> ## Documentation Index
> Fetch the complete documentation index at: https://docs.eachlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Available Models

> The full menu. 300+ LLM models, pricing per million tokens, context windows. Pick your favorite.

Grab any model ID from the tables below and pass it as the `model` parameter. Format: `provider/model-name`.

```python theme={"dark"}
response = client.chat.completions.create(
    model="openai/gpt-4o",  # any model ID from the tables below
    messages=[{"role": "user", "content": "Hello!"}]
)
```

***

## OpenAI

| Model ID                            | Name                       | Input    | Output   | Context   |
| ----------------------------------- | -------------------------- | -------- | -------- | --------- |
| `openai/gpt-5.4`                    | GPT-5.4                    | \$2.50   | \$15.00  | 1,050,000 |
| `openai/gpt-5.4-pro`                | GPT-5.4 Pro                | \$30.00  | \$180.00 | 1,050,000 |
| `openai/gpt-5.2`                    | GPT-5.2                    | \$1.75   | \$14.00  | 400,000   |
| `openai/gpt-5.2-pro`                | GPT-5.2 Pro                | \$21.00  | \$168.00 | 400,000   |
| `openai/gpt-5.2-codex`              | GPT-5.2 Codex              | \$1.75   | \$14.00  | 400,000   |
| `openai/gpt-5.1`                    | GPT-5.1                    | \$1.25   | \$10.00  | 400,000   |
| `openai/gpt-5.1-codex`              | GPT-5.1 Codex              | \$1.25   | \$10.00  | 400,000   |
| `openai/gpt-5.1-codex-max`          | GPT-5.1 Codex Max          | \$1.25   | \$10.00  | 400,000   |
| `openai/gpt-5.1-codex-mini`         | GPT-5.1 Codex Mini         | \$0.25   | \$2.00   | 400,000   |
| `openai/gpt-5`                      | GPT-5                      | \$1.25   | \$10.00  | 400,000   |
| `openai/gpt-5-pro`                  | GPT-5 Pro                  | \$15.00  | \$120.00 | 400,000   |
| `openai/gpt-5-codex`                | GPT-5 Codex                | \$1.25   | \$10.00  | 400,000   |
| `openai/gpt-5-mini`                 | GPT-5 Mini                 | \$0.25   | \$2.00   | 400,000   |
| `openai/gpt-5-nano`                 | GPT-5 Nano                 | \$0.05   | \$0.40   | 400,000   |
| `openai/gpt-5-image`                | GPT-5 Image                | \$10.00  | \$10.00  | 400,000   |
| `openai/gpt-5-image-mini`           | GPT-5 Image Mini           | \$2.50   | \$2.00   | 400,000   |
| `openai/gpt-4.1`                    | GPT-4.1                    | \$2.00   | \$8.00   | 1,047,576 |
| `openai/gpt-4.1-mini`               | GPT-4.1 Mini               | \$0.40   | \$1.60   | 1,047,576 |
| `openai/gpt-4.1-nano`               | GPT-4.1 Nano               | \$0.10   | \$0.40   | 1,047,576 |
| `openai/gpt-4o`                     | GPT-4o                     | \$2.50   | \$10.00  | 128,000   |
| `openai/gpt-4o-mini`                | GPT-4o Mini                | \$0.15   | \$0.60   | 128,000   |
| `openai/gpt-4o-search-preview`      | GPT-4o Search Preview      | \$2.50   | \$10.00  | 128,000   |
| `openai/gpt-4o-mini-search-preview` | GPT-4o Mini Search Preview | \$0.15   | \$0.60   | 128,000   |
| `openai/gpt-4o-audio-preview`       | GPT-4o Audio               | \$2.50   | \$10.00  | 128,000   |
| `openai/gpt-4-turbo`                | GPT-4 Turbo                | \$10.00  | \$30.00  | 128,000   |
| `openai/gpt-4`                      | GPT-4                      | \$30.00  | \$60.00  | 8,191     |
| `openai/gpt-3.5-turbo`              | GPT-3.5 Turbo              | \$0.50   | \$1.50   | 16,385    |
| `openai/o4-mini`                    | o4 Mini                    | \$1.10   | \$4.40   | 200,000   |
| `openai/o4-mini-high`               | o4 Mini High               | \$1.10   | \$4.40   | 200,000   |
| `openai/o3`                         | o3                         | \$2.00   | \$8.00   | 200,000   |
| `openai/o3-pro`                     | o3 Pro                     | \$20.00  | \$80.00  | 200,000   |
| `openai/o3-mini`                    | o3 Mini                    | \$1.10   | \$4.40   | 200,000   |
| `openai/o3-deep-research`           | o3 Deep Research           | \$10.00  | \$40.00  | 200,000   |
| `openai/o1`                         | o1                         | \$15.00  | \$60.00  | 200,000   |
| `openai/o1-pro`                     | o1 Pro                     | \$150.00 | \$600.00 | 200,000   |

