T15:31:23.681Z · Mga post ni Alex · Alex Getman
Sipi
🚨 Google released Gemini 3.8 Flash • still $0.75 per 1M input tokens • at the same price, it is noticeably stronger than Gemini 3.7 Flash across almost the entire table • especially big gains in autonomous computer use and complex scientific tasks • beats GPT 5.6 Terra, a model in the same class, on almost every benchmark
2026-08-18T05:00:00.000Z · Mga post ni Alex · Alex Getman
Sipi
⚡️ Qwen 3.8 27B beat GPT 5.6 Terra This happened in the AA Agentic Index, a benchmark that tests how well models handle agentic tasks. And you can run Qwen 3.8 27B locally on a single RTX 3090.
2026-08-14T05:00:00.000Z · Mga post ni Alex · Alex Getman
Sipi
Very few people know this, but Muse Spark 1.2 might be the best value model on the market right now. Its intelligence is around GPT-5.6 Terra. But if you use Muse Code and agree to code analysis through the Contributor tier, the price drops 10–20x. So you basically get Terra-level intelligence at Luna-level cost.
2026-08-03T08:01:30.947Z · Mga post ni Alex · Alex Getman
Sipi
⚡️ Alibaba released Qwen 3.8 Max The new 2.4T parameter flagship benchmarks in the same league as GPT-5.6 Sol, Claude Opus 4.8 and Fable 5. API pricing is $2/M input and $6/M output, roughly half the cost of GPT-5.6 Terra and Claude Sonnet. Now it’s up to users to see whether Qwen can beat DeepSeek-V4-Flash and GPT-5.6 Luna on price to performance.
2026-07-16T17:56:24.898Z · Mga post ni Alex · Alex Getman
Sipi
⚡️ RELEASE: KIMI K3 Moonshot just dropped its new flagship: 2.8T parameters, a 1M-token context window, native vision, and a focus on large projects, coding, games, and 3D. Full weights are expected in the next few days. API: $3 / $15 per million input/output tokens. Pricing-wise, that puts it around Sonnet 5 or GPT-5.6 Terra, while Moonshot’s own benchmarks have it taking shots at Opus 4.8 and Sol. So basically: Opus-class performance at Sonnet pricing. You can already try it for free on the Kimi website or through Kimi Code CLI.
2026-07-10T16:34:15.332Z · Mga post ni Alex · Alex Getman
Sipi
Grok 4.5: a new competitor to GPT and Claude Posting this a little late, but I prepared something interesting. Grok 4.5 is temporarily available to test for free in Grok Build and Cursor. The model runs at around 80 tokens per second, supports a context window of up to 500,000 tokens, and costs $2 per million input tokens and $6 per million output tokens. According to Musk, Grok 4.5 was trained from scratch on SpaceXAI’s own foundation and is not built on top of Kimi like Composer. In terms of quality, Grok 4.5 comes close to GPT-5.6 Terra, while its pricing is closer to Luna. Among Anthropic models, it sits between Haiku and Sonnet, making it especially attractive in terms of price-to-pe

Mga senyal 2

20:2509-19

OpenAIPagkatukoy

Natukoy ang GPT-5.6 Terra sa Artificial Analysis

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22:0909-11

OpenAIPagkatukoy

Natukoy ang GPT-5.6 Terra sa Arena

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Mga espesipikasyon

Ang GPT-5.6 Terra ay isang modelo mula sa OpenAI, na inilabas noong 2026-07-09. Ang context window ay 1,050,000 token. Ang pinakamahabang output ay 128,000 token. Ang presyo ay $2.00 sa bawat isang milyong input token at $12.00 sa bawat isang milyong output token.

Context window: 1,050,000
Pinakamahabang output: 128,000
Input: teksto · larawan
Output: teksto
Input · USD / 1 milyong token: $2.00
Output · USD / 1 milyong token: $12.00
Naka-cache na input · USD / 1 milyong token: $0.20

Mga tanong

Gaano kalaki ang context window ng GPT-5.6 Terra?

Ang context window ay 1,050,000 token. Ang pinakamahabang output ay 128,000 token.

Magkano ang GPT-5.6 Terra?

Ang presyo ay $2.00 sa bawat isang milyong input token at $12.00 sa bawat isang milyong output token. Ang naka-cache na input ay $0.20 sa bawat isang milyong token.

Kailan inilabas ang GPT-5.6 Terra?

Noong 2026-07-09.