Multimodal LLM releases

A fast multimodal model from Singapore's Agnes AI, surfaced mid-September 2026 (model card and provider coverage around Sep 14). This row reflects the disclosed open-weights PREVIEW checkpoint (Agnes-AI/Agnes-3.0-Flash on Hugging Face, Apache 2.0): a 33B-parameter model with a 262,144-token context, text/image/video input and text output, using a hybrid-attention architecture that mixes recurrent and standard attention to hold memory down at long context — of 72 decoder layers, 54 run a gated delta rule (a recurrent mechanism whose per-layer state does not grow with sequence length) while 18 use standard global grouped-query attention (24 query / 4 KV heads) and are the only layers that accumulate a KV cache. At bf16 it needs roughly 66 GB of disk and a single H100/H200-class GPU, and ships custom modeling code (trust_remote_code=True). Note the production "Agnes 3.0 Flash" served through Agnes AI's API is a different checkpoint with a 1M-token context window; the specs here are the open-weights preview. Vendor-reported figures, unverified independently at launch.

Moonshot AI's mid-tier coding and agentic model, released Sep 11 2026 and positioned between the flagship Kimi K3 and the older Kimi K2.7 Code. Moonshot describes its coding and agent performance as close to K3 with significantly more efficient "thinking," and brings the K3-series thinking controls — adjustable reasoning effort set to low, high, or max, with max as the default. Multimodal input (text, images, and video) with text output, and a 1M-token context window made available across all Kimi membership tiers rather than gated to upper plans as it was when K3 first opened 1M context. Served through Kimi Code under the model id kimi-for-coding, so existing clients pick it up without config changes. Proprietary and preview-status at launch; parameter count and architecture are undisclosed for this specific checkpoint, though it sits within Moonshot's MoE Kimi K2 line. Benchmark and efficiency figures are vendor-reported and unverified by an independent harness at launch.

The higher-performance tier of Sakana AI's Fugu orchestration family, released September 11 2026. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system — a language model trained to route tasks across a fixed, swappable pool of open-weight and specialized models and to recursively call instances of itself — detailed in Sakana's ICLR 2026 TRINITY (evolved LLM coordinator) and Conductor (RL-discovered natural-language coordination) papers. v2 prioritizes answer quality on complex multi-step reasoning, autonomous research, and full-stack software development, and notably does not include any proprietary frontier model in its pool: no Claude Fable 5, Fable 5.1, or GPT-6 Astra, routing instead over open-weights and specialized models including NVIDIA Nemotron via partnership, which Sakana positions as resilience against single-vendor and export-control risk. Supports configurable reasoning effort (high, xhigh, max), function calling, structured outputs, image and PDF input, and built-in web search. Pricing is $5/$30 per Mtok (cached input $0.50), with a premium tier of $10/$45/$1.00 above 272K-token context. Vendor-reported: 48.3 on Chartography (a visual reasoning / data-interpretation benchmark) versus 27.3 for Anthropic's Opus 5 and 29.5 for Fable 5 — achieved without either closed model in its pool — plus 74.3 on DeepSWE. Parameter count and context ceiling are undisclosed, the figures describe an orchestrated system rather than one set of weights, and all are unverified at launch.

The natively multimodal member of Ant Group inclusionAI's Ling-3.0 line, released Sep 10 2026 with MIT open weights (inclusionAI/Ling-3.0-flash-VL). A 124B-total / ~5.5B-active sparse MoE that inherits Ling-3.0-flash's language, reasoning, and long-context ability and extends it with native image and video understanding: a ViT visual encoder feeding a two-layer MLP projector, VideoRoPE positional encoding for video, and the family's 42-layer hybrid backbone alternating Kimi Delta Attention and Gated MLA layers at a 5:1 ratio. Carries a 256K-token context. inclusionAI frames the vision work around three axes — understanding complex visual information, reasoning over visual evidence, and interacting with interfaces (GUI agents) — and reports 42 on the Artificial Analysis Intelligence Index v4.1.1, four points above text-only Ling-3.0-flash at 38. Served free at launch via OpenRouter (inclusionai/ling-3.0-flash-vl) alongside self-hosting on vLLM. Vendor figures, unverified independently at launch.

