> ## Documentation Index > Fetch the complete documentation index at: https://docs.getlilac.com/llms.txt > Use this file to discover all available pages before exploring further. # Supported Models > Browse all models available on Lilac including Kimi K2.6, GLM 5.2, Gemma 4, and MiniMax M3 with context lengths, capabilities, and per-token pricing. ## Available Models Lilac currently supports the following models. We're actively adding more — [reach out](mailto:contact@getlilac.com) if there's a model you'd like to see. | Model | Model ID | Context Length | Quantization | Input Price | Cache Read Price | Output Price | | ---------- | ----------------------- | ---------------- | ------------ | ----------------- | ----------------- | ----------------- | | Kimi K2.6 | `moonshotai/kimi-k2.6` | 262,144 tokens | INT4 | \$0.70 / M tokens | \$0.20 / M tokens | \$3.50 / M tokens | | GLM 5.2 | `zai-org/glm-5.2` | 524,288 tokens | FP8/NVFP4 | \$0.90 / M tokens | \$0.27 / M tokens | \$3.00 / M tokens | | Gemma 4 | `google/gemma-4-31b-it` | 262,100 tokens | FP8 | \$0.11 / M tokens | — | \$0.35 / M tokens | | MiniMax M3 | `minimaxai/minimax-m3` | 1,048,576 tokens | FP8 | \$0.28 / M tokens | \$0.05 / M tokens | \$1.10 / M tokens | **Cache read** is the rate for repeated input tokens served from cache. It's billed at a lower rate than standard input tokens on supported models. Models that don't support cached input tokens are marked with `—`. More models are coming soon. Request a model by emailing [contact@getlilac.com](mailto:contact@getlilac.com). *** ## Kimi K2.6 Moonshot AI's flagship multimodal reasoning model. 1T total parameters (32B activated) with a Mixture-of-Experts architecture. Model card, benchmarks, and deployment guides. ### Capabilities | Capability | Status | Details | | ------------------------ | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Text input** | Supported | Chat, instructions, system prompts | | **Image input** | Supported | Native multimodal — pass images via `image_url` in messages | | **Text output** | Supported | Completions, structured JSON, tool calls | | **Reasoning (thinking)** | On by default | Chain-of-thought returned in `reasoning` field. Kimi K2.6's Moonshot chat template honors `chat_template_kwargs: {"thinking": false}` (the `enable_thinking` key is ignored here). For forward compatibility across models, see the [Reasoning section](/inference/chat-completions#reasoning). | | **Tool calling** | Supported | Function definitions with automatic argument extraction | | **Structured output** | Supported | `response_format` with `json_object` or `json_schema` | ### Recommended Parameters From the [Kimi K2.6 model card](https://huggingface.co/moonshotai/Kimi-K2.6): | Mode | Temperature | Top P | | ---------------------- | ----------- | ------ | | Thinking (default) | `1.0` | `0.95` | | Instant (thinking off) | `0.6` | `0.95` | ### Vision Kimi K2.6 natively supports image inputs. Pass images as base64 data URIs or URLs in the `content` array: ```python theme={null} response = client.chat.completions.create( model="moonshotai/kimi-k2.6", messages=[ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg", "detail": "auto" } } ] } ], ) ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "moonshotai/kimi-k2.6", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg", "detail": "auto"}} ] } ] }' ``` You can also pass base64-encoded images: ```python theme={null} import base64 with open("image.png", "rb") as f: image_b64 = base64.b64encode(f.read()).decode() response = client.chat.completions.create( model="moonshotai/kimi-k2.6", messages=[ { "role": "user", "content": [ {"type": "text", "text": "What's in this image?"