Modeller

A large-scale sparse Mixture-of-Experts language model built from the ground up by South Korea's Motif Technologies with a fully in-house, proprietary design (Grouped Differential Latent Attention, per-expert Grouped PolyNorm, modified mHC). Has roughly 314B total parameters with about 13B active per token (384 routed experts, top-8, plus one shared expert) and a native 256K context window (262,144 tokens). Multilingual and general-purpose, with reasoning and tool-calling support in vLLM serving. This is an intermediate preview/beta checkpoint, released with open weights (bfloat16) for non-commercial research use ahead of the final release.

A specialized, cyber-focused Gemini model built on top of Gemini 3.5 Flash and fine-tuned for finding and fixing cybersecurity vulnerabilities at a lower price per token than larger models. Deployed within Google's CodeMender code security agent, where multiple 3.5 Flash Cyber agents collaborate to reach competitive frontier performance on benchmarks like CyberGym. Given its dual-use nature, it is available exclusively to governments and trusted partners via CodeMender as a limited-access pilot.

Google DeepMind's workhorse Flash model that builds on Gemini 3.5 Flash with better coding, knowledge work, and multimodal performance while reducing output token usage by roughly 17% per the Artificial Analysis Index. Natively multimodal across text, image, audio, and video with a 1M-token context window, configurable thinking levels, and built-in computer use, tuned for scaling agentic workflows at a lower cost per output token.

Anthropic's new state-of-the-art model, a thoughtful and proactive successor to Opus 4.8 that approaches Claude Fable 5's frontier intelligence at half the price. Delivers major gains in coding, agentic workflows, computer use, knowledge work, and scientific research, with strong self-verification and iteration, configurable effort settings, and Anthropic's best alignment and safety results to date. Also available in a Fast mode that runs around 2.5x the default speed.

Google's fastest and most cost-effective Gemini 3.5-class model, delivering around 350 output tokens per second per the Artificial Analysis Index. Designed for low-latency and high-throughput agentic workflows such as agentic search and document processing, with configurable thinking levels, built-in computer use, and full multimodal support across a 1M-token context window.

Thinking Machines Lab's open-weights general-purpose multimodal Mixture-of-Experts model with 975B total parameters and 41B active parameters. Inkling accepts text, image, and audio inputs, produces text, and is designed for agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation.

Moonshot AI's flagship Kimi model for frontier intelligence, agentic coding, knowledge work, and deep reasoning. Kimi K3 supports a 1-million-token context window for long-running software engineering and research workflows.

OpenAI GPT-5.6 serisinin dengeli modeli; en yüksek kalite düzeyinden bir miktar ödün verirken belirgin şekilde daha düşük gecikme ve maliyet sunar. Ayarlanabilir akıl yürütme derinliğiyle güçlü reasoning, kodlama ve agent tabanlı araç kullanımını korur. Bu nedenle ileri yeteneklere ihtiyaç duyan yüksek hacimli production iş yükleri için varsayılan seçenek olmaya uygundur.

Google DeepMind'in yeni bir temel üzerinde yeniden geliştirdiği amiral gemisi Gemini modeli; 2 milyon token bağlam penceresi ve en zor matematik, kodlama ve multimodal görevler için Deep Think reasoning modu sunar. Metin, görüntü, ses ve videoyu yerel olarak işler; akışlı fonksiyon çağırmayı ve güvenilir uzun bağlam kullanımını destekler.

Meta Superintelligence Labs' updated flagship, building on Muse Spark with stronger agentic reasoning, more reliable multi-agent orchestration, and improved multimodal understanding across voice, text, and image. Extends the context window and reduces latency and reasoning token usage while raising coding and tool-use accuracy. Powers Meta AI across its product ecosystem.

OpenAI GPT-5.6 serisinin amiral gemisi modeli; zorlu gerçek dünya görevlerinde güvenilirliği ve verimliliği artırırken kodlama, bilimsel akıl yürütme, uzun vadeli planlama ve agent iş akışlarını geliştirir. Maksimum reasoning-effort ayarı ve karmaşık çok adımlı çalışmalar için alt agent'lar başlatan ultra modunu ekler.