DeepSeek's efficient, very-low-cost flagship released September 10 2026, retiring V4-Flash and taking over V4-Pro API traffic on September 14 (DeepSeek reports it beats V4-Pro on performance, cost, speed, and total time). A 552B-total-parameter multimodal sparse Mixture-of-Experts model built on a new Causal Encoder-Decoder architecture that activates only ~8B parameters per token on input and ~16B on output for cheaper long-context prefill, with a 1M-token context, up to 384K output tokens, and native image understanding (vision in, text out). The other headline change over July's V4-Flash is memory: FP4 quantization plus "pure CSA2" cross-layer attention reuse compress the KV cache to roughly 890 bytes per token — about an 8x reduction, cutting HBM to a quarter and SSD to an eighth for an equivalent conversation state, which is what makes million-token agentic runs practical on a single node. Open-weight under the MIT license, downloadable and self-hostable, and served across many inference providers. API pricing is $0.30/$1.20 per Mtok input/output at peak (01:00-04:00 and 06:00-10:00 UTC weekdays) and half that off-peak ($0.15/$0.60), with cache hits around $0.006/$0.003 per Mtok. DeepSeek reports it narrowly edges Claude Opus 5 and GPT-5.6 Sol on DeepSWE, but there is no independent benchmark table at launch and vendor performance claims are unverified.

The lightweight multimodal MoE member of Nex AGI's Nex-N2.5 family (Sep 8, 2026). 35B total / 3B active sparse MoE with a 262K context, sharing the Pro model's multimodal agentic stack (computer use, web browsing, visually grounded workflows) at much lower inference cost. Supports vision, reasoning, function calling, and tool use. Apache-2.0 weights on Hugging Face (nex-agi/Nex-N2.5-mini); hosted as a free preview on OpenRouter at launch.

Nex AGI's flagship multimodal MoE agentic model in the Nex-N2.5 family (Sep 8, 2026). 397B total / 17B active sparse MoE with a 262K context. Builds on Nex-N2 multimodal foundations with stronger computer use, web browsing, and visually grounded agent workflows; supports vision, reasoning, function calling, and tool use. Apache-2.0 weights on Hugging Face (nex-agi/Nex-N2.5-Pro); hosted as a free preview on OpenRouter at launch.

The higher-quality reasoning tier of OpenAI's GPT-6 Astra, released Sep 4, 2026 (API id gpt-6-astra-pro). It is the same underlying model as GPT-6 Astra served with reasoning.mode set to 'pro', trading latency for higher-quality responses on the hardest professional, coding, research, computer-use, and agentic tasks. Carries a 1.05M-token context window and up to 128K output tokens. Standard pricing is $10/$50 per Mtok input/output (cached input $1/Mtok, batch at half price), with a Fast mode at ~2x the standard rate. Access is limited to ChatGPT Pro, Business, and Enterprise users (and the pro reasoning mode on the API); off by default at launch and enabled per workspace. Shares GPT-6 Astra's positioning around state-of-the-art computer/browser use, finished professional artifacts, long-session Codex coding, and cybersecurity capability that crosses the 'Critical' threshold of OpenAI's Preparedness Framework — so exploit-creation capabilities ship gated behind OpenAI's Daybreak program.

OpenAI's new frontier flagship, released Sep 3, 2026 (API id gpt-6-astra) as the successor to GPT-5.6 Sol and OpenAI's most capable and most aligned model to date. Positioned around three areas: state-of-the-art computer/browser use, a step change in producing finished professional artifacts (documents, slides, spreadsheets, and websites via Sites in ChatGPT that follow the user's templates), and a jump in cybersecurity capability that crosses the 'Critical' threshold of OpenAI's Preparedness Framework. It decides when to ask clarifying questions versus proceed on assumptions, holds constraints across mid-task steering, and — with an updated Codex harness — keeps searchable notes across context windows instead of lossy compaction. Carries a 1M-token context window; available as gpt-6-astra in the OpenAI API and on Amazon Bedrock (with Microsoft Azure), plus a GPT-6 Astra Pro tier for ChatGPT Pro, Business, and Enterprise (off by default at launch, enabled per workspace). Standard API pricing is $10/$50 per Mtok input/output, with a Fast mode at ~2x price (~$20/$100) for up to 2.5x speed, plus separate cache rates. Vendor-reported benchmarks include OSWorld 2.0 72.6% (at ~47% less time per task than Sol), FrontierMath Tier 4 v2 97.6%, GPQA Diamond 96.0%, Terminal-Bench 4.0 57.7%, and ExploitBench 100%; the marquee ARC-AGI-3 ~99.9% depends on a stateful provider-adapter harness (stateless runs score far lower), and it trails Claude Fable 5.1 on Humanity's Last Exam with tools (57.2% vs 65.0%). Because it crosses the Critical cyber threshold, exploit-creation capabilities ship gated behind OpenAI's Daybreak program; OpenAI also flags a regression in chain-of-thought monitorability as a research priority.

