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  "aboutHtml": "<p>Ling 3.0 Flash ialah model teks-ke-teks daripada inclusionAI. Seni bina yang diterbitkan ialah campuran pakar dengan 124 bilion parameter keseluruhan dan kira-kira 5.1 bilion parameter aktif bagi setiap token. Pilih inclusionai/ling-3.0-flash melalui POST /v1/chat/completions dengan model dan tatasusunan messages. Arahan, bahan sumber dan giliran perbualan terdahulu diletakkan dalam teks mesej.</p><p>Gunakan prompt terfokus untuk mengubah bahan yang dibekalkan menjadi hasil khusus. Untuk balasan pelanggan, bekalkan soalan pelanggan dan teks produk atau dasar yang mesti digunakan oleh jawapan, kemudian tentukan nada dan langkah seterusnya. Untuk ringkasan dokumen, nyatakan bahagian yang diperlukan, seperti keputusan, persoalan yang belum selesai dan tindakan. Untuk pengekstrakan, tentukan medan yang diminta dan cara mewakili fakta yang tiada dalam sumber. Untuk kerja kod, sertakan fungsi atau petikan fail yang berkaitan dan minta penerangan, cadangan perubahan atau semakan berdasarkan keperluan yang jelas.</p><p>Tetingkap konteks yang diterbitkan ialah 262,144 token, dengan sehingga 32,768 token pelengkapan disenaraikan untuk model. Peruntukkan teks sumber, arahan, sejarah perbualan dan pelengkapan yang diminta bersama-sama. Gunakan max_tokens untuk menetapkan peruntukan output yang diminta. Dokumen perlu dibekalkan sebagai teks sebenarnya apabila anda mahu jawapan bergantung pada kandungannya; rujuk bahagian yang berkaitan secara jelas apabila permintaan menggabungkan beberapa petikan.</p><p>Senarai parameter yang disokong merangkumi tools dan tool_choice untuk panggilan fungsi. Takrifkan fungsi yang tersedia dan penerangan argumennya dalam permintaan. Aplikasi anda mengesahkan panggilan yang dikembalikan, melaksanakan fungsi yang dipilih dan membekalkan hasilnya kepada perbualan sebelum meminta jawapan susulan. Sebagai contoh, aliran kerja sokongan boleh menyediakan carian pesanan dengan skop sempit dan menggunakan fakta yang dikembalikan semasa mendraf balasan. Pastikan hasil alat dilampirkan pada giliran perbualan yang betul supaya model boleh membezakannya daripada permintaan asal pengguna.</p><p>Model menyokong response_format untuk output JSON, tanpa penguatkuasaan skema JSON. Nyatakan medan yang dikehendaki dan sahkan objek yang dikembalikan dalam aplikasi anda sebelum menggunakannya. Kawalan lain yang diterbitkan merangkumi temperature, top_p, top_k, min_p, stop, seed serta penalti pengulangan, kekerapan dan kehadiran yang disenaraikan. Senarai parameter turut merangkumi reasoning dan include_reasoning; pilih perwakilan yang disokong daripada spesifikasi permintaan, dan bukannya menganggap nilai daripada edisi yang berbeza.</p><p>Chat Completions boleh mengembalikan balasan lengkap atau teks berstrim. Untuk balasan lengkap, baca kandungan mesej yang dikembalikan dan simpan choices serta usage balasan. Dengan penstriman, gabungkan delta sebelum menganggap jawapan lengkap, dan simpan sebarang panggilan alat yang dikembalikan bersama argumennya. Hasilnya ialah teks atau permintaan panggilan fungsi untuk langkah aplikasi seterusnya; aplikasi yang mengelilinginya bertanggungjawab terhadap pelaksanaan dan penggunaan jawapan yang telah siap.</p>",
  "aboutText": "Ling 3.0 Flash ialah model teks-ke-teks daripada inclusionAI. Seni bina yang diterbitkan ialah campuran pakar dengan 124 bilion parameter keseluruhan dan kira-kira 5.1 bilion parameter aktif bagi setiap token. Pilih inclusionai/ling-3.0-flash melalui POST /v1/chat/completions dengan model dan tatasusunan messages. Arahan, bahan sumber dan giliran perbualan terdahulu diletakkan dalam teks mesej.\n\nGunakan prompt terfokus untuk mengubah bahan yang dibekalkan menjadi hasil khusus. Untuk balasan pelanggan, bekalkan soalan pelanggan dan teks produk atau dasar yang mesti digunakan oleh jawapan, kemudian tentukan nada dan langkah seterusnya. Untuk ringkasan dokumen, nyatakan bahagian yang diperlukan, seperti keputusan, persoalan yang belum selesai dan tindakan. Untuk pengekstrakan, tentukan medan yang diminta dan cara mewakili fakta yang tiada dalam sumber. Untuk kerja kod, sertakan fungsi atau petikan fail yang berkaitan dan minta penerangan, cadangan perubahan atau semakan berdasarkan keperluan yang jelas.\n\nTetingkap konteks yang diterbitkan ialah 262,144 token, dengan sehingga 32,768 token pelengkapan disenaraikan untuk model. Peruntukkan teks sumber, arahan, sejarah perbualan dan pelengkapan yang diminta bersama-sama. Gunakan max_tokens untuk menetapkan peruntukan output yang diminta. Dokumen perlu dibekalkan sebagai teks sebenarnya apabila anda mahu jawapan bergantung pada kandungannya; rujuk bahagian yang berkaitan secara jelas apabila permintaan menggabungkan beberapa petikan.\n\nSenarai parameter yang disokong merangkumi tools dan tool_choice untuk panggilan fungsi. Takrifkan fungsi yang tersedia dan penerangan argumennya dalam permintaan. Aplikasi anda mengesahkan panggilan yang dikembalikan, melaksanakan fungsi yang dipilih dan membekalkan hasilnya kepada perbualan sebelum meminta jawapan susulan. Sebagai contoh, aliran kerja sokongan boleh menyediakan carian pesanan dengan skop sempit dan menggunakan fakta yang dikembalikan semasa mendraf balasan. Pastikan hasil alat dilampirkan pada giliran perbualan yang betul supaya model boleh membezakannya daripada permintaan asal pengguna.