GPT-5.6 Terra

時系列

1. 2026-07-09 · APIでの提供 · 参照リンク

1. 2026-11-14T08:45:27.686Z · 利用を確認 · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
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   After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data

2. 2026-11-14T08:23:25.536Z · 利用を確認 · OpenAI · official articles · How we detect frontier model misalignment
3. 2026-11-12T18:45:20.401Z · カタログへの掲載 · GitHub · openai/openai-node · code
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   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. 2026-11-10T18:00:44.369Z · カタログへの掲載 · GitHub · openai/codex · code
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   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

   このコード内の参照は、実際に使用されるモデル識別子を裏付けるものではありません。

5. 2026-11-10T18:00:44.369Z · カタログへの掲載 · GitHub · openai/codex · code
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   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. 2026-11-10T17:59:50.472Z · カタログへの掲載 · GitHub · openai/openai-agents-python · code
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   "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. 2026-11-10T17:58:16.002Z · カタログへの掲載 · GitHub · openai/openai-python · code
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   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. 2026-11-10T17:58:10.980Z · カタログへの掲載 · GitHub · openai/openai-agents-js · code
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   .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. 2026-09-16T15:54:37.278Z · Alex の投稿 · Alex Getman
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   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. 2026-09-02T15:31:23.681Z · Alex の投稿 · Alex Getman
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    🚨 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. 2026-08-18T05:00:00.000Z · Alex の投稿 · Alex Getman
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    ⚡️ 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. 2026-08-14T05:00:00.000Z · Alex の投稿 · Alex Getman
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    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. 2026-08-03T08:01:30.947Z · Alex の投稿 · Alex Getman
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    ⚡️ 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. 2026-07-16T17:56:24.898Z · Alex の投稿 · Alex Getman
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    ⚡️ 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. 2026-07-10T16:34:15.332Z · Alex の投稿 · Alex Getman
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    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

シグナル 2

20:2509-19

OpenAI発見

Artificial Analysis で GPT-5.6 Terra を発見

確認済み4件の参照リンク

22:0909-11

OpenAI発見

Arena で GPT-5.6 Terra を発見

確認済み14件の参照リンク