HUMAIN's Arabic-focused frontier MoE model (428B total parameters), commissioned by HUMAIN and delivered on the MiniMax-M3 lineage with HUMAIN Arabic post-training. Natively multimodal (text plus image and video understanding) with agentic tool use and controllable reasoning modes. Available as a limited / research preview on HUMAIN Node (API id humain-m3). Distinct from the live minimax-m3; proprietary weights not yet released.

Google DeepMind's fast, cost-efficient Flash model released Sep 2, 2026 — its fourth Flash model in under four months and the successor to Gemini 3.7 Flash. At high reasoning it scores 59 on the Artificial Analysis Intelligence Index (up 3 points from Gemini 3.7 Flash and level with sub-maximum efforts of GPT-5.6 Sol and Grok 4.6), 57 at medium (matching GPT-5.6 Terra and Muse Spark 1.2), and 52 at low (matching Gemini 3.6 Flash at ~30% lower cost per task). The improvement is driven mainly by agentic evaluations — t^3-Banking tool use (+12 points to 45%), Terminal-Bench v2.1 coding, and GDPval-AA v2 real-world tasks. It keeps a 1M-token context window and multimodal input (text, image, video, speech) with text output. Pricing matches Gemini 3.7 Flash's current discounted rate of $0.75/$3.75 per Mtok input/output through the end of 2026 ($1.50/$7.50 at standard pricing), with cached input keeping a 90% discount; a ~30% rise in average output tokens per task (to ~48k) lifts cost per task to ~$0.58 at high reasoning despite unchanged per-token pricing. Available in the Gemini app for AI Pro and Ultra subscribers, AI Mode, and Gemini in Google Sheets, and for developers via Google Antigravity, AI Studio, and the Gemini API. Benchmark figures are vendor/third-party-reported.

The cybersecurity-tuned sibling of Gemini 3.8 Flash, introduced by Google DeepMind on Sep 2, 2026 in the same launch. It shares the same foundational intelligence as Gemini 3.8 Flash but ships with a more permissive set of mitigations for defensive security work, and is available only to trusted defenders — government authorities, critical-infrastructure operators, and software maintainers — through the new Fairwind Program. Google reports frontier-level autonomous vulnerability discovery (surpassing Gemini 3.5 Flash Cyber and much larger frontier models on the CyberGym benchmark), a success rate exceeding 70% on an internal real-world vulnerability benchmark spanning 20 programming languages, and 47.2% pass@1 on the external CWE-Bench patching benchmark (on the Pareto frontier versus a leading frontier model at 47.8%, at far lower cost). Google is already using it internally: the Chrome Security team reports 2.6x more correct patches than the best larger commercial models, and its Cloud Vulnerability Research team found a critical foundational vulnerability in under two hours. Keeps a 1M-token context and multimodal input with text output; prioritizes vulnerability fixing over offensive capability. Not publicly token-billed (trusted-access only), so no list price is recorded. Benchmark figures are vendor-reported.

Meta's successor to Muse Spark 1.2, released Sep 2, 2026. A multimodal reasoning model built for long-running agentic, multi-agent, and coding workflows: it is designed to keep track of information across extended tasks, work through conflicting inputs, and request clarification or confirmation when needed, with an emphasis on concise execution. Accepts text and image input over a 1M-token (1,048,576) context and returns text. Standard-tier API pricing is $1.25 / $4.25 per 1M input/output tokens ($0.15 cached input); as with prior Muse Spark releases a lower-cost muse-spark-1.3-contributor tier is offered in exchange for permission to train future Meta models on prompts and completions. Hosted by Meta and served via OpenRouter (meta/muse-spark-1.3).