\n\nModel menyokong response_format untuk output JSON, tanpa penguatkuasaan skema JSON. Nyatakan medan yang dikehendaki dan sahkan objek yang dikembalikan dalam aplikasi anda sebelum menggunakannya. Kawalan lain yang diterbitkan merangkumi temperature, top_p, top_k, min_p, stop, seed serta penalti pengulangan, kekerapan dan kehadiran yang disenaraikan. Senarai parameter turut merangkumi reasoning dan include_reasoning; pilih perwakilan yang disokong daripada spesifikasi permintaan, dan bukannya menganggap nilai daripada edisi yang berbeza.\n\nChat Completions boleh mengembalikan balasan lengkap atau teks berstrim. Untuk balasan lengkap, baca kandungan mesej yang dikembalikan dan simpan choices serta usage balasan. Dengan penstriman, gabungkan delta sebelum menganggap jawapan lengkap, dan simpan sebarang panggilan alat yang dikembalikan bersama argumennya. Hasilnya ialah teks atau permintaan panggilan fungsi untuk langkah aplikasi seterusnya; aplikasi yang mengelilinginya bertanggungjawab terhadap pelaksanaan dan penggunaan jawapan yang telah siap.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">inclusionAI: Ling 3.0 Flash</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Tutup\">×</button></div><div class=\"idline\"><span>inclusionAI /</span><code>inclusionai/ling-3.0-flash</code><button type=\"button\" id=\"copy-id\">Salin ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Bahagian kad model\"><button type=\"button\" data-card-jump=\"overview\">Pengenalan</button><button type=\"button\" data-card-jump=\"methods\">Input &amp; kaedah</button><button type=\"button\" data-card-jump=\"pricing\">Pelan harga</button><button type=\"button\" data-card-jump=\"examples\">Contoh</button><button type=\"button\" data-card-jump=\"results\">Hasil</button><button type=\"button\" data-card-jump=\"aliases\">Alias</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"Kaedah API\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|chat|/v1/chat/completions|inclusionai%2Fling-3.0-flash\">Chat</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Pengenalan</h3><p class=\"mdesc-full\">Ling 3.0 Flash ialah model teks inclusionAI untuk perbualan, kerja dokumen dan aliran kerja ejen yang dikawal aplikasi. Ia menerima teks melalui Chat Completions, menyokong pengisytiharan panggilan fungsi dan pemformatan output JSON, serta mempunyai tetingkap konteks 262,144 token yang diterbitkan. Gunakan ID tepat inclusionai/ling-3.0-flash apabila memilih edisi ini.</p><dl class=\"card-io\" data-card-io><div><dt>Input yang diterima</dt><dd>Teks</dd></div><div><dt>Hasil</dt><dd>Teks</dd></div></dl><details class=\"card-about\"><summary>Tentang model</summary><p class=\"note-s\" data-model-method-boundary>Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.</p><div class=\"mabout\" id=\"m-about\"><p>Ling 3.0 Flash ialah model teks-ke-teks daripada inclusionAI. Seni bina yang diterbitkan ialah campuran pakar dengan 124 bilion parameter keseluruhan dan kira-kira 5.1 bilion parameter aktif bagi setiap token. Pilih inclusionai/ling-3.0-flash melalui POST /v1/chat/completions dengan model dan tatasusunan messages. Arahan, bahan sumber dan giliran perbualan terdahulu diletakkan dalam teks mesej.</p><p>Gunakan prompt terfokus untuk mengubah bahan yang dibekalkan menjadi hasil khusus. Untuk balasan pelanggan, bekalkan soalan pelanggan dan teks produk atau dasar yang mesti digunakan oleh jawapan, kemudian tentukan nada dan langkah seterusnya. Untuk ringkasan dokumen, nyatakan bahagian yang diperlukan, seperti keputusan, persoalan yang belum selesai dan tindakan. Untuk pengekstrakan, tentukan medan yang diminta dan cara mewakili fakta yang tiada dalam sumber. Untuk kerja kod, sertakan fungsi atau petikan fail yang berkaitan dan minta penerangan, cadangan perubahan atau semakan berdasarkan keperluan yang jelas.</p><p>Tetingkap konteks yang diterbitkan ialah 262,144 token, dengan sehingga 32,768 token pelengkapan disenaraikan untuk model. Peruntukkan teks sumber, arahan, sejarah perbualan dan pelengkapan yang diminta bersama-sama. Gunakan max_tokens untuk menetapkan peruntukan output yang diminta. Dokumen perlu dibekalkan sebagai teks sebenarnya apabila anda mahu jawapan bergantung pada kandungannya; rujuk bahagian yang berkaitan secara jelas apabila permintaan menggabungkan beberapa petikan.</p><p>Senarai parameter yang disokong merangkumi tools dan tool_choice untuk panggilan fungsi. Takrifkan fungsi yang tersedia dan penerangan argumennya dalam permintaan. Aplikasi anda mengesahkan panggilan yang dikembalikan, melaksanakan fungsi yang dipilih dan membekalkan hasilnya kepada perbualan sebelum meminta jawapan susulan. Sebagai contoh, aliran kerja sokongan boleh menyediakan carian pesanan dengan skop sempit dan menggunakan fakta yang dikembalikan semasa mendraf balasan. Pastikan hasil alat dilampirkan pada giliran perbualan yang betul supaya model boleh membezakannya daripada permintaan asal pengguna.</p><p>Model menyokong response_format untuk output JSON, tanpa penguatkuasaan skema JSON. Nyatakan medan yang dikehendaki dan sahkan objek yang dikembalikan dalam aplikasi anda sebelum menggunakannya. Kawalan lain yang diterbitkan merangkumi temperature, top_p, top_k, min_p, stop, seed serta penalti pengulangan, kekerapan dan kehadiran yang disenaraikan. Senarai parameter turut merangkumi reasoning dan include_reasoning; pilih perwakilan yang disokong daripada spesifikasi permintaan, dan bukannya menganggap nilai daripada edisi yang berbeza.