165 models
Ranked by Design Arena: gamedev
1003.0

Mercury 2 is an extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). Instead of generating tokens sequentially, Mercury 2 produces and refines multiple tokens in parallel, achieving...

inception/mercury-2 128K context $0.25/M input $0.75/M output
997.0

Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for...

moonshotai/kimi-k2 131.072K context $0.57/M input $2.3/M output
993.0

For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...

openai/gpt-4.1-nano:batch 1.04758M context $0.05/M input $0.2/M output
993.0

Tiny GPT-4.1 option for classification, routing, and very high-volume tasks

openai/gpt-4.1-nano 2025-04-14 1.04758M context $0.1/M input $0.4/M output
20 providers
990.0

Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/news/codestral-25-08)

mistralai/codestral-2508:batch 256K context $0.15/M input $0.45/M output
990.0

Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/news/codestral-25-08)

mistralai/codestral-2508 256K context $0.3/M input $0.9/M output
980.0

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...

qwen/qwen3-235b-a22b-thinking-2507 131.072K context $0.23/M input $2.3/M output
974.0

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...

qwen/qwen3-235b-a22b-2507 262.144K context $0.087/M input $0.35/M output
971.0

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

mistralai/ministral-3b-2512 131.072K context $0.1/M input $0.1/M output
950.0

Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...

qwen/qwen3-235b-a22b 131.072K context $0.455/M input $1.82/M output
929.0

GPT-4o ("o" for "omni") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...

openai/gpt-4o:batch 128K context $1.25/M input $5/M output
Tools 929.0

Omni-era GPT for multimodal chat, practical coding, and general assistants

openai/gpt-4o 2024-05-13 128K context $2.5/M input $10/M output
23 providers
911.0

Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on...

mistralai/mistral-small-3.2-24b-instruct 128K context $0.075/M input $0.2/M output
860.0

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...

meta-llama/llama-4-maverick 128K context $0.2/M input $0.696/M output
796.0

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

meta-llama/llama-4-scout 327.68K context $0.1/M input $0.3/M output