An updated snapshot of Alibaba's flagship Qwen3.8-Max, released Sep 2 2026 (model id qwen3.8-max-0902; alias qwen3.8-max-2026-09-02). A post-training upgrade on the unchanged 2.4-trillion-parameter Mixture-of-Experts base (~95B active per token), it sharpens coding for engineering-scale projects and long-horizon autonomous development, multi-tool agent orchestration, and native vision (chart reasoning, document parsing, multimodal perception). Accepts text, image, and video input and returns text, with a 1M-token context, up to ~131K output tokens, and an optional thinking mode carrying up to 256K chain-of-thought tokens. Pricing is unchanged from Qwen3.8-Max at $2.00 / $6.00 per 1M input/output tokens. Vendor-reported gains include a +22-point CodeArena jump to 1,691 (first on that leaderboard at launch); figures are unverified. Served API-first (Alibaba, OpenRouter); the -Max flagship tier is closed-weight, so treat this snapshot as proprietary despite the open-weight base family. Distinct from the base Qwen3.8-Max (2026-08-03).

The restricted sibling of Claude Fable 5.1, announced Sep 1, 2026. It is the same underlying model as Fable 5.1 but ships with more permissive safeguards for vetted users, available only through Anthropic's trusted-access programs — the Cyber Verification Program (defensive security work) and the Life Sciences Verification Program (professional biology R&D, developed with the US government). Anthropic reports it has the strongest cyber capabilities of any model it has released (still within the lower risk tier of its Frontier Compliance Framework) and scores 60.9% on Terminal-Bench 4.0 (vs 55.8% for safeguarded Fable 5.1). Also powers Claude Security's codebase vulnerability scanning. Access is currently limited to a set of US organizations. Not publicly token-billed, so list pricing is not recorded.

Anthropic's most capable widely released model, launched Sep 1, 2026 (API id claude-fable-5-1) as a direct upgrade to Claude Fable 5 for agentic coding, long-running problem-solving, and knowledge work. Anthropic reports gains across coding, computer use, multidisciplinary reasoning, and finance/analysis while producing more concise plans and summaries, and cites benchmark results including Terminal-Bench 4.0 55.8%, Terminal-Bench-Science 0.1 52.6%, Humanity's Last Exam 65.0% (with tools), and CursorBench 3.2.0 73.4%. Base pricing is unchanged from Fable 5 at $10/$50 per Mtok input/output, but cache reads were cut 75% to $0.25/Mtok, reducing typical workload cost ~25% (up to ~45% for highly agentic work). Carries a 1M-token context and up to 128K output tokens; available on the Claude API, AWS, Google Cloud, and Microsoft Azure. Cybersecurity safeguards were loosened to allow defensive vulnerability discovery (Claude Code sees ~60% fewer false-positive interventions), while dual-use cyber and life-sciences R&D requests still route to Opus models. First Anthropic release to carry the EU AI Act text watermark.

Cohere's enterprise document-intelligence vision-language model, released Aug 27 2026 (parse-v5.0). A compact 2.3B-parameter VLM that converts PDFs, slides, and images into structured Markdown at scale — detecting and interpreting tables, forms, diagrams, and embedded images, extracting semantic context rather than a flat character stream, and returning bounding boxes for visual elements to support retrieval, grounding, and citation. Trained on business documents across finance, insurance, and scientific research, with support for nine major world languages, an 8,192-token context window, and a ~4.6GB footprint. Priced at $1.50 per 1,000 pages via the Cohere API, with Model Vault for higher-volume/managed deployment and availability on Amazon SageMaker and Microsoft Azure; it can also be deployed privately. Cohere's own comparison places Parse 5 behind larger general-purpose frontier models (GPT-5.5, Opus 4.8, Gemini 3.5 Flash) on raw accuracy, positioning it on price-to-performance and cost per page. Part of Cohere's document-AI line alongside North Micro Vision. Vendor figures, unverified independently at launch.