</p><p>Chat Completions boleh mengembalikan balasan lengkap atau teks berstrim. Untuk balasan lengkap, baca kandungan mesej yang dikembalikan dan simpan choices serta usage balasan. Dengan penstriman, gabungkan delta sebelum menganggap jawapan lengkap, dan simpan sebarang panggilan alat yang dikembalikan bersama argumennya. Hasilnya ialah teks atau permintaan panggilan fungsi untuk langkah aplikasi seterusnya; aplikasi yang mengelilinginya bertanggungjawab terhadap pelaksanaan dan penggunaan jawapan yang telah siap.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Tersedia di Ailoot · input &amp; kaedah</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Chat Completions</b> <code>POST /v1/chat/completions</code></summary><dl class=\"method-capabilities\"><div><dt>Input</dt><dd><code>text</code></dd></div><div><dt>Output</dt><dd><code>text</code></dd></div><div><dt>Konteks</dt><dd>262,144 tokens</dd></div><div><dt>Format output</dt><dd>JSON or supported SSE</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Medan</th><th>Jenis</th><th>Keperluan</th><th>Nilai &amp; had</th><th>Lalai</th></tr></thead><tbody><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>string</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Diperlukan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>inclusionai/ling-3.0-flash</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">messages</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>array</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Diperlukan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">stream</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>boolean</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>false</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">max_tokens</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>integer</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Minimum: 1</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">max_completion_tokens</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>integer</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Minimum: 1</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">n</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>integer</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Minimum: 1</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">temperature</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>number</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">top_p</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>number</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">seed</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>integer</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">stop</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code></code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">frequency_penalty</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>number</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">presence_penalty</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>number</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>string</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr></tbody></table></div><details class=\"note-s\" data-method-contract><summary>Parameter kaedah dan kitar hayat</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Kontrak</th></tr></thead><tbody><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>content_type</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>application/json</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>required</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>model, messages</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>optional</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>stream, max_tokens, max_completion_tokens, n, temperature, top_p, seed, stop, frequency_penalty, presence_penalty, user</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>material_types</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>text</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>conditions</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span></td></tr></tbody></table></div><p>Kitar hayat: <code>synchronous_json_or_sse</code></p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Pelan harga</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.</p><dl data-price-conditions><dt>Mod pantas</dt><dd>Tidak dinyatakan <span class=\"note-s\">Apabila mod pantas dipilih</span></dd><dt>Mod flex</dt><dd>Tidak dinyatakan <span class=\"note-s\">Apabila mod flex dipilih</span></dd><dt>Usaha penaakulan maksimum</dt><dd>Tidak dinyatakan <span class=\"note-s\">Apabila usaha penaakulan maksimum menggunakan pelarasan default</span></dd></dl></div><div data-declared-tariff-pricing><h4>Kadar Ailoot</h4><h4>Harga mengikut konfigurasi</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Konfigurasi</th><th>Jadual kadar</th><th>Caj</th></tr></thead><tbody><tr><th data-cell-label=\"Konfigurasi\" scope=\"row\"><span class=\"card-cell-label\">Konfigurasi</span>Kadar asas token</th><td data-cell-label=\"Jadual kadar\"><span class=\"card-cell-label\">Jadual kadar</span>Kadar