}, { "type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_b64}"} } ] } ], ) ``` *** ## GLM 5.2 Z.ai's GLM 5.2 is a frontier-scale MoE reasoning and coding model for long-horizon agentic work. Lilac serves GLM 5.2 with a 524k-token context window, tool calling, structured output, and configurable reasoning effort. Model card, benchmarks, and deployment guides. ### Capabilities | Capability | Status | Details | | ------------------------ | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Text input** | Supported | Chat, instructions, system prompts | | **Text output** | Supported | Completions, structured JSON, tool calls | | **Image input** | Not supported | GLM 5.2 is text-only | | **Reasoning (thinking)** | On by default | Chain-of-thought returned in `reasoning` field. GLM 5.2 honors `chat_template_kwargs: {"enable_thinking": false}` to disable thinking, and supports a `reasoning_effort` control with two levels — `max` and `high`. See the [Reasoning section](/inference/chat-completions#reasoning) for forward-compatible toggles. | | **Tool calling** | Supported | Function definitions with automatic argument extraction — strong performance on agentic tasks | | **Structured output** | Supported | `response_format` with `json_object` or `json_schema` | ### Reasoning effort GLM 5.2 exposes two reasoning effort levels: * `high` — the default when reasoning is enabled. Good balance of quality, latency, and token usage for most coding and reasoning tasks. * `max` — highest-quality reasoning for long-horizon agentic and complex problem-solving tasks. Higher latency and token usage than `high`. Notes: * Disable thinking entirely with `chat_template_kwargs.enable_thinking: false`. When thinking is disabled, `reasoning_effort` has no effect. * `reasoning_effort` can be sent either as a top-level field (OpenAI-style) or inside `chat_template_kwargs` (vLLM extra-body form). Both are accepted. ### Example requests ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "zai-org/glm-5.2", "messages": [ {"role": "user", "content": "Plan a migration for this service."} ], "reasoning_effort": "high" }' ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "zai-org/glm-5.2", "messages": [ {"role": "user", "content": "Plan a migration for this service."} ], "chat_template_kwargs": { "reasoning_effort": "high" } }' ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "zai-org/glm-5.2", "messages": [ {"role": "user", "content": "Answer directly: what is 2+2?"} ], "chat_template_kwargs": { "enable_thinking": false } }' ``` ```python theme={null} response = client.chat.completions.create( model="zai-org/glm-5.2", messages=[ {"role": "user", "content": "Plan a migration for this service."} ], extra_body={ "chat_template_kwargs": { "reasoning_effort": "high" } }, ) ``` ### Thinking controls **Preserved thinking is off by default.** GLM 5.2's effective default on Lilac is to clear previous assistant thinking blocks between turns. To preserve thinking across turns, use `chat_template_kwargs.clear_thinking: false` on Lilac. This is equivalent to vLLM-native chat-template control. Lilac does not currently consume Z.ai's top-level `thinking` object — in particular, top-level `thinking.clear_thinking` is ignored. For conceptual background, see [Z.ai's preserved thinking docs](https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking). ```json theme={null} { "model": "zai-org/glm-5.2", "messages": [...], "chat_template_kwargs": { "clear_thinking": false } } ``` ```json theme={null} { "model": "zai-org/glm-5.2", "messages": [...], "chat_template_kwargs": { "enable_thinking": true, "reasoning_effort": "max", "clear_thinking": false } } ``` ```json theme={null} { "model": "zai-org/glm-5.2", "messages": [...], "reasoning_effort": "none" } ``` *** ## Gemma 4 Google's open-weight multimodal model. 31B parameters with native support for text, image, and video inputs. 262K context window with FP8 precision. Released under the Gemma license. Model card, benchmarks, and deployment guides. ### Capabilities | Capability | Status | Details | | ------------------------ | -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Text input** | Supported | Chat, instructions, system prompts | | **Image input** | Supported | Native multimodal — pass images via `image_url` in messages | | **Video input** | Supported | Pass video frames as a sequence of images | | **Text output** | Supported | Completions, structured JSON | | **Reasoning (thinking)** | Off by default | Chain-of-thought returned in `reasoning` field when enabled. Gemma 4's chat template honors `chat_template_kwargs: {"enable_thinking": true}` (the `thinking` key is ignored here). Unlike Kimi K2.6 and GLM 5.2, thinking is **off by default** — you must opt in. See the [Reasoning section](/inference/chat-completions#reasoning) for the forward-compatible form. | | **Tool calling** | Supported | Function definitions with automatic argument extraction | | **Structured output** | Supported | `response_format` with `json_object` or `json_schema` | **Gemma 4 chain-of-thought may leak into `content`.