Alex GetmanAI의 초기 신호
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GPT-5.6 Terra

OpenAI

출시됨 업데이트
컨텍스트 창
1M
1,050,000 토큰
최대 출력
128K
128,000 토큰
입력 / 출력
텍스트, 이미지 → 텍스트

API 요금

USD / 토큰 100만 개

입력출력캐시 입력
  • $2.00
openrouter
  • $12.00
openrouter
  • $0.20
openrouter
사용량 계산

타임라인

  1. · API 제공 · 근거 링크
입력/출력 가격 · USD / 토큰 100만 개
모델근거 링크입력/출력 가격
gpt-5.6-terra
batch · long context
openai
API 경로를 사용할 수 없음
$2.00 (입력 > 272,000 토큰) / $9.00 (입력 > 272,000 토큰)
gpt-5.6-terra
batch · short context
openai
API 경로를 사용할 수 없음
$1.00 (입력 ≤ 272,000 토큰) / $6.00 (입력 ≤ 272,000 토큰)
gpt-5.6-terra
fast · long context
openai
API 경로를 사용할 수 없음
$8.00 (입력 > 272,000 토큰) / $36.00 (입력 > 272,000 토큰)
gpt-5.6-terra
fast · short context
openai
API 경로를 사용할 수 없음
$4.00 (입력 ≤ 272,000 토큰) / $24.00 (입력 ≤ 272,000 토큰)
gpt-5.6-terra
flex · long context
openai
API 경로를 사용할 수 없음
$2.00 (입력 > 272,000 토큰) / $9.00 (입력 > 272,000 토큰)
gpt-5.6-terra
flex · short context
openai
API 경로를 사용할 수 없음
$1.00 (입력 ≤ 272,000 토큰) / $6.00 (입력 ≤ 272,000 토큰)
gpt-5.6-terra
standard · long context
openai
API 경로를 사용할 수 없음
$4.00 (입력 > 272,000 토큰) / $18.00 (입력 > 272,000 토큰)
gpt-5.6-terra
standard · short context
openai
API 경로를 사용할 수 없음
$2.00 (입력 ≤ 272,000 토큰) / $12.00 (입력 ≤ 272,000 토큰)
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. · 사용 확인 · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
    발췌
    After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data
  2. · 사용 확인 · OpenAI · official articles · How we detect frontier model misalignment
  3. · 카탈로그 등록 · GitHub · openai/openai-node · code
    발췌
    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. · 카탈로그 등록 · GitHub · openai/codex · code
    발췌
    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

    이 코드 참조는 실제로 사용되는 모델 식별자를 입증하지 않습니다.

  5. · 카탈로그 등록 · GitHub · openai/codex · code
    발췌
    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. · 카탈로그 등록 · GitHub · openai/openai-agents-python · code
    발췌
    "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. · 카탈로그 등록 · GitHub · openai/openai-python · code
    발췌
    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. · 카탈로그 등록 · GitHub · openai/openai-agents-js · code
    발췌
    .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. · Alex의 게시물 · Alex Getman
    발췌
    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. · Alex의 게시물 · Alex Getman
    발췌
    🚨 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. · Alex의 게시물 · Alex Getman
    발췌
    ⚡️ 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. · Alex의 게시물 · Alex Getman
    발췌
    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. · Alex의 게시물 · Alex Getman
    발췌
    ⚡️ 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. · Alex의 게시물 · Alex Getman
    발췌
    ⚡️ 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. · Alex의 게시물 · Alex Getman
    발췌
    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

시그널 2

사양

GPT-5.6 Terra: OpenAI의 AI 모델, 출시일 2026-07-09. 컨텍스트 윈도는 1,050,000 토큰입니다. 최대 출력은 128,000 토큰입니다. 토큰 100만 개당 요금은 입력 $2.00, 출력 $12.00입니다.

컨텍스트 창
1,050,000
최대 출력
128,000
입력
텍스트 · 이미지
출력
텍스트
입력 · USD / 토큰 100만 개
$2.00
출력 · USD / 토큰 100만 개
$12.00
캐시 입력 · USD / 토큰 100만 개
$0.20

자주 묻는 질문

GPT-5.6 Terra의 컨텍스트 창 크기는 얼마인가요?

컨텍스트 윈도는 1,050,000 토큰입니다. 최대 출력은 128,000 토큰입니다.

GPT-5.6 Terra의 가격은 얼마인가요?

토큰 100만 개당 요금은 입력 $2.00, 출력 $12.00입니다. 캐시 입력은 100만 개당 $0.20입니다.

GPT-5.6 Terra의 출시일은 언제인가요?

2026-07-09.