asas</td><td data-cell-label=\"Caj\"><span class=\"card-cell-label\">Caj</span><div><span>Input · Token input</span> <b>$0.00012</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Output · Token output</span> <b>$0.00036</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Input · Bacaan cache</span> <b>$0.000024</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Input · Penulisan cache</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Input · Penulisan cache, 1 jam</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. <a href=\"/docs/ms/billing/reserve/\">Cara rizab dan penyelesaian caj berfungsi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Contoh permintaan</h3><h4>Ailoot Ultra · Chat Completions</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Pilihan kod\"><button class=\"cg-tab\" id=\"model-code-13-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-13-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl · cipta sekali sahaja dan simpan hasil lengkap</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Salin kod\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-13-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-13-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\nexport AILOOT_API_KEY\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;inclusionai/ling-3.0-flash&quot;, &quot;messages&quot;: [{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Summarize the supplied text in three concise points.&quot;}], &quot;stream&quot;: false}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Terima dan simpan hasil</h3><h4>Ailoot Ultra · Chat Completions</h4><p class=\"note-s\">JSON or supported SSE</p><div class=\"card-result-process\" data-all-results><h5>Simpan semua hasil yang selesai</h5><p class=\"note-s\">Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.</p><p class=\"note-s\">Retain the complete response, all actual results, original identifiers, usage and response headers.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Jika respons asal perlu disemak</h5><p class=\"note-s\">Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.</p></div></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Alias</h3><h4>Alias model</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=inclusionai%2Fling-3.0-flash&amp;route=Ultra&amp;caller=inclusionai%2Fling-3.0-flash&amp;fmt=chat\"><code>inclusionai/ling-3.0-flash</code></a></span></p><h4>Alias laluan kaedah</h4><p class=\"note-s\">Gunakan laluan kaedah yang dipaparkan di atas.</p><h4>ID pemanggil berkaitan</h4><p class=\"note-s\"><a href=\"#m=inclusionai%2Fling-3.0-flash&amp;route=Ultra&amp;caller=inclusionai%2Fling-3.0-flash&amp;fmt=chat\"><code>inclusionai/ling-3.0-flash</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "inclusionai/ling-3.0-flash",
        "fmt": "chat",
        "http": "POST",
        "path": "/v1/chat/completions",
        "request_model_id": "inclusionai/ling-3.0-flash",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|chat|/v1/chat/completions|inclusionai%2Fling-3.0-flash",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": "0.024",
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "example_body": {
              "messages": [
                {
                  "content": "Summarize the supplied text in three concise points.",
                  "role": "user"
                }
              ],
              "model": "inclusionai/ling-3.0-flash",
              "stream": false
            },
            "lifecycle": {
              "cancel_endpoint": null,
              "create": {
                "http": "POST",
                "path": "/v1/chat/completions"
              },
              "mode": "synchronous_json_or_sse",
              "status": [],
              "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
              "family": "text",
              "format": "JSON or supported SSE"
            },
            "parameters": [
              {
                "default_known": false,
                "enum": [
                  "inclusionai/ling-3.0-flash"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "additionalProperties": true,
                  "properties": {
                    "content": {
                      "type": "string"
                    },
                    "role": {
                      "enum": [
                        "user",
                        "assistant",
                        "system",
                        "developer"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "role",
                    "content"
                  ],
                  "type": "object"
                },
                "minItems": 1,
                "name": "messages",
                "required": true,
                "type": "array"
              },
              {
                "default": false,
                "default_known": true,
                "enum_known": false,
                "name": "stream",
                "required": false,
                "type": "boolean"
              },
              {
                "default_known": false,
                "enum_known": false,
                "minimum": 1,
                "name": "max_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "minimum": 1,
                "name": "max_completion_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "minimum": 1,
