元記事の説明文
<p>OpenAI's latest flagship model <a href="https://openai.com/index/gpt-5-6/">hit general availability this morning</a>, and comes in three sizes: Luna, Terra, and Sol (from smallest to largest).</p>
<p>The new models are priced per 1M input/output tokens as Luna $1/$6, Terra $2.50/$15, Sol $5/$30. For comparison, the Claude Opus series are $5/$25 and the Claude Fable 5 is $10/$50, but price-per-million tokens doesn't tell us much now that the number of reasoning tokens can differ so much between models for the same task.</p>
<p>OpenAI's biggest benchmark claim concerns long-running agentic performance, with one benchmark showing all three models outperforming Claude Fable 5:</p>
<blockquote>
<p>We trained GPT-5.6 to get more useful work from every token. On <a href="https://agents-last-exam.org/">Agents’ Last Exam</a>, an evaluation of long-running professional workflows across 55 fields, GPT-5.6 Sol sets a new high of 53.6, eclipsing Claude Fable 5 (adaptive reasoning) by 13.1 points. Even at medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost. That efficiency extends to smaller models, which are essential to making intelligence more abundant and affordable: GPT-5.6 Terra and GPT-5.6 Luna outperform Fable 5 at around one-sixteenth the cost.</p>
</blockquote>
<p>Amusingly, one self-reported benchmark that Fable 5 crushed the GPT-5.6 family on was SWE-Bench Pro, where Fable 5 got 80% compared to GUT-5.6 Sol getting 64.6%. This may help explain why OpenAI chose to publish <a href="https://openai.com/index/separating-signal-from-noise-coding-evaluations/">this article yesterday</a> specifically calling out SWE-Bench Pro for problems they found while auditing that benchmark:</p>
<blockquote>
<p>In light of these results, we estimate that ~30% of SWE-bench Pro tasks are broken, and advise that model developers carefully examine results</p>
</blockquote>
<p>I've had some early access to GPT-5.6 Sol - it's definitely very competent, though so far it hasn't struck me as better than Fable at the kind of complex coding tasks I've been using with Anthropic's model.</p>
<p>As usual, the <a href="https://developers.openai.com/api/docs/guides/latest-model?model=gpt-5.6">model guidance for using GPT-5.6</a> has the most interesting details. There are a bunch of new API features that I need to explore (and probably add support for in <a href="https://llm.datasette.io/">LLM</a>), including:</p>
<ul>
<li>
<a href="https://developers.openai.com/api/docs/guides/tools-programmatic-tool-calling">Programmatic Tool Calling</a> allows the models to "compose and run JavaScript that orchestrates tool calls" - which sounds to me like it could help bridge the gap between MCPs and full terminal sessions that can compose CLI utilities in useful ways. Also reminiscent of the <a href="https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool#dynamic-filtering">dynamic filtering</a> mechanism Anthropic added to their web search tool, which allows code execution against web results as part of a single model turn.</li>
<li>
<a href="https://developers.openai.com/api/docs/guides/tools-multi-agent">Multi-agent</a> lets the model "spin up subagents for parallel, focused work" - the sub-agent pattern now baked into the core API.</li>
<li>
<a href="https://developers.openai.com/api/docs/guides/prompt-caching#prompt-cache-breakpoints">Prompt cache breakpoints</a> brings the Claude model of prompt caching to OpenAI, letting you be explicit about where the cache breakpoints are rather than relying on the API to detect them automatically. Personally I much prefer automatic detection (still supported by OpenAI), but presumably there are optimization cost savings to be had here if you put the work in.</li>
<li>You can now set <a href="https://developers.openai.com/api/docs/guides/images-vision#choose-an-image-detail-level">detail: original</a> on image requests to avoid resizing the image at all before it is processed.</li>
</ul>
<p>Here's <a href="https://static.simonwillison.net/static/2026/gpt-5.6-pelicans.html">a full page with 18 different pelicans</a> - for reasoning efforts none, low, medium, high, xhigh, and max across the three different models. It also lists their token and calculated costs - the least expensive was gpt-5.6-luna at effort none for 0.71 cents, the most expensive was gpt-5.6-sol at max reasoning level for 48.55 cents.</p>
<p><img alt="A grid of nine pelicans riding bicycles, of varying quality" src="https://static.simonwillison.net/static/2026/gpt-5.6-pelicans.webp" /></p>
<p>In further pelican news, if you jump to 17:50 in <a href="https://www.youtube.com/live/Wq45rvPGNHs?t=1070s">their livestream from this morning</a> you'll see OpenAI's own demo of 3D pelicans riding a tricycle, a bicycle, a pony, and another pelican!</p>
<p><img alt="Frame from a livestream showing a 3D model of a pelican riding another pelican" src="https://static.simonwillison.net/static/2026/pelican-riding-a-pelican.jpg" /></p>
<p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/openai">openai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/llm-tool-use">llm-tool-use</a>, <a href="https://simonwillison.net/tags/llm-pricing">llm-pricing</a>, <a href="https://simonwillison.net/tags/pelican-riding-a-bicycle">pelican-riding-a-bicycle</a>, <a href="https://simonwillison.net/tags/llm-release">llm-release</a>, <a href="https://simonwillison.net/tags/gpt-5">gpt-5</a></p>