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https://github.com/HeyPuter/puter.git
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Add z.ai chat completions (#2874)
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ProgrammerIn-wonderland
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commit
7cd762afa2
@@ -20,6 +20,7 @@ import { OpenAiResponsesChatProvider } from './providers/openai/OpenAiChatRespon
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import { OpenRouterProvider } from './providers/openrouter/OpenRouterProvider.js';
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import { TogetherAIProvider } from './providers/together/TogetherAIProvider.js';
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import { XAIProvider } from './providers/xai/XAIProvider.js';
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import { ZAIProvider } from './providers/zai/ZAIProvider.js';
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import type {
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IChatCompleteResult,
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IChatModel,
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@@ -588,6 +589,18 @@ export class ChatCompletionDriver extends PuterDriver {
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);
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}
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const zai = providers['zai'];
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const zaiKey = readKey(zai);
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if (zaiKey) {
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this.#providers['zai'] = new ZAIProvider(
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{
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apiKey: zaiKey,
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apiBaseUrl: zai?.apiBaseUrl as string | undefined,
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},
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metering,
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);
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}
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const openrouter = providers['openrouter'];
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const openrouterKey = readKey(openrouter);
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if (openrouterKey) {
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@@ -0,0 +1,220 @@
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/*
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* Copyright (C) 2024-present Puter Technologies Inc.
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*
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* This file is part of Puter.
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*
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* Puter is free software: you can redistribute it and/or modify
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* it under the terms of the GNU Affero General Public License as published
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* by the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU Affero General Public License for more details.
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*
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* You should have received a copy of the GNU Affero General Public License
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* along with this program. If not, see <https://www.gnu.org/licenses/>.
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*/
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import { OpenAI } from 'openai';
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import { ChatCompletionCreateParams } from 'openai/resources/index.js';
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import { Context } from '../../../../core/context.js';
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import type { MeteringService } from '../../../../services/metering/MeteringService.js';
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import type { IChatProvider, ICompleteArguments } from '../../types.js';
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import * as OpenAIUtil from '../../utils/OpenAIUtil.js';
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import { ZAI_MODELS } from './models.js';
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type ZAIConfig = {
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apiBaseUrl?: string;
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apiKey: string;
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};
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type ZAICustomParams = {
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do_sample?: boolean;
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request_id?: string;
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response_format?: unknown;
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stop?: string[];
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thinking?: {
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type?: 'enabled' | 'disabled';
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clear_thinking?: boolean;
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};
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tool_stream?: boolean;
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user_id?: string;
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};
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const asRecord = (value: unknown): Record<string, unknown> =>
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value && typeof value === 'object' && !Array.isArray(value)
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? (value as Record<string, unknown>)
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: {};
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export class ZAIProvider implements IChatProvider {
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#openai: OpenAI;
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#meteringService: MeteringService;
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#defaultModel = 'glm-5.1';
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constructor(config: ZAIConfig, meteringService: MeteringService) {
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this.#openai = new OpenAI({
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apiKey: config.apiKey,
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baseURL: config.apiBaseUrl ?? 'https://api.z.ai/api/paas/v4',
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});
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this.#meteringService = meteringService;
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}
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getDefaultModel() {
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return this.#defaultModel;
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}
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models() {
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return ZAI_MODELS;
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}
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list() {
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const modelIds: string[] = [];
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for (const model of this.models()) {
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modelIds.push(model.id);
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if (model.aliases) {
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modelIds.push(...model.aliases);
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}
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}
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return modelIds;
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}
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async complete(
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params: ICompleteArguments,
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): ReturnType<IChatProvider['complete']> {
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const {
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custom,
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max_tokens,
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stream,
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temperature,
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tools,
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tool_choice,
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top_p,
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} = params;
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let { messages, model } = params;
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const actor = Context.get('actor');
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const availableModels = this.models();
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const modelUsed =
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availableModels.find((m) =>
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[m.id, ...(m.aliases || [])].includes(model),
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) || availableModels.find((m) => m.id === this.getDefaultModel())!;
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messages = await OpenAIUtil.process_input_messages(messages);
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messages = messages.map((message) => {
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delete message.cache_control;
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return message;
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});
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const customParams = asRecord(custom) as ZAICustomParams;
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const userId =
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customParams.user_id ??