The productionized, managed Qwen Cloud API model announced Aug 26 2026 alongside the open-weight Qwen3.8-Flash-Next, with official QwenCloud pricing confirmed Aug 27-28. It runs the same Qwen4-preview architecture as Flash-Next — a 125B-total sparse Mixture-of-Experts that activates ~6B parameters per token (~180B stored once a 51B n-gram embedding table and a multi-token-prediction module are counted; roughly 95% sparsity), built on Gated-DeltaNet + Qwen Sparse Attention with a gated residual stream and Muon-trained large linear layers — but ships as the hosted service rather than the self-host weights. On QwenCloud it defaults to a 1M-token context with built-in tools and accepts text/image/video in, returning text out. List pricing is $0.15 input / $0.47 output / $0.016 cache-hit per Mtok (domestic China Y0.8/Y2.7/Y0.1), roughly a third of DeepSeek-V4-Flash and about one-thirteenth of the Qwen3.8-Max flagship. Also reachable through OpenCode Go's flat-rate subscription. Distinct catalog row from the open-weight Qwen3.8-Flash-Next (self-host, Qwen Community License 1.0); this managed API is recorded proprietary/API-only. Self-reported benchmarks carry over from Flash-Next (SWE-bench Pro 62.5, DeepSWE 58.7) — all vendor numbers, unverified by independent labs at launch. Thinking on by default.

An open-weight, experimental preview of the architecture that will underpin Qwen4, released Aug 26 2026 (Qwen/Qwen3.8-Flash-Next). A sparse MoE with ~6B active parameters (headline 125B-with-6B-activated; ~180B stored once a 51B n-gram embedding table and 4B multi-token-prediction module are counted), 512 experts (10 routed + 1 shared), and a hybrid Gated-DeltaNet + Qwen Sparse Attention design. Native 262,144-token context, extensible to 1M via YaRN. Accepts text, image, and video in and returns text out. Distinct from the managed Qwen Cloud 'Qwen3.8-Flash' API (which defaults to 1M context and bundled tools); this Next build is catalog/self-host only with no hosted list price at launch, served via Transformers, vLLM, SGLang, and TokenSpeed. Weights under the Qwen Community License 1.0. Self-reported vs DeepSeek-V4-Flash-0731: DeepSWE 58.7 vs 54.4, SWE-bench Pro 62.5 vs 56.0, LiveCodeBench v6 91.9, GPQA Diamond 91.7, though NL2Repo 48.1 vs 54.2 is a regression; vision self-reports include AndroidWorld 84.5 and RealWorldQA 88.5 — all vendor numbers, unverified at launch. Thinking on by default.

Z.ai's first natively multimodal GLM-5 (text, image, and video understanding in one stack), released Aug 26 2026 and stealth-tested beforehand as 'ox-alpha'. A 320B-total / 18B-active MoE (45 layers, hybrid linear + sparse attention) with a 1M-token context window and MIT-licensed open weights (zai-org/GLM-5.3-Flash). It is a distinct model from the text-only flagship GLM-5.3 (753B, $1.40/$4.40) and is priced roughly 10x cheaper on input: list $0.15 / $0.03 cached / $0.50 per Mtok, with a 50% promo through 2026-09-09. Vision sits inside the coding/agent loop (self-visual judgment) rather than as a bolted-on VL head. Self-reported vs GLM-5.2: DeepSWE 63.4 vs 46.2, AutomationBench 48.8 vs 26.2, Terminal-Bench 2.1 84.3 vs 81.0; vision self-reports include CharXiv Reasoning 89.4 and Chartography 78.0, though BabyVision 53.4 trails Gemini 3.7 Flash — all vendor numbers, unverified at launch. Thinking is always on and cannot be disabled.

DeepSeek's first multimodal V4 model — an experimental vision-understanding checkpoint that went live on the DeepSeek API (model='deepseek-v4-flash-vision-exp') on Aug 21, 2026. It extends DeepSeek-V4-Flash with image understanding while keeping its full text capabilities (agents, reasoning, coding, and world knowledge), matching V4-Flash on text benchmarks. DeepSeek reports a major jump on multimodal agent benchmarks over V4-Flash, bringing multimodal-agent performance close to Opus-4.8 — a vendor-reported result, unverified by an independent harness at launch. Keeps V4-Flash's 284B-total / 13B-active sparse MoE architecture and 1M-token context, with up to ~393K output tokens; accepts text plus up to 600 images per request (8,192px per side, 64 MiB payload) and returns text only, with images billed at up to 384 tokens each. API pricing held at V4-Flash rates: $0.22 / $0.66 per 1M input/output tokens, with a $0.007 per 1M cached-input rate. API-only and experimental at launch — weights were not published, so treated as proprietary. RETIRED 2026-09-10: superseded by DeepSeek-V4.1-Flash, whose native multimodal support absorbs this experiment. For compatibility the `deepseek-v4-flash-vision-exp` API id temporarily routes to V4.1-Flash.

