GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves on GLM-5.2 in coding and in the balance between performance and token efficiency.

GLM-5.3

GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead.

GLM-5.3-Flash

Kimi K3 is Moonshot AI's 2.8T-parameter open-weight multimodal reasoning model. Built for complex coding, knowledge work, and long-horizon agentic workflows, it excels at navigating large repositories, calling tools, debugging, and iterating on images, logs, tests, and runtime feedback. Its architecture relies on KDA and Attention Residuals for computational efficiency.

Kimi-K3

Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total. It is suited for coding, research, complex reasoning, and agentic workflows.

Qwen3.8-2.4T-A95B

DeepSeek-V4-Pro-0813 is the official release of DeepSeek-V4-Pro, superseding the preview version, with greatly enhanced agentic capabilities and performance improvements that are especially pronounced in production environments. It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached.

DeepSeek-V4-Pro-0813

DeepSeek-V4-Flash-0731 is the official release of DeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities. DeepSeek-V4-Flash-0731 outperforms DeepSeek-V4-Pro (Preview) on benchmarks listed below despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available.

DeepSeek-V4-Flash-0731

GLM-5.2 is Z-AI's latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a **solid 1M-token context**.

GLM-5.2

Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6.

Kimi-K2.7-Code

Nemotron 3 Ultra is built for, frontier reasoning, orchestration, coding agents, deep research, and complex enterprise workflows. It delivers up to 5x faster inference and up to 30% lower cost for agentic workloads while supporting up to 1M token context.

NVIDIA-Nemotron-3-Ultra-550B-A55B

DeepSeek V4 Flash is an efficiency-focused MoE model with 284B total parameters (13B active) and a 1M-token context window. It's tuned for fast inference and high-throughput use cases while still holding up on reasoning and coding tasks.

DeepSeek-V4-Flash

DeepSeek V4 Pro is an MoE model with 1.6T total parameters (49B active) and a 1M-token context window. It's built for advanced reasoning, coding, and long-running agent tasks, and performs well on knowledge, math, and software engineering benchmarks.

DeepSeek-V4-Pro

Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.

Kimi-K2.6

MiMo-V2.5-Pro is an open-source Mixture-of-Experts (MoE) language model with 1.02T total parameters and 42B active parameters. It utilizes the hybrid attention architecture and 3-layers Multi-Token Prediction (MTP) introduced in [MiMo-V2-Flash](https://github.com/XiaomiMiMo/MiMo-V2-Flash).

MiMo-V2.5-Pro

Qwen3.6-35B-A3B is Alibaba's latest flagship Mixture-of-Experts model, with 35B total parameters and only 3B activated per token (256 experts, 8 routed + 1 shared). Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.

Qwen3.6-35B-A3B

GLM-5.1 is Z-AI's next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).

GLM-5.1

Qwen3.5-397B-A17B is Alibaba's most capable Qwen3.5 model, a Mixture-of-Experts architecture with 397B total parameters and 17B activated per token. It features a 262K token context window (extensible to 1M with YaRN), thinking/reasoning mode, tool calling with MCP integration, and support for 201 languages. Sets state-of-the-art results on reasoning, coding, math, and multimodal benchmarks.

Qwen3.5-397B-A17B

Efficient, MoE variant of Gemma 4. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input and generating text output.

gemma-4-26B-A4B-it

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input and generating text output.

gemma-4-31B-it

NVIDIA Nemotron 3 Super is a hybrid Mixture-of-Experts (MoE) model engineered for highest compute efficiency and accuracy in multi-agent applications and specialized agentic systems. It is optimized to run many collaborating agents per application on a single GPU, delivering high accuracy for reasoning, tool use, and instruction following.

NVIDIA-Nemotron-3-Super-120B-A12B

  Qwen3-TTS is an advanced text-to-speech model by Alibaba's Qwen team, delivering stable, expressive, and low-latency speech generation across 10 languages.                                                                                                                                                                                                                                                                                                                                           Key capabilities:                                                                                                                                                                                                                                  - 9 preset voices — Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee — covering diverse genders, ages, and accents                                                                                                              - Voice cloning — clone any voice from a short (~3s) audio sample via the voice_id parameter   - Instruction control — adjust tone, emotion, and speaking style with natural language (e.g. "speak slowly and calmly", "excited tone")   - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese   - Streaming support — real-time PCM streaming with ~97ms first-byte latency   - Multiple output formats — WAV, MP3, FLAC, PCM    Built on a 1.7B parameter architecture using discrete multi-codebook language modeling for end-to-end speech synthesis without cascading errors. Uses a custom 12Hz acoustic tokenizer that preserves paralinguistic information and   environmental audio details.