** vLLM's Gemma 4 reasoning parser can fail to populate the `reasoning` field when special tokens are stripped before the parser runs — see [vllm-project/vllm#38855](https://github.com/vllm-project/vllm/issues/38855). When reasoning is enabled, clients that require a clean split should post-process by treating text inside `<|channel|>thought ... <|channel|>` markers as reasoning. **Structured output caveat.** On current vLLM builds, combining `--reasoning-parser gemma4` with `enable_thinking: false` can silently disable xgrammar-backed structured output — see [vllm-project/vllm#39130](https://github.com/vllm-project/vllm/issues/39130). If you rely on `response_format: json_schema` with Gemma 4, leave thinking enabled or validate output client-side. ### Enabling reasoning Gemma 4 is the only model in the catalog where reasoning is off by default. To turn it on, use the forward-compatible form recommended in the [Reasoning section](/inference/chat-completions#reasoning): ```python theme={null} response = client.chat.completions.create( model="google/gemma-4-31b-it", messages=[{"role": "user", "content": "Prove there are infinitely many primes."}], extra_body={ "chat_template_kwargs": { "thinking": True, "enable_thinking": True, } }, ) ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "google/gemma-4-31b-it", "messages": [ {"role": "user", "content": "Prove there are infinitely many primes."} ], "chat_template_kwargs": { "thinking": true, "enable_thinking": true } }' ``` ### Vision Gemma 4 natively supports image inputs. Pass images as base64 data URIs or URLs in the `content` array: ```python theme={null} response = client.chat.completions.create( model="google/gemma-4-31b-it", messages=[ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg", "detail": "auto" } } ] } ], ) ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "google/gemma-4-31b-it", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg", "detail": "auto"}} ] } ] }' ``` ### Video Gemma 4 can process video by accepting a sequence of frames as images. Extract frames from your video and pass them as multiple `image_url` entries: ```python theme={null} import base64 import cv2 # Extract frames from video video = cv2.VideoCapture("video.mp4") frames = [] while video.isOpened(): ret, frame = video.read() if not ret: break _, buffer = cv2.imencode(".jpg", frame) frames.append(base64.b64encode(buffer).decode()) video.release() # Sample frames to fit context window sampled = frames[::len(frames) // 8][:8] response = client.chat.completions.create( model="google/gemma-4-31b-it", messages=[ { "role": "user", "content": [ {"type": "text", "text": "Describe what happens in this video."}, *[ { "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{frame}"} } for frame in sampled ] ] } ], ) ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "google/gemma-4-31b-it", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Describe what happens in this video."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,FRAME_1_BASE64"}}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,FRAME_2_BASE64"}} ] } ] }' ``` *** ## MiniMax M3 MiniMax M3 is a frontier MoE model for coding, agents, and long-context reasoning. Lilac serves MiniMax M3 with a 1M-token context window, tool calling, structured output, and per-request thinking modes. Model card, benchmarks, and deployment guides. ### Capabilities | Capability | Status | Details | | ------------------------ | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- | | **Text input** | Supported | Chat, instructions, system prompts | | **Text output** | Supported | Completions, structured JSON, tool calls | | **Image input** | Supported | Native multimodal — pass images via `image_url` in messages | | **Video input** | Supported | Pass video frames as a sequence of images | | **Reasoning (thinking)** | Supported | Per-request thinking modes via `chat_template_kwargs.thinking_mode`. See the [Reasoning section](/inference/chat-completions#reasoning) for details. | | **Tool calling** | Supported | Function definitions with automatic argument extraction | | **Structured output** | Supported | `response_format` with `json_object` or `json_schema` | ### Limits | Limit | Value | | -------------- | ---------------- | | Context length | 1,048,576 tokens | ### Thinking modes MiniMax M3 uses a single `thinking_mode` control instead of a boolean toggle: | Mode | Behavior | | ---------- | ---------------------------------------------------------------------------- | | `adaptive` | Default when unset. The model decides whether to think based on the request. | | `enabled` | Always think. Best for complex reasoning and multi-step agents. | | `disabled` | No thinking. Best for latency-sensitive requests. | `thinking_mode` is passed inside `chat_template_kwargs`: ```json theme={null} { "chat_template_kwargs": { "thinking_mode": "adaptive" } } ``` ### Example requests ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "minimaxai/minimax-m3", "messages": [ {"role": "user", "content": "Analyze this large codebase summary."} ], "chat_template_kwargs": { "thinking_mode": "adaptive" } }' ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "minimaxai/minimax-m3", "messages": [ {"role": "user", "content": "Solve this multi-step planning problem."} ], "chat_template_kwargs": { "thinking_mode": "enabled" } }' ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "minimaxai/minimax-m3", "messages": [ {"role": "user", "content": "Give me a concise answer."} ], "chat_template_kwargs": { "thinking_mode": "disabled" } }' ``` ```python theme={null} response = client.chat.completions.create( model="minimaxai/minimax-m3", messages=[ {"role": "user", "content": "Solve this multi-step planning problem."} ], extra_body={ "chat_template_kwargs": { "thinking_mode": "enabled" } }, ) ``` ### Vision MiniMax M3 natively supports image inputs. Pass images as base64 data URIs or URLs in the `content` array: ```python theme={null} response = client.chat.completions.create( model="minimaxai/minimax-m3", messages=[ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, { "type": "image_url", "image_url": { "url": "https://example.com/image.jpg", "detail": "auto" } } ] } ], ) ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "minimaxai/minimax-m3", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image."}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg", "detail": "auto"}} ] } ] }' ``` ### Video MiniMax M3 can process video by accepting a sequence of frames as images. Extract frames from your video and pass them as multiple `image_url` entries: ```python theme={null} import base64 import cv2 # Extract frames from video video = cv2.VideoCapture("video.mp4") frames = [] while video.isOpened(): ret, frame = video.read() if not ret: break _, buffer = cv2.imencode(".jpg", frame) frames.append(base64.b64encode(buffer).decode()) video.release() # Sample frames to fit context window sampled = frames[::len(frames) // 8][:8] response = client.chat.completions.create( model="minimaxai/minimax-m3", messages=[ { "role": "user", "content": [ {"type": "text", "text": "Describe what happens in this video."}, *[ { "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{frame}"} } for frame in sampled ] ] } ], ) ``` ```bash theme={null} curl https://api.getlilac.com/v1/chat/completions \ -H "Authorization: Bearer your-lilac-api-key" \ -H "Content-Type: application/json" \ -d '{ "model": "minimaxai/minimax-m3", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Describe what happens in this video."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,FRAME_1_BASE64"}}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,FRAME_2_BASE64"}} ] } ] }' ``` *** ## Listing Models via API ```python theme={null} from openai import OpenAI client = OpenAI( base_url="https://api.getlilac.com/v1", api_key="your-lilac-api-key", ) models = client.models.list() for model in models: print(model.id) ``` ```javascript theme={null} import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://api.getlilac.com/v1", apiKey: "your-lilac-api-key", }); const models = await client.models.list(); for await (const model of models) { console.log(model.id); } ``` ```bash theme={null} curl https://api.getlilac.com/v1/models \ -H "Authorization: Bearer your-lilac-api-key" ```