Qwen3.5-9B
Qwen3.5-9B is a 9B dense model that outperforms Qwen3-30B on most benchmarks and beats GPT-5-Nano on vision tasks. Natively multimodal with 262K-1M context, Apache 2.0 licensed.
Models
GPT-5.2
GPT-5.2 is OpenAI's most capable model with three modes, 400K context, and record-setting professional benchmarks - but speed and pricing raise questions.
Gemini 3 Deep Think
Google DeepMind's reasoning mode scores 84.6% on ARC-AGI-2, 3455 Codeforces Elo, and solves 18 previously unsolved research problems - outpacing Claude Opus 4.6 and GPT-5.2 on reasoning-heavy tasks.
Nano Banana 2 (Gemini 3.1 Flash Image)
Google DeepMind's natively multimodal image generation and editing model built on Gemini 3.1 Flash - Pro-level quality at Flash speed, free for all Gemini users.
Kimi K2.5
Moonshot AI's Kimi K2.5 is a 1T-parameter MoE model activating 32B per token with native multimodal vision via MoonViT-3D, Agent Swarm coordination of up to 100 sub-agents via PARL, and top-tier math and coding benchmarks under a modified MIT license.
DeepSeek V3.2
DeepSeek V3.2 is a 671B-parameter MoE model activating 37B per token that delivers frontier-class reasoning and coding at the lowest API price in the industry - $0.14/$0.28 input, $0.42 output per million tokens.
Gemini 2.5 Flash-Lite
Google's cheapest Gemini model pairs a 1M-token context window with $0.10/$0.40 per million token pricing, multimodal input, and 359 tokens/second throughput for high-volume production workloads.
GLM-4.7-Flash
Zhipu's GLM-4.7-Flash is a 30B-A3B MoE model that posts 59.2% on SWE-bench Verified and 79.5% on tau2-Bench while running on a single RTX 4090 - MIT licensed and free via the Z.AI API.
Google Gemma 3 27B
Google Gemma 3 27B is a 27B dense multimodal model supporting text and vision with a 128K context window, 140+ languages, and single-GPU deployment - the most capable open model at its size class.
GPT-4o mini
OpenAI's budget API workhorse pairs 128K context with $0.15/$0.60 per million token pricing, solid coding benchmarks, and the broadest third-party ecosystem of any small model.
Llama 4 Maverick
Meta's Llama 4 Maverick packs 400B total parameters into a 128-expert MoE architecture with only 17B active per token, beating GPT-4o on Chatbot Arena while matching DeepSeek V3 on reasoning at half the active parameters.
Llama 4 Scout
Meta's Llama 4 Scout is a 109B-total, 17B-active MoE model with 16 experts and a 10M-token context window - the longest of any open-weight model - with native multimodal support for text and images.