## Anthropic

| Model ID                               | Name                         | Input   | Output  | Context   |
| -------------------------------------- | ---------------------------- | ------- | ------- | --------- |
| `anthropic/claude-opus-4.6`            | Claude Opus 4.6              | \$5.00  | \$25.00 | 1,000,000 |
| `anthropic/claude-opus-4.5`            | Claude Opus 4.5              | \$5.00  | \$25.00 | 200,000   |
| `anthropic/claude-opus-4.1`            | Claude Opus 4.1              | \$15.00 | \$75.00 | 200,000   |
| `anthropic/claude-opus-4`              | Claude Opus 4                | \$15.00 | \$75.00 | 200,000   |
| `anthropic/claude-sonnet-4.6`          | Claude Sonnet 4.6            | \$3.00  | \$15.00 | 1,000,000 |
| `anthropic/claude-sonnet-4.5`          | Claude Sonnet 4.5            | \$3.00  | \$15.00 | 1,000,000 |
| `anthropic/claude-sonnet-4`            | Claude Sonnet 4              | \$3.00  | \$15.00 | 200,000   |
| `anthropic/claude-3.7-sonnet`          | Claude 3.7 Sonnet            | \$3.00  | \$15.00 | 200,000   |
| `anthropic/claude-3.7-sonnet:thinking` | Claude 3.7 Sonnet (thinking) | \$3.00  | \$15.00 | 200,000   |
| `anthropic/claude-3.5-sonnet`          | Claude 3.5 Sonnet            | \$6.00  | \$30.00 | 200,000   |
| `anthropic/claude-haiku-4.5`           | Claude Haiku 4.5             | \$1.00  | \$5.00  | 200,000   |
| `anthropic/claude-3.5-haiku`           | Claude 3.5 Haiku             | \$0.80  | \$4.00  | 200,000   |
| `anthropic/claude-3-haiku`             | Claude 3 Haiku               | \$0.25  | \$1.25  | 200,000   |

## Google

| Model ID                           | Name                   | Input  | Output  | Context   |
| ---------------------------------- | ---------------------- | ------ | ------- | --------- |
| `google/gemini-3.1-pro-preview`    | Gemini 3.1 Pro Preview | \$2.00 | \$12.00 | 1,048,576 |
| `google/gemini-3-pro-preview`      | Gemini 3 Pro Preview   | \$2.00 | \$12.00 | 1,048,576 |
| `google/gemini-3-flash-preview`    | Gemini 3 Flash Preview | \$0.50 | \$3.00  | 1,048,576 |
| `google/gemini-2.5-pro`            | Gemini 2.5 Pro         | \$1.25 | \$10.00 | 1,048,576 |
| `google/gemini-2.5-pro-preview`    | Gemini 2.5 Pro Preview | \$1.25 | \$10.00 | 1,048,576 |
| `google/gemini-2.5-flash`          | Gemini 2.5 Flash       | \$0.30 | \$2.50  | 1,048,576 |
| `google/gemini-2.5-flash-lite`     | Gemini 2.5 Flash Lite  | \$0.10 | \$0.40  | 1,048,576 |
| `google/gemini-2.0-flash-001`      | Gemini 2.0 Flash       | \$0.10 | \$0.40  | 1,048,576 |
| `google/gemini-2.0-flash-lite-001` | Gemini 2.0 Flash Lite  | \$0.07 | \$0.30  | 1,048,576 |
| `google/gemma-3-27b-it`            | Gemma 3 27B            | \$0.04 | \$0.15  | 128,000   |
| `google/gemma-3-12b-it`            | Gemma 3 12B            | \$0.04 | \$0.13  | 131,072   |
| `google/gemma-3-4b-it`             | Gemma 3 4B             | \$0.04 | \$0.08  | 131,072   |
| `google/gemma-3n-e4b-it`           | Gemma 3n 4B            | \$0.02 | \$0.04  | 32,768    |

