Alex Getmanwczesne sygnały AI

GPT-5.6 Terra

OpenAI

Wydano Zaktualizowano
Okno kontekstowe
1M
1 050 000 tokenów
Maksymalna długość odpowiedzi
128K
128 000 tokenów
Wejście / Wyjście
tekst, obraz → tekst

Ceny API

USD / 1 mln tokenów

WejścieWyjścieWejście z pamięci podręcznej
  • $2,00
openrouter
  • $12,00
openrouter
  • $0,20
openrouter

Oś czasu

  1. · Dostępność API · odnośnik źródłowy
Cena wejścia/wyjścia · USD / 1 mln tokenów
ModelOdnośniki źródłoweCena wejścia/wyjścia
gpt-5.6-terra
batch · long context
openai
Trasa API jest niedostępna
$2,00 (Wejście > 272 000 tokenów) / $9,00 (Wejście > 272 000 tokenów)
gpt-5.6-terra
batch · short context
openai
Trasa API jest niedostępna
$1,00 (Wejście ≤ 272 000 tokenów) / $6,00 (Wejście ≤ 272 000 tokenów)
gpt-5.6-terra
fast · long context
openai
Trasa API jest niedostępna
$8,00 (Wejście > 272 000 tokenów) / $36,00 (Wejście > 272 000 tokenów)
gpt-5.6-terra
fast · short context
openai
Trasa API jest niedostępna
$4,00 (Wejście ≤ 272 000 tokenów) / $24,00 (Wejście ≤ 272 000 tokenów)
gpt-5.6-terra
flex · long context
openai
Trasa API jest niedostępna
$2,00 (Wejście > 272 000 tokenów) / $9,00 (Wejście > 272 000 tokenów)
gpt-5.6-terra
flex · short context
openai
Trasa API jest niedostępna
$1,00 (Wejście ≤ 272 000 tokenów) / $6,00 (Wejście ≤ 272 000 tokenów)
gpt-5.6-terra
standard · long context
openai
Trasa API jest niedostępna
$4,00 (Wejście > 272 000 tokenów) / $18,00 (Wejście > 272 000 tokenów)
gpt-5.6-terra
standard · short context
openai
Trasa API jest niedostępna
$2,00 (Wejście ≤ 272 000 tokenów) / $12,00 (Wejście ≤ 272 000 tokenów)
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. · Potwierdzone użycie · 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. · Potwierdzone użycie · OpenAI · official articles · How we detect frontier model misalignment
  3. · Wpis w katalogu · 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. · Wpis w katalogu · 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

    To odwołanie w kodzie nie potwierdza używanego identyfikatora modelu.

  5. · Wpis w katalogu · 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. · Wpis w katalogu · 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. · Wpis w katalogu · 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. · Wpis w katalogu · 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. · Wpisy Alexa · 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. · Wpisy Alexa · 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. · Wpisy Alexa · 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. · Wpisy Alexa · 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. · Wpisy Alexa · 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. · Wpisy Alexa · 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. · Wpisy Alexa · 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

Sygnały 2

Dane techniczne

GPT-5.6 Terra to model AI od OpenAI, wydany 2026-07-09. Okno kontekstowe wynosi 1 050 000 tokenów. Maksymalna długość odpowiedzi to 128 000 tokenów. Cena za milion tokenów wynosi $2,00 na wejściu i $12,00 na wyjściu.

Okno kontekstowe
1 050 000
Maksymalna długość odpowiedzi
128 000
Wejście
tekst · obraz
Wyjście
tekst
Wejście · USD / 1 mln tokenów
$2,00
Wyjście · USD / 1 mln tokenów
$12,00
Wejście z pamięci podręcznej · USD / 1 mln tokenów
$0,20

Pytania

Jakie jest okno kontekstowe GPT-5.6 Terra?

Okno kontekstowe wynosi 1 050 000 tokenów. Maksymalna długość odpowiedzi to 128 000 tokenów.

Ile kosztuje GPT-5.6 Terra?

Cena za milion tokenów wynosi $2,00 na wejściu i $12,00 na wyjściu. Wejście z pamięci podręcznej kosztuje $0,20 za milion tokenów.

Kiedy wydano GPT-5.6 Terra?

2026-07-09.