The open-weight, single-GPU sibling of Qwen3.8-Max, published by Alibaba on Hugging Face on Aug 14 2026 under Apache 2.0 — the smaller open release Alibaba had promised alongside the closed Qwen3.8-Max flagship. A 27B dense model (~28B counting the ~1B vision encoder) with 64 layers, hidden size 5,120, and a 248,320-token vocabulary. Uses a hybrid attention stack — 48 Gated DeltaNet linear-attention layers to 16 full Gated Attention layers (a 3:1 split) — for a native 262,144-token context, extendable to 1M via YaRN. Natively multimodal (text, image, and video input; text output) and ships with Multi-Token Prediction for speculative decoding. Quantized (Unsloth dynamic GGUFs) it runs in ~16-17GB of VRAM, fitting a single consumer GPU such as a 3090 or 4090 — positioned as one of the most capable local models of 2026.

The first open-weight release in the dots3 series from Dots Studio (rednote-hilab), the AI lab of Xiaohongshu (RedNote), open-weighted on Hugging Face on Aug 14 2026 under Apache-2.0. A Mixture-of-Experts model with ~280B total parameters and ~16B active per token, carrying a 512K-token context and multimodal understanding across text, vision, and audio (text output). Positioned less around benchmark-maxxing and more around combining reasoning, long-context processing, coding, and multi-step agent workflows in a compute-efficient architecture optimized for long-horizon real-world tasks. Introduces TEMPO, a reinforcement-learning method for long-horizon agents in which the model periodically checkpoints its own progress and updates its working memory mid-task. Comes from the same dots3 series whose internal build scored a perfect 42/42 at the 2026 International Mathematical Olympiad. Weights ship in BF16 and FP8; served free on OpenRouter (dots-studio/dots3-note-preview) at launch.

Google DeepMind's 2026-08-13 workhorse Flash model, tuned this cycle for software engineering, agent workflows, and multi-step execution. Multimodal input (text, image, video, audio, PDF) over a 1,048,576-token context with a 65,536-token output ceiling; supports function calling, search-as-a-tool, and computer use. Introductory pricing is $0.75 / $3.75 per 1M input/output tokens through 2026-12-31, rising to $1.50 / $7.50 thereafter. Google-reported gains over Gemini 3.6 Flash: DeepSWE v1.1 65.3% (vs 49.0%), AutomationBench 30.4% (vs 17.0%), WebDev Arena Elo 1588 (vs 1538).

xAI's frontier update to Grok 4.5, released 2026-08-12. Built on the same reported 1.5-trillion-parameter V9 foundation as Grok 4.5, with gains coming from upgraded supervised fine-tuning and reinforcement learning rather than a new base. Supports a 500K-token context and configurable reasoning effort (low, medium, high default, xhigh). List pricing is $2 / $6 per 1M input/output tokens below 200K prompt tokens ($0.50 cached input), rising to $4 / $12 above 200K.

Cohere's compact document-focused vision-language model, published Aug 12 2026 under Apache 2.0. A 2.4B-parameter VLM combining a custom 400M native-resolution vision encoder, a 2B language model on the Command A+ architecture, and a projector; it preserves aspect ratio for images up to 1654x2339px (an A4 page at 200 dpi). Multilingual visual understanding across documents, charts, and natural images, text output. Vendor-reported: 0.921 DocVQA and 0.808 ChartQA on document tasks, 0.732 RefCOCO on visual grounding, and 0.687 MMBench on general VQA; text-only capability lags larger models. Part of Cohere's North product family alongside North Mini Code. Open weights on Hugging Face.