## Meta (Llama)

| Model ID                                   | Name                    | Input  | Output | Context   |
| ------------------------------------------ | ----------------------- | ------ | ------ | --------- |
| `meta-llama/llama-4-maverick`              | Llama 4 Maverick        | \$0.15 | \$0.60 | 1,048,576 |
| `meta-llama/llama-4-scout`                 | Llama 4 Scout           | \$0.08 | \$0.30 | 327,680   |
| `meta-llama/llama-3.3-70b-instruct`        | Llama 3.3 70B Instruct  | \$0.10 | \$0.32 | 131,072   |
| `meta-llama/llama-3.1-405b-instruct`       | Llama 3.1 405B Instruct | \$4.00 | \$4.00 | 131,000   |
| `meta-llama/llama-3.1-70b-instruct`        | Llama 3.1 70B Instruct  | \$0.40 | \$0.40 | 131,072   |
| `meta-llama/llama-3.1-8b-instruct`         | Llama 3.1 8B Instruct   | \$0.02 | \$0.05 | 16,384    |
| `meta-llama/llama-3.2-11b-vision-instruct` | Llama 3.2 11B Vision    | \$0.05 | \$0.05 | 131,072   |
| `meta-llama/llama-3.2-3b-instruct`         | Llama 3.2 3B Instruct   | \$0.05 | \$0.34 | 80,000    |
| `meta-llama/llama-3.2-1b-instruct`         | Llama 3.2 1B Instruct   | \$0.03 | \$0.20 | 60,000    |

## DeepSeek

| Model ID                                 | Name                 | Input  | Output | Context |
| ---------------------------------------- | -------------------- | ------ | ------ | ------- |
| `deepseek/deepseek-v3.2`                 | DeepSeek V3.2        | \$0.25 | \$0.40 | 163,840 |
| `deepseek/deepseek-chat-v3.1`            | DeepSeek V3.1        | \$0.15 | \$0.75 | 32,768  |
| `deepseek/deepseek-chat`                 | DeepSeek V3          | \$0.32 | \$0.89 | 163,840 |
| `deepseek/deepseek-r1`                   | DeepSeek R1          | \$0.70 | \$2.50 | 64,000  |
| `deepseek/deepseek-r1-0528`              | DeepSeek R1 0528     | \$0.45 | \$2.15 | 163,840 |
| `deepseek/deepseek-r1-distill-llama-70b` | R1 Distill Llama 70B | \$0.70 | \$0.80 | 131,072 |
| `deepseek/deepseek-r1-distill-qwen-32b`  | R1 Distill Qwen 32B  | \$0.29 | \$0.29 | 32,768  |

## xAI (Grok)

| Model ID                | Name             | Input  | Output  | Context   |
| ----------------------- | ---------------- | ------ | ------- | --------- |
| `x-ai/grok-4`           | Grok 4           | \$3.00 | \$15.00 | 256,000   |
| `x-ai/grok-4-fast`      | Grok 4 Fast      | \$0.20 | \$0.50  | 2,000,000 |
| `x-ai/grok-4.1-fast`    | Grok 4.1 Fast    | \$0.20 | \$0.50  | 2,000,000 |
| `x-ai/grok-3`           | Grok 3           | \$3.00 | \$15.00 | 131,072   |
| `x-ai/grok-3-mini`      | Grok 3 Mini      | \$0.30 | \$0.50  | 131,072   |
| `x-ai/grok-code-fast-1` | Grok Code Fast 1 | \$0.20 | \$1.50  | 256,000   |