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(actor?.user?.id
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? `puter-${actor.user.id}${actor.app?.uid ? `-${actor.app.uid}` : ''}`.slice(
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0,
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128,
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)
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: undefined);
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const completionParams: ChatCompletionCreateParams = {
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messages,
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model: modelUsed.id,
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...(tools ? { tools } : {}),
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...(tool_choice !== undefined ? { tool_choice } : {}),
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...(max_tokens !== undefined ? { max_tokens } : {}),
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...(temperature !== undefined ? { temperature } : {}),
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...(top_p !== undefined ? { top_p } : {}),
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...(customParams.do_sample !== undefined
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? { do_sample: customParams.do_sample }
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: {}),
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...(customParams.request_id
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? { request_id: customParams.request_id }
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: {}),
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...(customParams.response_format
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? { response_format: customParams.response_format }
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: {}),
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...(customParams.stop ? { stop: customParams.stop } : {}),
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...(customParams.thinking
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? { thinking: customParams.thinking }
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: {}),
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...(customParams.tool_stream !== undefined
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? { tool_stream: customParams.tool_stream }
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: {}),
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...(userId ? { user_id: userId } : {}),
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stream: !!stream,
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...(stream
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? {
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stream_options: { include_usage: true },
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}
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: {}),
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} as ChatCompletionCreateParams;
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const completion =
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await this.#openai.chat.completions.create(completionParams);
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const result = await OpenAIUtil.handle_completion_output({
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usage_calculator: ({ usage }) => {
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const trackedUsage = usage
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? OpenAIUtil.extractMeteredUsage(usage)
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: {
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prompt_tokens: 0,
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completion_tokens: 0,
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cached_tokens: 0,
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};
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const costsOverrideFromModel = Object.fromEntries(
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Object.entries(trackedUsage).map(([key, value]) => {
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return [key, value * Number(modelUsed.costs[key] ?? 0)];
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}),
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);
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this.#meteringService.utilRecordUsageObject(
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trackedUsage,
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actor,
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`zai:${modelUsed.id}`,
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costsOverrideFromModel,
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);
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return trackedUsage;
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},
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stream,
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completion,
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});
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this.#normalizeReasoningContent(result);
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return result;
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}
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checkModeration(
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_text: string,
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): ReturnType<IChatProvider['checkModeration']> {
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throw new Error('Method not implemented.');
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}
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#normalizeReasoningContent(
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result: Awaited<ReturnType<IChatProvider['complete']>>,
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) {
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if (!('message' in result) || !result.message) return;
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const message = result.message as Record<string, unknown>;
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if (
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message.reasoning === undefined &&
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message.reasoning_content !== undefined
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) {
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message.reasoning = message.reasoning_content;
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}
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delete message.reasoning_content;
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if (!Array.isArray(message.content)) return;
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for (const contentPart of message.content) {
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const part = asRecord(contentPart);
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if (
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part.reasoning === undefined &&
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part.reasoning_content !== undefined
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) {
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part.reasoning = part.reasoning_content;
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}
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delete part.reasoning_content;
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}
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}
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}
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@@ -0,0 +1,192 @@
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import type { IChatModel } from '../../types.js';
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const CENTS_PER_USD = 100;
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const MTOK = 1_000_000;
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const K = 1_000;
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const usdPerMToken = (
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inputUsd: number,
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outputUsd: number,
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cachedInputUsd = 0,
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) => ({
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tokens: MTOK,
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prompt_tokens: inputUsd * CENTS_PER_USD,
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completion_tokens: outputUsd * CENTS_PER_USD,
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cached_tokens: cachedInputUsd * CENTS_PER_USD,
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});
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const textModel = (
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id: string,
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name: string,
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context: number,
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maxTokens: number,
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costs: IChatModel['costs'],
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): IChatModel => ({
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puterId: `zai:zai/${id}`,
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id,
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name,
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aliases: [`zai/${id}`],
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modalities: { input: ['text'], output: ['text'] },
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open_weights: false,
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tool_call: true,
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context,
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max_tokens: maxTokens,
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costs_currency: 'usd-cents',
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input_cost_key: 'prompt_tokens',
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output_cost_key: 'completion_tokens',
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costs,
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});
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const visionModel = (
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id: string,
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name: string,
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context: number,
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maxTokens: number,
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costs: IChatModel['costs'],
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): IChatModel => ({
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puterId: `zai:zai/${id}`,
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id,
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name,
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aliases: [`zai/${id}`],
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modalities: { input: ['text', 'image', 'video', 'file'], output: ['text'] },
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open_weights: false,
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tool_call: true,
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context,
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max_tokens: maxTokens,
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costs_currency: 'usd-cents',
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input_cost_key: 'prompt_tokens',
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output_cost_key: 'completion_tokens',
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costs,
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});
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// Hardcoded from https://docs.z.ai/api-reference/llm/chat-completion and
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// https://docs.z.ai/guides/overview/pricing.
