When it launched in July 2024, GPT-4o mini was the first model to outperform GPT-4 on LMSYS chat preference while costing less than GPT-3.5 Turbo, a roughly 100x cost reduction versus GPT-4 at comparable quality. It scores 82.0% on MMLU with 128K context and supports text and image input, function calling, and JSON mode at $0.15/$0.60 per million tokens.
GPT-4o mini scores 82.0% on MMLU (5-shot), surpassing GPT-3.5 Turbo (70.0%) by 12 points and approaching GPT-4 (86.4%) territory at less than 1% the cost. On LMSYS chat preference it outperforms GPT-4 despite the 4-point MMLU gap, suggesting stronger conversational quality than raw knowledge scores indicate. It is the highest quality-per-dollar model on the sheet.
Running GPT-4o mini through Telnyx Inference costs $0.15 per million input tokens and $0.60 per million output tokens. Processing 10,000,000 classification tasks at 500 tokens each would cost approximately $3,750, more than 60% cheaper than GPT-3.5 Turbo and roughly 100x cheaper than GPT-4 at comparable chat quality.
Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.
| Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License |
|---|---|---|---|---|---|---|---|
| Telnyx | DeepSeek-V4-Flash-0731 | text generation | English | 1,048,576 | 284.0B | unlisted | mit |
| Telnyx | DeepSeek-V4.1-Flash | text generation | English | 1,048,576 | 552.0B | small | mit |
| gemma-2b-it | text generation | English | 8,192 | 2.5B | small | gemma | |
| gemma-4-26B-A4B-it | text to-text | Multilingual | 262,144 | 25.8B | small | apache-2.0 | |
| gemma-4-31B-it | text generation | Multilingual | 131,072 | 30.7B | small | apache-2.0 | |
| meta-llama | Llama-3.3-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 |
| meta-llama | Meta-Llama-3.1-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 |
| meta-llama | Meta-Llama-3.1-8B-Instruct | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 |
| minimaxai | MiniMax-M2.7 | text generation | Multilingual | 200,000 | 0 | large | minimaxai |
| minimaxai | MiniMax-M3-MXFP8 | text generation | Multilingual | 1,000,000 | 428.0B | small | minimax-community |
| moonshotai | Kimi-K2.5 | text generation | Multilingual | 256,000 | 1.0T | large | modified-mit |
| moonshotai | Kimi-K2.6 | text generation | Multilingual | 262,144 | 1.0T | medium | modified-mit |
| moonshotai | Kimi-K3 | text generation | English | 1,000,000 | 2.8M | large | MIT Modified |
| nvidia | personaplex-7b-v1 | speech to-speech | English | 3,000 | 7.0B | small | nvidia-open-model-license |
| poolside | Laguna-S-2.1 | text generation | English | 1,048,576 | 117.6B | unlisted | OpenMDW-1.1 |
| Qwen | Qwen3-235B-A22B | text generation | English | 32,768 | 235.1B | large | apache-2.0 |
| Telnyx | Qwen3.8-27B | text generation | English | 262,144 | 27.0B | small | Apache 2.0 |
| zai-org | GLM-5.1-FP8 | text generation | Multilingual | 202,752 | 753.9B | large | mit |
| zai-org | GLM-5.2 | text generation | English | 1,000,000 | 753.9B | medium | MIT |
| Telnyx | GLM-5.3 | text generation | English | 1,048,576 | 753.0B | medium | glm-5.3 |
| zai-org | GLM-5.3-Flash | text generation | English | 1,048,576 | 320.0B | small | mit |
| anthropic | claude-haiku-4-5 | text generation | Multilingual | 400,000 | 0 | large | openai |
| gemini-2.5-flash | text generation | Multilingual | 400,000 | 0 | large | openai | |
| gemini-3.7-flash | text generation | Multilingual | 400,000 | 0 | large | openai | |
| groq | gpt-oss-120b | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-4.1 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-4o | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-4o-mini | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.2 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.4 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.4-mini | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.6-luna | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.6-sol | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-live-1 | speech to-speech | Multilingual | 400,000 | 0 | large | openai |
Check out our helpful tools to help get you started.
GPT-4o is the full multimodal model with maximum capability, while GPT-4o mini is a smaller, faster variant optimized for cost-efficient production tasks. GPT-4o mini scores 82% on MMLU while costing a fraction of GPT-4o.
GPT-4o mini is available in ChatGPT's free tier with usage limits. Through the API, it requires a paid account with pricing at $0.15 per million input tokens, accessible through OpenAI and inference providers.
GPT-4o is a natively multimodal model that processes text, images, and audio jointly, while GPT-4 is text-only (with separate vision capabilities). GPT-4o is also faster and cheaper than GPT-4, making it the recommended successor for most applications.
"4o mini" refers to the small, fast variant of GPT-4o (the "o" stands for "omni" indicating multimodal capability). It is ChatGPT's default model for everyday tasks where speed and cost efficiency are prioritized over maximum reasoning depth.
Yes, GPT-4o mini is the default free model in ChatGPT. It is also available through the API at $0.15 per million input tokens, making it one of OpenAI's most affordable options.
GPT-4.1 mini currently offers the strongest performance among OpenAI's mini models, followed by GPT-4o mini and GPT-5 mini. The best choice depends on your task: GPT-4.1 mini leads on structured output, while GPT-5 mini is stronger on reasoning.
GPT-4o mini is priced at $0.15 per million input tokens and $0.60 per million output tokens through the API. Infrastructure providers offer access with additional benefits like co-located inference for lower latency.
