Alex Getmanearly AI signals

GPT-5.6 Terra

OpenAI

Released Updated
Context window
1M
1,050,000 tokens
Max output
128K
128,000 tokens
Input / Output
text, image → text

API pricing

USD / 1M tokens

InputOutputCached input
  • $2.00
openrouter
  • $12.00
openrouter
  • $0.20
openrouter

Timeline

  1. · API availability · evidence link
Input / output price · USD / 1M tokens
ModelEvidence linksInput / output price
gpt-5.6-terra
batch · long context
openai
API route unavailable
$2.00 (Input > 272,000 tokens) / $9.00 (Input > 272,000 tokens)
gpt-5.6-terra
batch · short context
openai
API route unavailable
$1.00 (Input ≤ 272,000 tokens) / $6.00 (Input ≤ 272,000 tokens)
gpt-5.6-terra
fast · long context
openai
API route unavailable
$8.00 (Input > 272,000 tokens) / $36.00 (Input > 272,000 tokens)
gpt-5.6-terra
fast · short context
openai
API route unavailable
$4.00 (Input ≤ 272,000 tokens) / $24.00 (Input ≤ 272,000 tokens)
gpt-5.6-terra
flex · long context
openai
API route unavailable
$2.00 (Input > 272,000 tokens) / $9.00 (Input > 272,000 tokens)
gpt-5.6-terra
flex · short context
openai
API route unavailable
$1.00 (Input ≤ 272,000 tokens) / $6.00 (Input ≤ 272,000 tokens)
gpt-5.6-terra
standard · long context
openai
API route unavailable
$4.00 (Input > 272,000 tokens) / $18.00 (Input > 272,000 tokens)
gpt-5.6-terra
standard · short context
openai
API route unavailable
$2.00 (Input ≤ 272,000 tokens) / $12.00 (Input ≤ 272,000 tokens)
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. · Confirmed use · OpenAI · official articles · V7 cuts costs 78% while boosting accuracy with GPT-5.6 Luna
  2. · Confirmed use · OpenAI · official articles · Retell uses OpenAI models to help power 100M monthly calls
    Excerpt
    After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data
  3. · Confirmed use · OpenAI · official articles · How we detect frontier model misalignment
  4. · Catalogue listing · GitHub · openai/openai-node · code
    Excerpt
    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
  5. · Catalogue listing · GitHub · openai/codex · code
    Excerpt
    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

    This code reference does not establish a working model identifier.

  6. · Catalogue listing · GitHub · openai/codex · code
    Excerpt
    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_
  7. · Catalogue listing · GitHub · openai/openai-agents-python · code
    Excerpt
    "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-
  8. · Catalogue listing · GitHub · openai/openai-python · code
    Excerpt
    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",
  9. · Catalogue listing · GitHub · openai/openai-agents-js · code
    Excerpt
    .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
  10. · Alex's posts · Alex Getman
    Excerpt
    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.
  11. · Alex's posts · Alex Getman
    Excerpt
    🚨 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
  12. · Alex's posts · Alex Getman
    Excerpt
    ⚡️ 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.
  13. · Alex's posts · Alex Getman
    Excerpt
    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.
  14. · Alex's posts · Alex Getman
    Excerpt
    ⚡️ 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.
  15. · Alex's posts · Alex Getman
    Excerpt
    ⚡️ 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.
  16. · Alex's posts · Alex Getman
    Excerpt
    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

Alex's take

Signals 2

Specifications

GPT-5.6 Terra is a model from OpenAI, released on 9 Jul 2026. The context window is 1,050,000 tokens. The maximum output is 128,000 tokens. Pricing is $2.00 per million input tokens and $12.00 per million output tokens.

Context window
1,050,000
Max output
128,000
Input
text · image
Output
text
Input · USD / 1M tokens
$2.00
Output · USD / 1M tokens
$12.00
Cached input · USD / 1M tokens
$0.20

Questions

What is the context window of GPT-5.6 Terra?

The context window is 1,050,000 tokens. The maximum output is 128,000 tokens.

How much does GPT-5.6 Terra cost?

Pricing is $2.00 per million input tokens and $12.00 per million output tokens. Cached input is $0.20 per million.

When was GPT-5.6 Terra released?

On 9 Jul 2026.