Alex GetmanAI 早期信号

GPT-5.6 Terra

OpenAI

已发布 更新于
上下文窗口
1M
1,050,000 token
最大输出
128K
128,000 token
输入 / 输出
文本, 图像 → 文本

API 价格

USD / 百万 token

输入输出缓存输入
  • $2.00
openrouter
  • $12.00
openrouter
  • $0.20
openrouter

时间线

  1. · API 开放 · 参考链接
输入/输出价格 · USD / 百万 token
模型参考链接输入/输出价格
gpt-5.6-terra
batch · long context
openai
API 路由不可用
$2.00 (输入 > 272,000 token) / $9.00 (输入 > 272,000 token)
gpt-5.6-terra
batch · short context
openai
API 路由不可用
$1.00 (输入 ≤ 272,000 token) / $6.00 (输入 ≤ 272,000 token)
gpt-5.6-terra
fast · long context
openai
API 路由不可用
$8.00 (输入 > 272,000 token) / $36.00 (输入 > 272,000 token)
gpt-5.6-terra
fast · short context
openai
API 路由不可用
$4.00 (输入 ≤ 272,000 token) / $24.00 (输入 ≤ 272,000 token)
gpt-5.6-terra
flex · long context
openai
API 路由不可用
$2.00 (输入 > 272,000 token) / $9.00 (输入 > 272,000 token)
gpt-5.6-terra
flex · short context
openai
API 路由不可用
$1.00 (输入 ≤ 272,000 token) / $6.00 (输入 ≤ 272,000 token)
gpt-5.6-terra
standard · long context
openai
API 路由不可用
$4.00 (输入 > 272,000 token) / $18.00 (输入 > 272,000 token)
gpt-5.6-terra
standard · short context
openai
API 路由不可用
$2.00 (输入 ≤ 272,000 token) / $12.00 (输入 ≤ 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
  1. · 已确认使用 · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
    摘录
    After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data
  2. · 已确认使用 · OpenAI · official articles · How we detect frontier model misalignment
  3. · 目录记录 · GitHub · openai/openai-node · code
    摘录
    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. · 目录记录 · GitHub · openai/codex · code
    摘录
    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

    此代码引用并不能证实这是实际使用中的模型标识符。

  5. · 目录记录 · GitHub · openai/codex · code
    摘录
    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. · 目录记录 · GitHub · openai/openai-agents-python · code
    摘录
    "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. · 目录记录 · GitHub · openai/openai-python · code
    摘录
    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. · 目录记录 · GitHub · openai/openai-agents-js · code
    摘录
    .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. · Alex 的文章 · Alex Getman
    摘录
    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. · Alex 的文章 · Alex Getman
    摘录
    🚨 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. · Alex 的文章 · Alex Getman
    摘录
    ⚡️ 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. · Alex 的文章 · Alex Getman
    摘录
    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. · Alex 的文章 · Alex Getman
    摘录
    ⚡️ 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. · Alex 的文章 · Alex Getman
    摘录
    ⚡️ 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. · Alex 的文章 · Alex Getman
    摘录
    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

信号 2

规格

GPT-5.6 Terra 是 OpenAI 的 AI 模型,于 2026-07-09 发布。 上下文窗口为1,050,000 token。 最大输出为128,000 token。 每百万 token 的价格为:输入 $2.00,输出 $12.00。

上下文窗口
1,050,000
最大输出
128,000
输入
文本 · 图像
输出
文本
输入 · USD / 百万 token
$2.00
输出 · USD / 百万 token
$12.00
缓存输入 · USD / 百万 token
$0.20

常见问题

GPT-5.6 Terra 的上下文窗口有多大?

上下文窗口为1,050,000 token。 最大输出为128,000 token。

GPT-5.6 Terra 的价格是多少?

每百万 token 的价格为:输入 $2.00,输出 $12.00。 缓存输入价格为每百万 token $0.20。

GPT-5.6 Terra 何时发布?

2026-07-09。