                "name": "n",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "temperature",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_p",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "seed",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "stop",
                "oneOf": [
                  {
                    "type": "string"
                  },
                  {
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                ],
                "required": false
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "frequency_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "presence_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "string"
              }
            ],
            "request_schema": {
              "additionalProperties": true,
              "properties": {
                "frequency_penalty": {
                  "type": "number"
                },
                "max_completion_tokens": {
                  "minimum": 1,
                  "type": "integer"
                },
                "max_tokens": {
                  "minimum": 1,
                  "type": "integer"
                },
                "messages": {
                  "items": {
                    "additionalProperties": true,
                    "properties": {
                      "content": {
                        "type": "string"
                      },
                      "role": {
                        "enum": [
                          "user",
                          "assistant",
                          "system",
                          "developer"
                        ],
                        "type": "string"
                      }
                    },
                    "required": [
                      "role",
                      "content"
                    ],
                    "type": "object"
                  },
                  "minItems": 1,
                  "type": "array"
                },
                "model": {
                  "enum": [
                    "inclusionai/ling-3.0-flash"
                  ],
                  "type": "string"
                },
                "n": {
                  "minimum": 1,
                  "type": "integer"
                },
                "presence_penalty": {
                  "type": "number"
                },
                "seed": {
                  "type": "integer"
                },
                "stop": {
                  "oneOf": [
                    {
                      "type": "string"
                    },
                    {
                      "items": {
                        "type": "string"
                      },
                      "type": "array"
                    }
                  ]
                },
                "stream": {
                  "default": false,
                  "type": "boolean"
                },
                "temperature": {
                  "type": "number"
                },
                "top_p": {
                  "type": "number"
                },
                "user": {
                  "type": "string"
                }
              },
              "required": [
                "model",
                "messages"
              ],
              "type": "object"
            },
            "schema_template": {
              "body": {
                "messages": [
                  {
                    "content": "Summarize the supplied text in three concise points.",
                    "role": "user"
                  }
                ],
                "model": "inclusionai/ling-3.0-flash",
                "stream": false
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [
              "stream",
              "max_tokens",
              "max_completion_tokens",
              "n",
              "temperature",
              "top_p",
              "seed",
              "stop",
              "frequency_penalty",
              "presence_penalty",
              "user"
            ],
            "required": [
              "model",
              "messages"
            ]
          },
          "lifecycle": {
            "cancel_endpoint": null,
            "create": {
              "http": "POST",
              "path": "/v1/chat/completions"
            },
            "mode": "synchronous_json_or_sse",
            "status": [],
            "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/chat/completions"
          },
          "method_aliases": [],
          "output": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "family": "text",
            "format": "JSON or supported SSE"
          }
        },
        "ctx": 262144,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.00012000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000012",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0.00036000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0.00000036",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": "0.000024000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": "0.000000024",
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"inclusionai/ling-3.0-flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n",