## Mistral

| Model ID                                   | Name                  | Input  | Output | Context |
| ------------------------------------------ | --------------------- | ------ | ------ | ------- |
| `mistralai/mistral-large-2512`             | Mistral Large 3       | \$0.50 | \$1.50 | 262,144 |
| `mistralai/mistral-large`                  | Mistral Large         | \$2.00 | \$6.00 | 128,000 |
| `mistralai/mistral-medium-3.1`             | Mistral Medium 3.1    | \$0.40 | \$2.00 | 131,072 |
| `mistralai/mistral-medium-3`               | Mistral Medium 3      | \$0.40 | \$2.00 | 131,072 |
| `mistralai/mistral-small-3.2-24b-instruct` | Mistral Small 3.2 24B | \$0.06 | \$0.18 | 131,072 |
| `mistralai/mistral-small-3.1-24b-instruct` | Mistral Small 3.1 24B | \$0.35 | \$0.56 | 128,000 |
| `mistralai/mistral-nemo`                   | Mistral Nemo          | \$0.02 | \$0.04 | 131,072 |
| `mistralai/codestral-2508`                 | Codestral 2508        | \$0.30 | \$0.90 | 256,000 |
| `mistralai/devstral-2512`                  | Devstral 2            | \$0.40 | \$2.00 | 262,144 |
| `mistralai/devstral-medium`                | Devstral Medium       | \$0.40 | \$2.00 | 131,072 |
| `mistralai/devstral-small`                 | Devstral Small 1.1    | \$0.10 | \$0.30 | 131,072 |
| `mistralai/pixtral-large-2411`             | Pixtral Large         | \$2.00 | \$6.00 | 131,072 |
| `mistralai/mistral-saba`                   | Saba                  | \$0.20 | \$0.60 | 32,768  |

## Qwen

| Model ID                           | Name                  | Input  | Output | Context   |
| ---------------------------------- | --------------------- | ------ | ------ | --------- |
| `qwen/qwen3-coder`                 | Qwen3 Coder 480B A35B | \$0.22 | \$1.00 | 262,144   |
| `qwen/qwen3-coder-flash`           | Qwen3 Coder Flash     | \$0.20 | \$0.97 | 1,000,000 |
| `qwen/qwen3-coder-plus`            | Qwen3 Coder Plus      | \$0.65 | \$3.25 | 1,000,000 |
| `qwen/qwen3-coder-next`            | Qwen3 Coder Next      | \$0.12 | \$0.75 | 262,144   |
| `qwen/qwen3-max`                   | Qwen3 Max             | \$1.20 | \$6.00 | 262,144   |
| `qwen/qwen3-max-thinking`          | Qwen3 Max Thinking    | \$0.78 | \$3.90 | 262,144   |
| `qwen/qwen3-235b-a22b`             | Qwen3 235B A22B       | \$0.45 | \$1.82 | 131,072   |
| `qwen/qwen3-32b`                   | Qwen3 32B             | \$0.08 | \$0.24 | 40,960    |
| `qwen/qwen3-14b`                   | Qwen3 14B             | \$0.06 | \$0.24 | 40,960    |
| `qwen/qwen3-8b`                    | Qwen3 8B              | \$0.05 | \$0.40 | 40,960    |
| `qwen/qwen3.5-397b-a17b`           | Qwen3.5 397B A17B     | \$0.39 | \$2.34 | 262,144   |
| `qwen/qwen3.5-122b-a10b`           | Qwen3.5 122B A10B     | \$0.26 | \$2.08 | 262,144   |
| `qwen/qwen3.5-27b`                 | Qwen3.5 27B           | \$0.20 | \$1.56 | 262,144   |
| `qwen/qwen3.5-flash-02-23`         | Qwen3.5 Flash         | \$0.10 | \$0.40 | 1,000,000 |
| `qwen/qwen3.5-plus-02-15`          | Qwen3.5 Plus          | \$0.26 | \$1.56 | 1,000,000 |
| `qwen/qwen-max`                    | Qwen Max              | \$1.04 | \$4.16 | 32,768    |
| `qwen/qwen-plus`                   | Qwen Plus             | \$0.40 | \$1.20 | 1,000,000 |
| `qwen/qwen-turbo`                  | Qwen Turbo            | \$0.03 | \$0.13 | 131,072   |
| `qwen/qwq-32b`                     | QwQ 32B               | \$0.15 | \$0.40 | 32,768    |
| `qwen/qwen-2.5-72b-instruct`       | Qwen2.5 72B Instruct  | \$0.12 | \$0.39 | 32,768    |
| `qwen/qwen-2.5-coder-32b-instruct` | Qwen2.5 Coder 32B     | \$0.20 | \$0.20 | 32,768    |