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export const ZAI_MODELS: IChatModel[] = [
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textModel(
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'glm-5.1',
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'GLM-5.1',
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200 * K,
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128 * K,
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usdPerMToken(1.4, 4.4, 0.26),
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),
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textModel('glm-5', 'GLM-5', 200 * K, 128 * K, usdPerMToken(1, 3.2, 0.2)),
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textModel(
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'glm-5-turbo',
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'GLM-5-Turbo',
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200 * K,
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128 * K,
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usdPerMToken(1.2, 4, 0.24),
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),
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textModel(
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'glm-4.7',
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'GLM-4.7',
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200 * K,
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128 * K,
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usdPerMToken(0.6, 2.2, 0.11),
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),
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textModel(
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'glm-4.7-flashx',
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'GLM-4.7-FlashX',
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200 * K,
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128 * K,
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usdPerMToken(0.07, 0.4, 0.01),
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),
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textModel(
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'glm-4.7-flash',
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'GLM-4.7-Flash',
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200 * K,
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128 * K,
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usdPerMToken(0, 0, 0),
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),
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textModel(
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'glm-4.6',
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'GLM-4.6',
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200 * K,
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128 * K,
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usdPerMToken(0.6, 2.2, 0.11),
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),
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textModel(
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'glm-4.5',
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'GLM-4.5',
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128 * K,
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96 * K,
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usdPerMToken(0.6, 2.2, 0.11),
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),
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textModel(
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'glm-4.5-x',
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'GLM-4.5-X',
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128 * K,
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96 * K,
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usdPerMToken(2.2, 8.9, 0.45),
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),
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textModel(
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'glm-4.5-air',
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'GLM-4.5-Air',
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128 * K,
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96 * K,
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usdPerMToken(0.2, 1.1, 0.03),
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),
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textModel(
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'glm-4.5-airx',
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'GLM-4.5-AirX',
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128 * K,
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96 * K,
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usdPerMToken(1.1, 4.5, 0.22),
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),
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textModel(
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'glm-4.5-flash',
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'GLM-4.5-Flash',
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128 * K,
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96 * K,
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usdPerMToken(0, 0, 0),
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),
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textModel(
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'glm-4-32b-0414-128k',
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'GLM-4-32B-0414-128K',
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128 * K,
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16 * K,
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usdPerMToken(0.1, 0.1, 0),
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),
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visionModel(
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'glm-5v-turbo',
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'GLM-5V-Turbo',
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200 * K,
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128 * K,
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usdPerMToken(1.2, 4, 0.24),
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),
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visionModel(
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'glm-4.6v',
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'GLM-4.6V',
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128 * K,
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32 * K,
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usdPerMToken(0.3, 0.9, 0.05),
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),
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visionModel(
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'glm-4.6v-flashx',
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'GLM-4.6V-FlashX',
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128 * K,
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32 * K,
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usdPerMToken(0.04, 0.4, 0.004),
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),
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visionModel(
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'glm-4.6v-flash',
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'GLM-4.6V-Flash',
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128 * K,
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32 * K,
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usdPerMToken(0, 0, 0),
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),
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visionModel(
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'glm-4.5v',
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'GLM-4.5V',
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128 * K,
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16 * K,
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usdPerMToken(0.6, 1.8, 0.11),
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),
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visionModel(
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'autoglm-phone-multilingual',
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'AutoGLM-Phone-Multilingual',
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4 * K,
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4 * K,
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usdPerMToken(0, 0, 0),
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),
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];
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