Qwen3-TTS

● Qwen3-TTS-VoiceDesign is a voice design variant of Qwen3-TTS by Alibaba's Qwen team. Instead of selecting from preset voices, you describe the voice you want in natural language — and the model generates speech in that voice.                                                                                                                                                                                                                                                                     Key capabilities:                                                                                                                                                                                                                                  - Natural language voice control — describe any voice with free text (e.g. "a deep male voice with a calm, authoritative presence", "a young cheerful female with a warm and friendly tone")   - 10 languages — English, Chinese, Japanese, Korean, German, French, Russian, Spanish, Italian, Portuguese                                                                                                                                         - Streaming support — real-time PCM streaming   - Multiple output formats — WAV, MP3, FLAC, PCM    Built on the same 1.7B parameter architecture as Qwen3-TTS, using discrete multi-codebook language modeling and a custom 12Hz acoustic tokenizer for high-quality end-to-end speech synthesis.

Qwen3-TTS-VoiceDesign

The latest flagship model in the Qwen family. State-of-the-art results across a comprehensive suite of benchmarks — including knowledge, reasoning, coding, instruction following, human preference alignment, agent tasks, and multilingual understanding.

Qwen3-Max

The latest flagship reasoning model in the Qwen3 family. Further enhanced by multiple innovations like adaptive tool-use and advanced test-time scaling techniques

Qwen3-Max-Thinking

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

DeepSeek-V3.2

The fastest model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

FLUX-2-klein-4b

The best quality-to-latency ratio, production apps model of the Flux 2 family. Frontier visual intelligence — state-of-the-art image generation and editing from Black Forest Labs

FLUX-2-klein-9b

claude

Claude

deepseek

DeepSeek

flux

Flux

gemini

Gemini

kimi

Kimi

llama

Llama

mistral

Mistral

nemotron

Nemotron

qwen

Qwen

BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval), and Multi-Granularity (handling inputs from short queries to long documents). It achieves state-of-the-art retrieval performance across diverse benchmarks while maintaining a single model for multiple retrieval modes. Inputs to this endpoint are truncated to 512 tokens.

DeepInfra supports the OpenAI embeddings API.
The following creates an embedding vector representing the input text

```bash
curl "https://api.deepinfra.com/v1/openai/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "BAAI/bge-m3-multi",
    "encoding_format": "float"
  }'
```

which will return something similar to

```json
{
  "object":"list",
  "data":[
    {
      "object": "embedding",
      "index":0,
      "embedding":[
        -0.010480394586920738,
        -0.0026091758627444506
        ...
        0.031979579478502274,
        0.02021978422999382
      ]
    }
  ],
  "model": "BAAI/bge-m3-multi",
  "usage": {
    "prompt_tokens":12,
    "total_tokens":12
  }
}
```


You can use the official OpenAI python client to create embedding vectors

```python
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/v1/openai",
)

input = "The food was delicious and the waiter...", # or an array ["hello", "world"]

embeddings = openai.embeddings.create(
  model="BAAI/bge-m3-multi",
  input=input,
  encoding_format="float"
)

if isinstance(input, str):
    print(embeddings.data[0].embedding)
else:
    for i in range(len(input)):
        print(embeddings.data[i].embedding)

print(embeddings.usage.prompt_tokens)

# [0.0241, 0.5, 1.0, ..., 0.3123, 0.743564]
# 42
```


