OpenAI announced a preview of its GPT-5.6 family, including a top-tier model comparable to Claude 5 Mythos — but so far it’s available only to users that are selected by the U.S. government.
What’s new: OpenAI launched three closed vision-language models that descend in price and performance from GPT-5.6 Sol, the most capable model, to the mid-tier GPT-5.6 Terra, and the fast and less-expensive GPT-5.6 Luna. They include safeguards to deny access to potentially dangerous biological, chemical, and cybersecurity information. All three models, as well as versions in which the safeguards are relaxed, are available to a limited number of organizations the U.S. government has approved. The company promises a wider release in the next few weeks.
- Input/output: Text and images in, text out
- Features: Max reasoning level (GPT-5.6 Sol only), ultra mode (delegates work to multiple agents) (GPT-5.6 Sol only), prompt caching with explicit breakpoints that let developers specify where reusable portions end
- Performance: In OpenAI’s tests, GPT-5.6 Sol set the state of the art on Terminal-Bench 2.1 (command-line coding)
- Availability: Currently limited to U.S. government-approved partners via OpenAI’s API and Codex, wider access planned via ChatGPT, Codex, and API
- Price: GPT-5.6 Sol $5/$0.50/$30 per 1 million input/cached/output tokens, GPT-5.6 Terra $2.50/$0.25/$15 per 1 million input/cached/output tokens, GPT-5.6 Luna $1/$0.10/$6 per 1 million input/cached/output tokens; starting in July, Cerebras will offer access to GPT-5.6 Sol at speeds up to 750 tokens per second at a price to be announced
- Undisclosed: Parameter count, architecture, training data and methods
How it works: Following its usual practice, OpenAI revealed little about how it built the GPT-5.6 models but detailed the guardrails around them.
- OpenAI trained all three GPT-5.6 models to reason through long deliberative traces before generating output, using reinforcement learning to reward reasoning traces that led to successful output. Training drew on public web data, data licensed from partners, data from OpenAI users, and data supplied by human trainers.
- A new max reasoning level, available only with GPT-5.6 Sol, expends more tokens to deliberate on input prompts. GPT-5.6 Sol also has a new ultra mode, in which the model spawns multiple subagents, assigns each of them a piece of a multi-step task, and coordinates their work.
- The models were trained to resist jailbreaks and prompt injections, with special guardrails around knowledge of biology, chemistry, and cybersecurity. All three models use a fast classifier that scans every conversation for information related to biological, chemical, or cybersecurity attacks. Sol and Terra add a second classifier that watches their internal activations and intervenes mid-generation to pause potentially problematic responses and hand them to a separate reasoning model to verify, which allows or withholds them. User behavior can trigger an automated review of the user’s other conversations and, in some cases, a manual review that could lead the company to suspend or ban the user.
- OpenAI plans to reserve for vetted organizations the highest-risk cyber and biology/chemistry capabilities its models can deliver. Organizations that qualify for a trusted-access program will receive versions of the models that have fewer safeguards.
Performance: In OpenAI’s tests, the GPT-5.6 models achieved strong performance in coding, cybersecurity, and biology, but independent confirmation is scarce. Model Evaluation and Threat Research (METR), a nonprofit test lab, published an inconclusive evaluation of GPT-5.6 Sol’s ability to act autonomously. Tests by the nonprofit biosecurity outfit SecureBio are the only independent results available and indicate an unusually high degree of biology knowledge.
- According to OpenAI’s tests, GPT-5.6 Sol set to ultra mode achieved the state of the art (91.9 percent) on Terminal-Bench 2.1, which tests multistep command-line coding. On the same benchmark, GPT-5.6 Sol set to an unspecified reasoning level (88.8 percent) narrowly outperformed Anthropic’s Claude Mythos 5 (88.0 percent).
- OpenAI says that on ExploitBench, which tests a model’s ability to find and exploit software vulnerabilities, GPT-5.6 Sol set to max reasoning (73.5 percent) approached Claude Mythos Preview (74.2 percent) but generated about one-third as many output tokens. It achieved 96.7 percent on OpenAI’s internal capture-the-flag security challenges, in which the model finds and exploits flaws to retrieve hidden tokens. Nonetheless, OpenAI places GPT-5.6 Sol below its “critical” cybersecurity capability threshold. It found bugs and components of exploits, but it did not autonomously build a complete working exploit.
- In independent tests, SecureBio found that a guardrail-free version of GPT-5.6 Sol set to an unspecified reasoning level posted the highest scores it had recorded on several expert-level biology tests, including 68.3 percent on World-Class Bio, a test of advanced bioengineering knowledge, a nearly 10-point improvement over GPT-5.5 (59.7 percent).
- METR could not produce a clean capability score because GPT-5.6 Sol so often found shortcuts to correct answers, such as hidden test cases, without solving the problems posed by prompts. METR measures the length of time a model can work on tasks autonomously (longer is better). GPT-5.6 Sol worked for 11.3 hours (similar to Claude Opus 4.6) if METR counted its shortcuts as failures, but it worked for more than 270 hours (well beyond the state of the art) if METR counted shortcuts as successes.
- OpenAI’s system card notes that GPT-5.6 Sol is better than its predecessor at shaping its own chain of thought (CoT) on instructions, a proxy for the ability to conceal its reasoning. OpenAI argues low CoT controllability represents safer model behavior, since it suggests a model cannot obfuscate its reasoning to evade oversight as well as a model with high CoT controllability. GPT-5.6 Sol’s rate (1.3 percent) tripled from GPT-5.5’s (0.4 percent) at 5,000 tokens of reasoning.
Behind the news: The U.S. government lately has begun to control the launch of top-performing AI models. OpenAI said it previewed the three models and their capabilities to the U.S. government before it launched the models. At the government’s request, OpenAI limited the launch to around 20 government-approved organizations. OpenAI did not disclose their names or what kinds of organizations they were. In its blog post, the company said this step was temporary and that it does not want government-controlled access to become usual. The same day that OpenAI announced GPT-5.6, the U.S. government granted Anthropic permission to offer its Claude Mythos 5 model to roughly 100 companies and federal agencies, two weeks after it forced Anthropic to suspend Claude Mythos 5 and Claude Fable 5 for all customers. Days later, Anthropic was able to restore Claude Fable 5.
Why it matters: The GPT-5.6 family brings concerns about AI’s security implications to the lower-priced tier. Cheaper, faster models used to draw less scrutiny because they were less capable, and speed was a premium. By adding to even GPT-5.6 Luna safeguards once reserved for the most advanced models, OpenAI is making life more difficult for developers of high-volume services. Engineers who work on legitimate applications, say, verifying codebase vulnerabilities or chemistry lab results, may now encounter refusals, added latency from paused output, or even account-level reviews.
We’re thinking: OpenAI said it is working with the White House on “a repeatable process for future model releases.” We hope this process grows more transparent, more predictable, and includes wider access than the launches of Claude 5 Mythos and GPT-5.6.