Liquid AI's edge vision-language model, released Aug 12 2026 — a 3.1B-parameter VLM built on the LFM2.5-2.6B text base with an integrated SigLIP2 400M NaFlex vision encoder. Accepts text, images, and video frames and returns text, tuned for on-device screen understanding, visual grounding, and tool calling. Vendor-reported: 80.7 average on ScreenSpot-v2 screen understanding, 87.9 P@1 on RefCOCO grounding, 59.5 on ToolSandbox function calling, 81.0 on MMBench, and 69.4 averaged across 28 benchmarks. Runs on-device at ~228 tok/s on an Apple M5 Max and ~116 tok/s on an AMD Ryzen AI Max+ 395; supported in llama.cpp, MLX, vLLM, SGLang, and ONNX. Open weights on Hugging Face.

Sakana AI's Japanese-specialized reasoning and agent model, opened as an API on Aug 11 2026. Rather than pretraining from scratch, Sakana builds on Moonshot's open-weight Kimi K2.6 and adds training for Japanese language and business contexts — the lab's 'sovereign AI by tuning other labs' strategy. Handles Japanese instruction following, business writing, math, coding, research, and multi-step agentic workflows; accepts text, images, and files such as PDFs and returns text, over a 262,144-token context with up to 65,536 output tokens, and ships native web-search and code-execution tools through a unified OpenAI-compatible API. Priced on OpenRouter at $0.95 / $4.00 per 1M input/output tokens. Not available in the EU/EEA, the UK, or Switzerland. Served via API; weights not separately published (base Kimi K2.6 is open-weight).

Meta's first open-weight agentic model, released Aug 10, 2026 under an Apache 2.0 license — Meta's return to open weights after the closed Muse Spark line. A ~30B-parameter dense causal transformer (about 29.6B parameters across 52 layers) paired with a ~1.8B ViT-G/14 perception encoder, so it accepts interleaved text and images and returns text across more than 100 languages. Carries a 131,072-token context, a 202,048-token vocabulary, and a Jan 4, 2026 knowledge cutoff. Uses grouped-query attention (32 query heads, 2 KV heads) in a local/local/local/global pattern with a 2,048-token sliding window, plus speculative decoding for throughput. Quantized to roughly 4-bit it fits inside a ~24GB memory envelope, running on a single consumer GPU or an Apple-silicon Mac — the model is tuned for on-device agent workloads. Shipped the same week as the closed-weight Muse Spark 1.2 coding flagship; Meta's first agentic model to ship with both open weights and a permissive commercial-use license.

Meta's flagship coding model, released 2026-08-05 and purpose-built for complex software engineering: debugging sprawling codebases, validating changes across thousands of files, and multi-step reasoning, with deep integration into persistent asynchronous background agents. Accepts text, image, video, audio, and PDF input over a 1M-token context and returns text. Standard-tier API pricing is $1.25 / $4.25 per 1M input/output tokens ($0.15 cached input); a new muse-spark-1.2-contributor tier drops to $0.10 / $0.20 in exchange for permission to train future Meta models on your prompts and completions. Shipped alongside Muse Code, a terminal-based coding agent powered by the model. Meta has signaled open weights are coming.

Alibaba's largest model to date and the flagship of the Qwen3.8 line — a 2.4-trillion-parameter sparse Mixture-of-Experts (~95B active per query) that Alibaba positions just behind Anthropic's Fable 5 on overall performance. Previewed 2026-07-19 at the World AI Conference in Shanghai, it went generally available on 2026-08-03 with a published benchmark table, standard API access, and firm per-token pricing ($2 / $6 per 1M input/output tokens). Fully multimodal (text, image, video input) over a 1M-token context. Alibaba also committed to shipping open weights for both Qwen3.8-Max and a smaller Qwen3.8-27B checkpoint.

Cost-optimized multimodal member of the Qwen3.7 line — a vision-language reasoning model with a 1M-token context, tuned for high-volume multimodal agent workloads (visual coding, screen perception, browser/computer use, search) where cost matters more than peak intelligence. Launched quietly on Jul 27, 2026 as an OpenRouter/API listing at $0.03/$0.13 per 1M tokens, making it the cheapest 1M-context multimodal model available at release. Closed-weights and API-only; Alibaba published no technical report, benchmark suite, or architecture details, though community speculation points to a small sparse-MoE design.