            "label": "curl · create once and retain the complete result",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "chat",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": "0.12",
        "input": [
          "text"
        ],
        "label": "Chat Completions",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "out": "0.36",
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "cache_read_price": "0.000000024",
          "cache_write_1h_price": null,
          "cache_write_price": null,
          "fast_multiplier": null,
          "flex_multiplier": null,
          "image_input_price": null,
          "image_output_price": null,
          "input_price": "0.00000012",
          "intervals": [],
          "max_reasoning_effort_multiplier": null,
          "output_price": "0.00000036",
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.00012000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000012",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0.00036000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0.00000036",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": "0.000024000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": "0.000000024",
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "inclusionai/ling-3.0-flash",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "unit": "token",
        "units": [
          {
            "amount": "0.00012000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000012",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0.00036000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0.00000036",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": "0.000024000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": "0.000000024",
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    }
  ],
  "model_id": "inclusionai/ling-3.0-flash",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "bd3a61a46d12a90b1d7754dafa8128d13c62cab32f94352c58bb756569ccac74",
    "input_html_sha256": "ffb56ef0b975ff52dd66c24d7e1068673cfff739824bc18aecf8c29258eac4e3",
    "private_audit_sha256": "0e0f19861a43247b3c256d2057e56b581c65509bec5389513aacfa7908d34290",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/ms/inclusionai-ling-3-0-flash/",
    "description": "Ling 3.0 Flash ialah model teks inclusionAI untuk perbualan, kerja dokumen dan aliran kerja ejen yang dikawal aplikasi. Ia menerima teks melalui Chat Completions, menyokong pengisytiharan panggilan fungsi dan pemformatan output JSON, serta mempunyai tetingkap konteks 262,144 token yang diterbitkan. Gunakan ID tepat inclusionai/ling-3.0-flash apabila memilih edisi ini.",
    "locale": "ms",
    "md_url": "https://ailoot.ai/models/ms/inclusionai-ling-3-0-flash/index.md",
    "title": "inclusionAI: Ling 3.0 Flash — Ailoot",
    "url": "https://ailoot.ai/models/ms/inclusionai-ling-3-0-flash/"
  },
  "rawModel": {
    "about": "Ling 3.0 Flash ialah model teks-ke-teks daripada inclusionAI. Seni bina yang diterbitkan ialah campuran pakar dengan 124 bilion parameter keseluruhan dan kira-kira 5.1 bilion parameter aktif bagi setiap token. Pilih inclusionai/ling-3.0-flash melalui POST /v1/chat/completions dengan model dan tatasusunan messages. Arahan, bahan sumber dan giliran perbualan terdahulu diletakkan dalam teks mesej.\n\nGunakan prompt terfokus untuk mengubah bahan yang dibekalkan menjadi hasil khusus. Untuk balasan pelanggan, bekalkan soalan pelanggan dan teks produk atau dasar yang mesti digunakan oleh jawapan, kemudian tentukan nada dan langkah seterusnya. Untuk ringkasan dokumen, nyatakan bahagian yang diperlukan, seperti keputusan, persoalan yang belum selesai dan tindakan. Untuk pengekstrakan, tentukan medan yang diminta dan cara mewakili fakta yang tiada dalam sumber. Untuk kerja kod, sertakan fungsi atau petikan fail yang berkaitan dan minta penerangan, cadangan perubahan atau semakan berdasarkan keperluan yang jelas.\n\nTetingkap konteks yang diterbitkan ialah 262,144 token, dengan sehingga 32,768 token pelengkapan disenaraikan untuk model. Peruntukkan teks sumber, arahan, sejarah perbualan dan pelengkapan yang diminta bersama-sama. Gunakan max_tokens untuk menetapkan peruntukan output yang diminta. Dokumen perlu dibekalkan sebagai teks sebenarnya apabila anda mahu jawapan bergantung pada kandungannya; rujuk bahagian yang berkaitan secara jelas apabila permintaan menggabungkan beberapa petikan.\n\nSenarai parameter yang disokong merangkumi tools dan tool_choice untuk panggilan fungsi. Takrifkan fungsi yang tersedia dan penerangan argumennya dalam permintaan. Aplikasi anda mengesahkan panggilan yang dikembalikan, melaksanakan fungsi yang dipilih dan membekalkan hasilnya kepada perbualan sebelum meminta jawapan susulan. Sebagai contoh, aliran kerja sokongan boleh menyediakan carian pesanan dengan skop sempit dan menggunakan fakta yang dikembalikan semasa mendraf balasan. Pastikan hasil alat dilampirkan pada giliran perbualan yang betul supaya model boleh membezakannya daripada permintaan asal pengguna.\n\nModel menyokong response_format untuk output JSON, tanpa penguatkuasaan skema JSON. Nyatakan medan yang dikehendaki dan sahkan objek yang dikembalikan dalam aplikasi anda sebelum menggunakannya. Kawalan lain yang diterbitkan merangkumi temperature, top_p, top_k, min_p, stop, seed serta penalti pengulangan, kekerapan dan kehadiran yang disenaraikan. Senarai parameter turut merangkumi reasoning dan include_reasoning; pilih perwakilan yang disokong daripada spesifikasi permintaan, dan bukannya menganggap nilai daripada edisi yang berbeza.\n\nChat Completions boleh mengembalikan balasan lengkap atau teks berstrim. Untuk balasan lengkap, baca kandungan mesej yang dikembalikan dan simpan choices serta usage balasan. Dengan penstriman, gabungkan delta sebelum menganggap jawapan lengkap, dan simpan sebarang panggilan alat yang dikembalikan bersama argumennya. Hasilnya ialah teks atau permintaan panggilan fungsi untuk langkah aplikasi seterusnya; aplikasi yang mengelilinginya bertanggungjawab terhadap pelaksanaan dan penggunaan jawapan yang telah siap.",