You can use the official OpenAI Node.js client to create embedding vectors

```javascript
import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: 'https://api.deepinfra.com/v1/openai',
  apiKey: "$DEEPINFRA_TOKEN",
});

const input = "The quick brown fox jumped over the lazy dog" // or an array ["hello", "world"]

async function main() {
  const embedding = await openai.embeddings.create({
    model: "BAAI/bge-m3-multi",
    input: input,
    encoding_format: "float",
  });

  // check if input is a string or array
  if (typeof input === "string") {
    console.log(embedding.data[0].embedding);
  } else {
    console.log(embedding.data.map((data) => data.embedding));
  }

  console.log(embedding.usage.prompt_tokens);
}

main();

// [0.0241, 0.5, 1.0, ..., 0.3123, 0.743564]
// 42
```


You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -d '{"inputs": ["I like chocolate"]}'  \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/BAAI/bge-m3-multi'
```

which will give you back something similar to:

```json
{
  "input_tokens": 42,
  "embeddings": [
    [
      0.0,
      0.5,
      1.0
    ],
    [
      1.0,
      0.5,
      0.0
    ]
  ],
  "sparse": [
    [
      0.0,
      0.0,
      1.0
    ],
    [
      0.0,
      0.6,
      0.0
    ]
  ],
  "colbert": [
    [
      [
        0.5,
        0.1,
        1.0
      ],
      [
        0.3,
        0.6,
        0.5
      ]
    ],
    [
      [
        0.3,
        0.6,
        0.5
      ]
    ]
  ],
  "embedding_jsons": [
    "[0.0, 0.5, 1.0]",
    "[1.0, 0.5, 0.0]"
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0,
    "output_length": 0
  }
}

```


You can use our command-line tool [deepctl](/docs/advanced/deepctl) to run
inferences:

```bash
deepctl infer \
    -m 'BAAI/bge-m3-multi'  \
    -i 'inputs=["I like chocolate"]'
```

which will give you back something similar to:

```json
{
  "input_tokens": 42,
  "embeddings": [
    [
      0.0,
      0.5,
      1.0
    ],
    [
      1.0,
      0.5,
      0.0
    ]
  ],
  "sparse": [
    [
      0.0,
      0.0,
      1.0
    ],
    [
      0.0,
      0.6,
      0.0
    ]
  ],
  "colbert": [
    [
      [
        0.5,
        0.1,
        1.0
      ],
      [
        0.3,
        0.6,
        0.5
      ]
    ],
    [
      [
        0.3,
        0.6,
        0.5
      ]
    ]
  ],
  "embedding_jsons": [
    "[0.0, 0.5, 1.0]",
    "[1.0, 0.5, 0.0]"
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0,
    "output_length": 0
  }
}

```


We recommend using our NodeJS client https://github.com/deepinfra/deepinfra-node.

You can install it with

```bash
npm install deepinfra
```

and then

```javascript
import { Embeddings } from "deepinfra";

const DEEPINFRA_API_KEY = "$DEEPINFRA_TOKEN";
const MODEL = "BAAI/bge-m3-multi";

const main = async () => {
  const client = new Embeddings(MODEL, DEEPINFRA_API_KEY);
  const body = {
    inputs: [
      "What is the capital of France?",
      "What is the capital of Germany?",
      "What is the capital of Italy?",
    ],
  };
  const output = await client.generate(body);
  console.log(output.embeddings[0]);
};

main();
```



### 1. Only Dense Embeddings
This request returns only dense embeddings (`dense: true`, others `false`).

```bash
curl https://api.deepinfra.com/v1/inference/BAAI/bge-m3-multi \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -d '{
    "inputs": "I like chocolate",
    "dense": true,
    "sparse": false,
    "colbert": false
  }'
```

#### Response Schema
```json
{
  "request_id": "RJ9X08LaqG3Q4tl5fp2eM2cp",
  "inference_status": {
    "status": "succeeded",
    "runtime_ms": 34,
    "cost": 0.0,
    "tokens_generated": null,
    "tokens_input": null
  },
  "input_tokens": 13,
  "embeddings": [
    [0.0088958740234375, 0.01234, ...]
  ],
  "embedding_jsons": [
    "[0.0088958740234375, 0.01234, ...]",
  ],
  "sparse": null,
  "colbert": null
}
```

---

### 2. Only Sparse Embeddings
This request returns sparse embeddings (`dense: false`, `sparse: true`, `colbert: false`).
Corpus list can be found [here](https://gist.github.com/ovuruska/d3dd8e8508410725f3cbf8a721a5cb1d).


