Alex Getmanseñales tempranas de IA

GPT-5.6 Terra

OpenAI

Lanzado Actualizado
Ventana de contexto
1M
1.050.000 tokens
Salida máxima
128K
128.000 tokens
Entrada / Salida
texto, imagen → texto

Precios de API

USD / 1 millón de tokens

EntradaSalidaEntrada en caché
  • $2,00
openrouter
  • $12,00
openrouter
  • $0,20
openrouter

Cronología

  1. · Disponibilidad en API · enlace de referencia
Precio entrada/salida · USD / 1 millón de tokens
ModeloEnlaces de referenciaPrecio entrada/salida
gpt-5.6-terra
batch · long context
openai
Ruta de API no disponible
$2,00 (Entrada > 272.000 tokens) / $9,00 (Entrada > 272.000 tokens)
gpt-5.6-terra
batch · short context
openai
Ruta de API no disponible
$1,00 (Entrada ≤ 272.000 tokens) / $6,00 (Entrada ≤ 272.000 tokens)
gpt-5.6-terra
fast · long context
openai
Ruta de API no disponible
$8,00 (Entrada > 272.000 tokens) / $36,00 (Entrada > 272.000 tokens)
gpt-5.6-terra
fast · short context
openai
Ruta de API no disponible
$4,00 (Entrada ≤ 272.000 tokens) / $24,00 (Entrada ≤ 272.000 tokens)
gpt-5.6-terra
flex · long context
openai
Ruta de API no disponible
$2,00 (Entrada > 272.000 tokens) / $9,00 (Entrada > 272.000 tokens)
gpt-5.6-terra
flex · short context
openai
Ruta de API no disponible
$1,00 (Entrada ≤ 272.000 tokens) / $6,00 (Entrada ≤ 272.000 tokens)
gpt-5.6-terra
standard · long context
openai
Ruta de API no disponible
$4,00 (Entrada > 272.000 tokens) / $18,00 (Entrada > 272.000 tokens)
gpt-5.6-terra
standard · short context
openai
Ruta de API no disponible
$2,00 (Entrada ≤ 272.000 tokens) / $12,00 (Entrada ≤ 272.000 tokens)
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
  1. · Uso confirmado · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
    Fragmento
    After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data
  2. · Uso confirmado · OpenAI · official articles · How we detect frontier model misalignment
  3. · Entrada de catálogo · GitHub · openai/openai-node · code
    Fragmento
    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
  4. · Entrada de catálogo · GitHub · openai/codex · code
    Fragmento
    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

    Esta referencia en el código no confirma un identificador de modelo en uso.

  5. · Entrada de catálogo · GitHub · openai/codex · code
    Fragmento
    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_
  6. · Entrada de catálogo · GitHub · openai/openai-agents-python · code
    Fragmento
    "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-
  7. · Entrada de catálogo · GitHub · openai/openai-python · code
    Fragmento
    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",
  8. · Entrada de catálogo · GitHub · openai/openai-agents-js · code
    Fragmento
    .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
  9. · Publicaciones de Alex · Alex Getman
    Fragmento
    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.
  10. · Publicaciones de Alex · Alex Getman
    Fragmento
    🚨 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
  11. · Publicaciones de Alex · Alex Getman
    Fragmento
    ⚡️ 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.
  12. · Publicaciones de Alex · Alex Getman
    Fragmento
    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.
  13. · Publicaciones de Alex · Alex Getman
    Fragmento
    ⚡️ 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.
  14. · Publicaciones de Alex · Alex Getman
    Fragmento
    ⚡️ 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.
  15. · Publicaciones de Alex · Alex Getman
    Fragmento
    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

Señales 2

Especificaciones

GPT-5.6 Terra es un modelo de OpenAI, lanzado el 2026-07-09. La ventana de contexto es de 1.050.000 tokens. El máximo de salida es de 128.000 tokens. El precio por millón de tokens es de $2,00 para la entrada y $12,00 para la salida.

Ventana de contexto
1.050.000
Salida máxima
128.000
Entrada
texto · imagen
Salida
texto
Entrada · USD / 1 millón de tokens
$2,00
Salida · USD / 1 millón de tokens
$12,00
Entrada en caché · USD / 1 millón de tokens
$0,20

Preguntas

¿Cuál es la ventana de contexto de GPT-5.6 Terra?

La ventana de contexto es de 1.050.000 tokens. El máximo de salida es de 128.000 tokens.

¿Cuánto cuesta GPT-5.6 Terra?

El precio por millón de tokens es de $2,00 para la entrada y $12,00 para la salida. La entrada en caché cuesta $0,20 por millón.

¿Cuándo se lanzó GPT-5.6 Terra?

El 2026-07-09.