    "alias_of": null,
    "aliases": [],
    "author": "inclusionAI",
    "ctx": 262144,
    "formats": [
      "chat"
    ],
    "id": "inclusionai/ling-3.0-flash",
    "input": [
      "text"
    ],
    "kind": "text",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": "0.024",
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "example_body": {
              "messages": [
                {
                  "content": "Summarize the supplied text in three concise points.",
                  "role": "user"
                }
              ],
              "model": "inclusionai/ling-3.0-flash",
              "stream": false
            },
            "lifecycle": {
              "cancel_endpoint": null,
              "create": {
                "http": "POST",
                "path": "/v1/chat/completions"
              },
              "mode": "synchronous_json_or_sse",
              "status": [],
              "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
              "family": "text",
              "format": "JSON or supported SSE"
            },
            "parameters": [
              {
                "default_known": false,
                "enum": [
                  "inclusionai/ling-3.0-flash"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "additionalProperties": true,
                  "properties": {
                    "content": {
                      "type": "string"
                    },
                    "role": {
                      "enum": [
                        "user",
                        "assistant",
                        "system",
                        "developer"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "role",
                    "content"
                  ],
                  "type": "object"
                },
                "minItems": 1,
                "name": "messages",
                "required": true,
                "type": "array"
              },
              {
                "default": false,
                "default_known": true,
                "enum_known": false,
                "name": "stream",
                "required": false,
                "type": "boolean"
              },
              {
                "default_known": false,
                "enum_known": false,
                "minimum": 1,
                "name": "max_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "minimum": 1,
                "name": "max_completion_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "minimum": 1,
                "name": "n",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "temperature",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_p",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "seed",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "stop",
                "oneOf": [
                  {
                    "type": "string"
                  },
                  {
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                ],
                "required": false
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "frequency_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "presence_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "string"
              }
            ],
            "request_schema": {
              "additionalProperties": true,
              "properties": {
                "frequency_penalty": {
                  "type": "number"
                },
                "max_completion_tokens": {
                  "minimum": 1,
                  "type": "integer"
                },
                "max_tokens": {
                  "minimum": 1,
                  "type": "integer"
                },
                "messages": {
                  "items": {
                    "additionalProperties": true,
                    "properties": {
                      "content": {
                        "type": "string"
                      },
                      "role": {
                        "enum": [
                          "user",
                          "assistant",
                          "system",
                          "developer"
                        ],
                        "type": "string"
                      }
                    },
                    "required": [
                      "role",
                      "content"
                    ],
                    "type": "object"
                  },
                  "minItems": 1,
                  "type": "array"
                },
                "model": {
                  "enum": [
                    "inclusionai/ling-3.0-flash"
                  ],
                  "type": "string"
                },