The fast, low-cost tier of OpenAI's GPT-5.6 series, optimized for high-volume, latency-sensitive tasks such as classification, extraction, routing, and lightweight agentic steps. Approaches the larger GPT-5.6 tiers on many benchmarks while running several times faster at a fraction of the price.

xAI'nin bugüne kadarki en yetenekli modeli; kodlama, agent görevleri ve bilgi çalışması için geliştirilmiş, gerçek yazılım geliştirme araçlarıyla birlikte tasarlanmıştır. OpenAI uyumlu API üzerinden güncel bilgiye erişimi, genişletilmiş akıl yürütmeyi ve geniş bağlamda araç kullanımını destekler.

Anthropic'in en yetenekli Sonnet sınıfı modeli; ileri kodlama, agent ve profesyonel çalışma yeteneklerini orta segmente taşır ve daha düşük fiyatla Opus 4.8 arasındaki farkı azaltır. Seçilebilir reasoning derinliğiyle uyarlanabilir düşünmeyi, 1 milyon token bağlam penceresini ve metin, görüntü ve dosya girişlerini destekler. Kod adı Fennec'tir.

Sakana AI's multi-agent orchestration model from Tokyo, delivered as a single OpenAI-compatible API. Fugu is itself a language model trained to call a pool of specialist LLMs (and recursive instances of itself), handling model selection, delegation, verification, and synthesis behind one endpoint. Built on Sakana AI's TRINITY and Conductor research, its routing intelligence is learned in model weights rather than hand-configured.

The higher-quality tier of Sakana AI's Fugu multi-agent orchestration system, tuned for the hardest coding, reasoning, science, and agentic tasks. Coordinates a swappable pool of frontier LLMs through one OpenAI-compatible endpoint, delegating sub-tasks, verifying intermediate work, and synthesizing a single answer. Sakana reports strong vendor benchmarks including 93.2 on LiveCodeBench, 73.7 on SWE-Bench Pro, and 82.1 on TerminalBench.

Sarvam AI's compact 2B-parameter language model built from the ground up for Indian languages. Provides best-in-class performance across 10 Indic languages (bn, gu, hi, kn, ml, mr, or, pa, ta, te) alongside English, outperforming larger general-purpose models like Gemma-2-2B and Llama-3.2-3B thanks to careful data curation and an efficient Indic tokenizer. Edge-deployable.

Sarvam AI's 30B-parameter Mixture-of-Experts reasoning model trained from scratch with only 2.4B active parameters per token. Optimized for real-time deployment and Indian languages, delivering strong reasoning, coding, and conversational performance while remaining efficient to serve. Open-weights.

Z.ai's (formerly Zhipu AI) flagship open-weight coding model with a 1M-token context window. Mixture-of-Experts architecture with 753B total parameters and ~40B active per request, featuring two cost-balancing reasoning modes. Tops several coding benchmarks while remaining a fraction of the cost of comparable proprietary frontier models. MIT-licensed weights.

Sarvam AI's 24B-parameter instruction-tuned model derived from Mistral-Small-3.1-24B, post-trained on English plus eleven major Indic languages (bn, hi, kn, gu, mr, ml, or, pa, ta, te). Delivers large relative gains on Indian-language, math, and programming benchmarks over its base model, with a hybrid reasoning mode for complex tasks.

Sarvam AI's sovereign 105B-parameter Mixture-of-Experts model activating ~9B parameters per token, with a 128K-token context window. Trained on 12 trillion tokens across 22 Indian languages using 128 sparse experts with Multi-head Latent Attention and a custom low-fertility Indic tokenizer. Wins the majority of pairwise comparisons on Indian-language and STEM benchmarks.

Cohere's enterprise flagship model building on Command A with stronger reasoning, agentic tool use, and multilingual performance across 23 languages. Optimized for secure, high-throughput RAG, retrieval, and long-horizon agent workflows in regulated environments, with private and on-premise deployment options.

Moonshot AI's latest open-source, coding-focused model in the Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. A 1-trillion-parameter model that cuts reasoning token usage by roughly 30% versus K2.6 while improving coding and agent performance — +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite for multi-language support. Released under a Modified MIT License and available via Kimi APIs and Hugging Face.

Google DeepMind's experimental diffusion-based member of the Gemma 4 open model family. Unlike autoregressive models that generate text one token at a time, DiffusionGemma denoises a canvas of placeholder tokens to produce up to 256 tokens in parallel, finalizing output in one block. A Mixture-of-Experts model with 26B total parameters and 3.8B active per inference, delivering roughly 4x the throughput of similarly sized autoregressive Gemma models on local hardware. Excels at non-linear tasks like in-line editing, molecular sequencing, mathematical graphing, and self-correcting puzzles.