Alex Getmansinyal AI awal

GPT-5.6 Terra

OpenAI

Dirilis Diperbarui
Jendela konteks
1M
1.050.000 token
Keluaran maksimal
128K
128.000 token
Masukan / Keluaran
teks, gambar → teks

Harga API

USD / 1 juta token

MasukanKeluaranMasukan dalam cache
  • $2,00
openrouter
  • $12,00
openrouter
  • $0,20
openrouter

Linimasa

  1. · Ketersediaan API · tautan bukti
Harga masukan / keluaran · USD / 1 juta token
ModelTautan buktiHarga masukan / keluaran
gpt-5.6-terra
batch · long context
openai
Rute API tidak tersedia
$2,00 (Masukan > 272.000 token) / $9,00 (Masukan > 272.000 token)
gpt-5.6-terra
batch · short context
openai
Rute API tidak tersedia
$1,00 (Masukan ≤ 272.000 token) / $6,00 (Masukan ≤ 272.000 token)
gpt-5.6-terra
fast · long context
openai
Rute API tidak tersedia
$8,00 (Masukan > 272.000 token) / $36,00 (Masukan > 272.000 token)
gpt-5.6-terra
fast · short context
openai
Rute API tidak tersedia
$4,00 (Masukan ≤ 272.000 token) / $24,00 (Masukan ≤ 272.000 token)
gpt-5.6-terra
flex · long context
openai
Rute API tidak tersedia
$2,00 (Masukan > 272.000 token) / $9,00 (Masukan > 272.000 token)
gpt-5.6-terra
flex · short context
openai
Rute API tidak tersedia
$1,00 (Masukan ≤ 272.000 token) / $6,00 (Masukan ≤ 272.000 token)
gpt-5.6-terra
standard · long context
openai
Rute API tidak tersedia
$4,00 (Masukan > 272.000 token) / $18,00 (Masukan > 272.000 token)
gpt-5.6-terra
standard · short context
openai
Rute API tidak tersedia
$2,00 (Masukan ≤ 272.000 token) / $12,00 (Masukan ≤ 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. · Penggunaan terkonfirmasi · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
    Kutipan
    After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data
  2. · Penggunaan terkonfirmasi · OpenAI · official articles · How we detect frontier model misalignment
  3. · Entri katalog · GitHub · openai/openai-node · code
    Kutipan
    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. · Entri katalog · GitHub · openai/codex · code
    Kutipan
    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

    Referensi dalam kode ini tidak mengonfirmasi pengenal model yang digunakan.

  5. · Entri katalog · GitHub · openai/codex · code
    Kutipan
    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. · Entri katalog · GitHub · openai/openai-agents-python · code
    Kutipan
    "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. · Entri katalog · GitHub · openai/openai-python · code
    Kutipan
    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. · Entri katalog · GitHub · openai/openai-agents-js · code
    Kutipan
    .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. · Postingan Alex · Alex Getman
    Kutipan
    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. · Postingan Alex · Alex Getman
    Kutipan
    🚨 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. · Postingan Alex · Alex Getman
    Kutipan
    ⚡️ 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. · Postingan Alex · Alex Getman
    Kutipan
    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. · Postingan Alex · Alex Getman
    Kutipan
    ⚡️ 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. · Postingan Alex · Alex Getman
    Kutipan
    ⚡️ 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. · Postingan Alex · Alex Getman
    Kutipan
    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

Sinyal 2

Spesifikasi

GPT-5.6 Terra adalah model AI dari OpenAI, dirilis pada 2026-07-09. Jendela konteksnya sebesar 1.050.000 token. Keluaran maksimalnya 128.000 token. Harga per satu juta token adalah $2,00 untuk input dan $12,00 untuk output.

Jendela konteks
1.050.000
Keluaran maksimal
128.000
Masukan
teks · gambar
Keluaran
teks
Masukan · USD / 1 juta token
$2,00
Keluaran · USD / 1 juta token
$12,00
Masukan dalam cache · USD / 1 juta token
$0,20

Pertanyaan

Seberapa besar jendela konteks GPT-5.6 Terra?

Jendela konteksnya sebesar 1.050.000 token. Keluaran maksimalnya 128.000 token.

Berapa biaya GPT-5.6 Terra?

Harga per satu juta token adalah $2,00 untuk input dan $12,00 untuk output. Masukan dari cache berharga $0,20 per juta token.

Kapan GPT-5.6 Terra dirilis?

2026-07-09.