                "n": {
                  "minimum": 1,
                  "type": "integer"
                },
                "presence_penalty": {
                  "type": "number"
                },
                "seed": {
                  "type": "integer"
                },
                "stop": {
                  "oneOf": [
                    {
                      "type": "string"
                    },
                    {
                      "items": {
                        "type": "string"
                      },
                      "type": "array"
                    }
                  ]
                },
                "stream": {
                  "default": false,
                  "type": "boolean"
                },
                "temperature": {
                  "type": "number"
                },
                "top_p": {
                  "type": "number"
                },
                "user": {
                  "type": "string"
                }
              },
              "required": [
                "model",
                "messages"
              ],
              "type": "object"
            },
            "schema_template": {
              "body": {
                "messages": [
                  {
                    "content": "Summarize the supplied text in three concise points.",
                    "role": "user"
                  }
                ],
                "model": "inclusionai/ling-3.0-flash",
                "stream": false
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [
              "stream",
              "max_tokens",
              "max_completion_tokens",
              "n",
              "temperature",
              "top_p",
              "seed",
              "stop",
              "frequency_penalty",
              "presence_penalty",
              "user"
            ],
            "required": [
              "model",
              "messages"
            ]
          },
          "lifecycle": {
            "cancel_endpoint": null,
            "create": {
              "http": "POST",
              "path": "/v1/chat/completions"
            },
            "mode": "synchronous_json_or_sse",
            "status": [],
            "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/chat/completions"
          },
          "method_aliases": [],
          "output": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "family": "text",
            "format": "JSON or supported SSE"
          }
        },
        "ctx": 262144,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.00012000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000012",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0.00036000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0.00000036",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": "0.000024000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": "0.000000024",
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"inclusionai/ling-3.0-flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n",
            "label": "curl · create once and retain the complete result",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "chat",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": "0.12",
        "input": [
          "text"
        ],
        "label": "Chat Completions",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "out": "0.36",
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "cache_read_price": "0.000000024",
          "cache_write_1h_price": null,
          "cache_write_price": null,
          "fast_multiplier": null,
          "flex_multiplier": null,
          "image_input_price": null,
          "image_output_price": null,
          "input_price": "0.00000012",
          "intervals": [],
          "max_reasoning_effort_multiplier": null,
          "output_price": "0.00000036",
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.00012000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000012",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
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    ],
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    },
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    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
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  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
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          "contract_json_pointer": "/methods/0/rawMethod/contract",
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        }
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      "json_pointers": [
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    "schema": "ailoot-native-model-representations-v1"
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  "schema": "ailoot-static-model-facts-v1",
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  "shortText": "Ling 3.0 Flash ialah model teks inclusionAI untuk perbualan, kerja dokumen dan aliran kerja ejen yang dikawal aplikasi. Ia menerima teks melalui Chat Completions, menyokong pengisytiharan panggilan fungsi dan pemformatan output JSON, serta mempunyai tetingkap konteks 262,144 token yang diterbitkan. Gunakan ID tepat inclusionai/ling-3.0-flash apabila memilih edisi ini.",
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}