## Amazon

| Model ID                 | Name         | Input  | Output  | Context   |
| ------------------------ | ------------ | ------ | ------- | --------- |
| `amazon/nova-2-lite-v1`  | Nova 2 Lite  | \$0.30 | \$2.50  | 1,000,000 |
| `amazon/nova-premier-v1` | Nova Premier | \$2.50 | \$12.50 | 1,000,000 |
| `amazon/nova-pro-v1`     | Nova Pro     | \$0.80 | \$3.20  | 300,000   |
| `amazon/nova-lite-v1`    | Nova Lite    | \$0.06 | \$0.24  | 300,000   |
| `amazon/nova-micro-v1`   | Nova Micro   | \$0.04 | \$0.14  | 128,000   |

## Cohere

| Model ID                        | Name        | Input  | Output  | Context |
| ------------------------------- | ----------- | ------ | ------- | ------- |
| `cohere/command-a`              | Command A   | \$2.50 | \$10.00 | 256,000 |
| `cohere/command-r-plus-08-2024` | Command R+  | \$2.50 | \$10.00 | 128,000 |
| `cohere/command-r-08-2024`      | Command R   | \$0.15 | \$0.60  | 128,000 |
| `cohere/command-r7b-12-2024`    | Command R7B | \$0.04 | \$0.15  | 128,000 |

## MoonshotAI (Kimi)

| Model ID                      | Name             | Input  | Output | Context |
| ----------------------------- | ---------------- | ------ | ------ | ------- |
| `moonshotai/kimi-k2.5`        | Kimi K2.5        | \$0.45 | \$2.20 | 262,144 |
| `moonshotai/kimi-k2-0905`     | Kimi K2 0905     | \$0.40 | \$2.00 | 131,072 |
| `moonshotai/kimi-k2`          | Kimi K2          | \$0.55 | \$2.20 | 131,000 |
| `moonshotai/kimi-k2-thinking` | Kimi K2 Thinking | \$0.47 | \$2.00 | 131,072 |

## MiniMax

| Model ID               | Name         | Input  | Output | Context   |
| ---------------------- | ------------ | ------ | ------ | --------- |
| `minimax/minimax-m2.5` | MiniMax M2.5 | \$0.29 | \$1.20 | 196,608   |
| `minimax/minimax-m2.1` | MiniMax M2.1 | \$0.27 | \$0.95 | 196,608   |
| `minimax/minimax-m2`   | MiniMax M2   | \$0.26 | \$1.00 | 196,608   |
| `minimax/minimax-m1`   | MiniMax M1   | \$0.40 | \$2.20 | 1,000,000 |
| `minimax/minimax-01`   | MiniMax 01   | \$0.20 | \$1.10 | 1,000,192 |

## NVIDIA

| Model ID                                   | Name                    | Input  | Output | Context |
| ------------------------------------------ | ----------------------- | ------ | ------ | ------- |
| `nvidia/llama-3.3-nemotron-super-49b-v1.5` | Nemotron Super 49B V1.5 | \$0.10 | \$0.40 | 131,072 |
| `nvidia/llama-3.1-nemotron-70b-instruct`   | Nemotron 70B Instruct   | \$1.20 | \$1.20 | 131,072 |
| `nvidia/nemotron-3-nano-30b-a3b`           | Nemotron 3 Nano 30B     | \$0.05 | \$0.20 | 262,144 |
| `nvidia/nemotron-nano-12b-v2-vl`           | Nemotron Nano 12B VL    | \$0.20 | \$0.60 | 131,072 |
| `nvidia/nemotron-nano-9b-v2`               | Nemotron Nano 9B        | \$0.04 | \$0.16 | 131,072 |

## Perplexity

| Model ID                         | Name                | Input  | Output  | Context |
| -------------------------------- | ------------------- | ------ | ------- | ------- |
| `perplexity/sonar-pro`           | Sonar Pro           | \$3.00 | \$15.00 | 200,000 |
| `perplexity/sonar-pro-search`    | Sonar Pro Search    | \$3.00 | \$15.00 | 200,000 |
| `perplexity/sonar-reasoning-pro` | Sonar Reasoning Pro | \$2.00 | \$8.00  | 128,000 |
| `perplexity/sonar-deep-research` | Sonar Deep Research | \$2.00 | \$8.00  | 128,000 |
| `perplexity/sonar`               | Sonar               | \$1.00 | \$1.00  | 127,072 |

