Alex Getmansemnale timpurii despre IA

GPT-5.6 Terra

OpenAI

Lansat Actualizat
Fereastră de context
1M
1.050.000 de tokeni
Lungimea maximă a răspunsului
128K
128.000 de tokeni
Intrare / Ieșire
text, imagine → text

Prețuri API

USD / 1 mil. de tokeni

IntrareIeșireIntrare din cache
  • $2,00
openrouter
  • $12,00
openrouter
  • $0,20
openrouter

Cronologie

  1. · Disponibilitate prin API · link către dovezi
Preț intrare / ieșire · USD / 1 mil. de tokeni
ModelLinkuri către doveziPreț intrare / ieșire
gpt-5.6-terra
batch · long context
openai
Ruta API nu este disponibilă
$2,00 (Intrare > 272.000 de tokeni) / $9,00 (Intrare > 272.000 de tokeni)
gpt-5.6-terra
batch · short context
openai
Ruta API nu este disponibilă
$1,00 (Intrare ≤ 272.000 de tokeni) / $6,00 (Intrare ≤ 272.000 de tokeni)
gpt-5.6-terra
fast · long context
openai
Ruta API nu este disponibilă
$8,00 (Intrare > 272.000 de tokeni) / $36,00 (Intrare > 272.000 de tokeni)
gpt-5.6-terra
fast · short context
openai
Ruta API nu este disponibilă
$4,00 (Intrare ≤ 272.000 de tokeni) / $24,00 (Intrare ≤ 272.000 de tokeni)
gpt-5.6-terra
flex · long context
openai
Ruta API nu este disponibilă
$2,00 (Intrare > 272.000 de tokeni) / $9,00 (Intrare > 272.000 de tokeni)
gpt-5.6-terra
flex · short context
openai
Ruta API nu este disponibilă
$1,00 (Intrare ≤ 272.000 de tokeni) / $6,00 (Intrare ≤ 272.000 de tokeni)
gpt-5.6-terra
standard · long context
openai
Ruta API nu este disponibilă
$4,00 (Intrare > 272.000 de tokeni) / $18,00 (Intrare > 272.000 de tokeni)
gpt-5.6-terra
standard · short context
openai
Ruta API nu este disponibilă
$2,00 (Intrare ≤ 272.000 de tokeni) / $12,00 (Intrare ≤ 272.000 de tokeni)
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. · Utilizare confirmată · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
    Fragment
    After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data
  2. · Utilizare confirmată · OpenAI · official articles · How we detect frontier model misalignment
  3. · Înregistrare în catalog · GitHub · openai/openai-node · code
    Fragment
    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. · Înregistrare în catalog · GitHub · openai/codex · code
    Fragment
    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

    Această referință din cod nu confirmă un identificator de model utilizat.

  5. · Înregistrare în catalog · GitHub · openai/codex · code
    Fragment
    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. · Înregistrare în catalog · GitHub · openai/openai-agents-python · code
    Fragment
    "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. · Înregistrare în catalog · GitHub · openai/openai-python · code
    Fragment
    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. · Înregistrare în catalog · GitHub · openai/openai-agents-js · code
    Fragment
    .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. · Postările lui Alex · Alex Getman
    Fragment
    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. · Postările lui Alex · Alex Getman
    Fragment
    🚨 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. · Postările lui Alex · Alex Getman
    Fragment
    ⚡️ 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. · Postările lui Alex · Alex Getman
    Fragment
    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. · Postările lui Alex · Alex Getman
    Fragment
    ⚡️ 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. · Postările lui Alex · Alex Getman
    Fragment
    ⚡️ 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. · Postările lui Alex · Alex Getman
    Fragment
    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

Semnale 2

Specificații

GPT-5.6 Terra este un model de la OpenAI, lansat la data de 2026-07-09. Fereastra de context este de 1.050.000 de tokeni. Lungimea maximă a răspunsului este de 128.000 de tokeni. Prețul este de $2,00 pentru un milion de tokeni de intrare și de $12,00 pentru un milion de tokeni de ieșire.

Fereastră de context
1.050.000
Lungimea maximă a răspunsului
128.000
Intrare
text · imagine
Ieșire
text
Intrare · USD / 1 mil. de tokeni
$2,00
Ieșire · USD / 1 mil. de tokeni
$12,00
Intrare din cache · USD / 1 mil. de tokeni
$0,20

Întrebări

Care este fereastra de context a modelului GPT-5.6 Terra?

Fereastra de context este de 1.050.000 de tokeni. Lungimea maximă a răspunsului este de 128.000 de tokeni.

Cât costă GPT-5.6 Terra?

Prețul este de $2,00 pentru un milion de tokeni de intrare și de $12,00 pentru un milion de tokeni de ieșire. Intrarea din cache costă $0,20 pentru un milion de tokeni.

Când a fost lansat GPT-5.6 Terra?

La data de 2026-07-09.