```bash
curl https://api.deepinfra.com/v1/inference/BAAI/bge-m3-multi \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -d '{
    "inputs": ["I like chocolate", "Another sequence"],
    "dense": false,
    "sparse": true,
    "colbert": false
  }'
```






#### Response Schema
```json
{
  "request_id": "RJ9X08LaqG3Q4tl5fp2eM2cp",
  "inference_status": {
    "status": "succeeded",
    "runtime_ms": 34,
    "cost": 0.0,
    "tokens_generated": null,
    "tokens_input": null
  },
  "input_tokens": 13,
  "sparse": [
    [0.0, 0.0, 1.0, ...],
    [0.0, 0.6, 0.0, ...]
  ],
  "embedding_jsons": null,
  "colbert": null
}
```

---

### 3. Dense, Sparse, and ColBERT Embeddings
This request returns all embedding types (`dense: true`, `sparse: true`, `colbert: true`).

```bash
curl https://api.deepinfra.com/v1/inference/BAAI/bge-m3-multi \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -d '{
    "inputs": "I like chocolate",
    "dense": true,
    "sparse": true,
    "colbert": true
  }'
```

#### Response Schema
```json
{
  "request_id": "RJ9X08LaqG3Q4tl5fp2eM2cp",
  "inference_status": {
    "status": "succeeded",
    "runtime_ms": 34,
    "cost": 0.0,
    "tokens_generated": null,
    "tokens_input": null
  },
  "input_tokens": 13,
  "embeddings": [
    [0.0088958740234375, 0.01234, ...]
  ],
  "embeddings_jsons": [
    "[0.0088958740234375, 0.01234, ...]"
  ],
  "sparse": [
    [0.0, 0.0, 1.0, ...]
  ],
  "colbert": [
    [
      [0.5, 0.1, 1.0],
      [0.3, 0.6, 0.5],
      ...
    ]
  ]
}
```



inputs

Dense is a set of low-dimensional vectors where every token in the input is represented by a fully populated embedding derived from a neural model.

dense

Sparse is a collection of high-dimensional vectors where each word in the input is assigned a lexical weight, with most values being zero.

sparse

Colbert is a system of contextualized vectors where every token in the input is represented by its own BERT-derived embedding.

colbert

whether to normalize the computed embeddings

normalize

The webhook to call when inference is done, by default you will get the output in the response of your inference request

webhook

Colbert

Dense

Inputs

Normalize

Sparse

Webhook

MultiModalEmbeddingsIn

estimated cost billed for the request in USD

Cost

Output Length

Runtime Ms

Status

Tokens Generated

Tokens Input

InferenceReplyStatus

ColBert vectors for each token in each input sequence

Embedding Jsons

the computed dense embeddings for each input sequence

Embeddings

Object containing the status of the inference request

The number of input tokens in the request

Input Tokens

Request Id

lexical weights computed embeddings for each input sequencein sparse format

MultiModalEmbeddingsOut

The service tier used for processing the request. 'priority' processes the request with higher priority (premium rate); 'flex' processes it at lower priority for a discount, served only when spare capacity exists and may be retried/timed out under load. Both apply only to models that support the respective tier. For compatibility, 'auto' is treated as 'priority' and 'standard_only' as 'default'.

service_tier

If true, the request is rejected immediately with HTTP 429 when the model has no spare capacity, instead of waiting in the queue. Opt-in; the default (false) keeps standard queueing behavior.

fail_fast

model

text or multimodal content to embed. Each item is either a string, or a list of content parts ({"type":"text"} / {"type":"image_url"}) for multimodal embedding models such as nvidia/llama-nemotron-embed-vl-1b-v2.

input

Role hint for asymmetric retrieval models: 'query' embeds a search query, 'passage'/'document' embeds a document. Controls the query:/passage: prefix on VL embedding models; ignored by symmetric models.

input_type

encoding_format

The number of dimensions in the embedding. If not provided, the model's default will be used.If provided bigger than model's default, the embedding will be padded with zeros.

bge-m3-multi

OpenAI-compatible HTTP API

Input fields

Input Schema

Output Schema