## Z.ai (GLM)

| Model ID             | Name          | Input  | Output | Context |
| -------------------- | ------------- | ------ | ------ | ------- |
| `z-ai/glm-5`         | GLM 5         | \$0.80 | \$2.56 | 202,752 |
| `z-ai/glm-4.7`       | GLM 4.7       | \$0.30 | \$1.40 | 202,752 |
| `z-ai/glm-4.7-flash` | GLM 4.7 Flash | \$0.06 | \$0.40 | 202,752 |
| `z-ai/glm-4.6`       | GLM 4.6       | \$0.39 | \$1.90 | 204,800 |
| `z-ai/glm-4.5`       | GLM 4.5       | \$0.60 | \$2.20 | 131,072 |
| `z-ai/glm-4.5-air`   | GLM 4.5 Air   | \$0.13 | \$0.85 | 131,072 |

## ByteDance (Seed)

| Model ID                        | Name           | Input  | Output | Context |
| ------------------------------- | -------------- | ------ | ------ | ------- |
| `bytedance-seed/seed-1.6`       | Seed 1.6       | \$0.25 | \$2.00 | 262,144 |
| `bytedance-seed/seed-1.6-flash` | Seed 1.6 Flash | \$0.07 | \$0.30 | 262,144 |
| `bytedance-seed/seed-2.0-mini`  | Seed 2.0 Mini  | \$0.10 | \$0.40 | 262,144 |

## Baidu (ERNIE)

| Model ID                           | Name                   | Input  | Output | Context |
| ---------------------------------- | ---------------------- | ------ | ------ | ------- |
| `baidu/ernie-4.5-300b-a47b`        | ERNIE 4.5 300B         | \$0.28 | \$1.10 | 123,000 |
| `baidu/ernie-4.5-21b-a3b`          | ERNIE 4.5 21B          | \$0.07 | \$0.28 | 120,000 |
| `baidu/ernie-4.5-21b-a3b-thinking` | ERNIE 4.5 21B Thinking | \$0.07 | \$0.28 | 131,072 |
| `baidu/ernie-4.5-vl-424b-a47b`     | ERNIE 4.5 VL 424B      | \$0.42 | \$1.25 | 123,000 |
| `baidu/ernie-4.5-vl-28b-a3b`       | ERNIE 4.5 VL 28B       | \$0.14 | \$0.56 | 30,000  |

## Inception (Mercury)

| Model ID                  | Name          | Input  | Output | Context |
| ------------------------- | ------------- | ------ | ------ | ------- |
| `inception/mercury-2`     | Mercury 2     | \$0.25 | \$0.75 | 128,000 |
| `inception/mercury`       | Mercury       | \$0.25 | \$0.75 | 128,000 |
| `inception/mercury-coder` | Mercury Coder | \$0.25 | \$0.75 | 128,000 |

## Other Providers

| Model ID                               | Name                      | Input  | Output  | Context   |
| -------------------------------------- | ------------------------- | ------ | ------- | --------- |
| `ai21/jamba-large-1.7`                 | AI21 Jamba Large 1.7      | \$2.00 | \$8.00  | 256,000   |
| `writer/palmyra-x5`                    | Writer Palmyra X5         | \$0.60 | \$6.00  | 1,040,000 |
| `upstage/solar-pro-3`                  | Upstage Solar Pro 3       | \$0.15 | \$0.60  | 128,000   |
| `inflection/inflection-3-productivity` | Inflection 3 Productivity | \$2.50 | \$10.00 | 8,000     |
| `microsoft/phi-4`                      | Microsoft Phi 4           | \$0.06 | \$0.14  | 16,384    |
| `tencent/hunyuan-a13b-instruct`        | Tencent Hunyuan A13B      | \$0.14 | \$0.57  | 131,072   |
| `xiaomi/mimo-v2-flash`                 | Xiaomi MiMo V2 Flash      | \$0.09 | \$0.29  | 262,144   |
| `stepfun/step-3.5-flash`               | StepFun Step 3.5 Flash    | \$0.10 | \$0.30  | 256,000   |
| `prime-intellect/intellect-3`          | INTELLECT-3               | \$0.20 | \$1.10  | 131,072   |
| `ibm-granite/granite-4.0-h-micro`      | IBM Granite 4.0 Micro     | \$0.02 | \$0.11  | 131,000   |

<Note>
  Pricing is per million tokens. Prices may change, so check your [dashboard](https://www.eachlabs.ai) for the latest rates.
</Note>