Anthropic's flagship Opus model, released July 24, 2026 — positioned as the go-to model for most knowledge work and automation, approaching the capability of Claude Fable 5 in many categories at roughly half the price. Built for demanding reasoning, autonomous coding, software development, and long-horizon agentic work. Introduces a five-level 'effort' dial exposed to developers on the Claude API and Platform, letting them trade compute and tokens for capability — at lower effort it preserves much of its performance while using fewer tokens and costing less to run. 1M-token context window (available at standard token pricing, not a separate long-context surcharge) with up to 128K output tokens; text, vision, and code. Standard API pricing $5/$25 per Mtok (the same as its predecessor Opus 4.8), plus a Fast mode at $10/$50. Anthropic describes it as its most aligned Opus model and the least susceptible to being tricked into misuse. Becomes the default model for Claude Max subscribers and is available across Anthropic's paid plans; scores 61 on the Artificial Analysis Intelligence Index. Closed weights; architecture, parameter count, and training compute undisclosed.

Google DeepMind's fastest and most cost-effective 3.5-class model, released July 21, 2026 for low-latency and high-throughput agentic workloads like agentic search and document processing. Runs at ~350 output tokens/s (Artificial Analysis) with configurable thinking levels and built-in computer use, priced at $0.30 / $2.50 per 1M input/output tokens. Multimodal over a 1M-token context and a large step up on 3.1 Flash-Lite: Terminal-Bench 2.1 54% (vs 31%), GDM-MRCR v2 72.2% (vs 60.1%), GDPval-AA v2 1140 (vs 642); on several agentic and coding evals it even surpasses 3 Flash (SWE-Bench Pro 54.2% vs 49.6%, OSWorld-Verified 74.0% vs 65.1%). Available in the Gemini API (AI Studio, Android Studio), Gemini Enterprise, the Gemini app, and rolling out in Google Search.

Google DeepMind's July 2026 workhorse Flash model, built for scaling agentic workflows. Multimodal over a 1M-token context, it improves on Gemini 3.5 Flash in coding, knowledge work, and computer use while cutting output-token usage ~17% (up to 65% on some benchmarks like DeepSWE) and taking fewer reasoning steps and tool calls. Ships at a lower price than 3.5 Flash ($1.50 / $7.50 per 1M input/output tokens). Google-reported gains: DeepSWE 49% (vs 37%), MLE-Bench 63.9% (vs 49.7%), OSWorld-Verified 83.0% (vs 78.4%), GDPval-AA v2 1421 (vs 1349); knowledge cutoff advances to March 2026. Computer use is a built-in client-side tool. Available in the Gemini API (AI Studio, Android Studio, Antigravity), Gemini Enterprise, and the Gemini app.

Moonshot's flagship open-weight agentic model and the largest open model released to date: a 2.8T-parameter MoE (896 experts, 16 active per token) using Kimi Delta Attention and Attention Residuals, with native multimodal input and a 1M-token context. Launched via API on Jul 16, 2026 at $3/$15 per Mtok (cached input $0.30); full open weights published to Hugging Face on Jul 26, 2026 — a day ahead of the announced Jul 27 target — under a Modified MIT license, making it freely downloadable and self-hostable.

Thinking Machines Lab's first model and the leading U.S. open-weights release: a natively multimodal Mixture-of-Experts with 975B total / 41B active parameters that reasons across text, image, and audio inputs and emits text. Pretrained on ~45T tokens; served with a 1M-token context from the Hugging Face weights (256K on the hosted Tinker API). Apache-2.0 licensed (BF16 + NVFP4 checkpoints on Hugging Face), built for developers fine-tuning on proprietary data — coding assistants, agents/tool use, chatbots, and RAG — with an explicit low-cost and censorship-resistance focus. Debuted at 41 on the Artificial Analysis Intelligence Index. Hosted pricing (256K) $3.74/$9.36 per Mtok reflects a limited-time 50% launch discount.

OpenAI's GPT-5.6 series umbrella row. Officially previewed June 26, 2026 as three durable capability tiers — Sol (flagship), Terra (balanced, for everyday work), and Luna (fast and affordable) — introduced with a new `max` reasoning effort for deeper reasoning and an `ultra` mode that leverages subagents to accelerate complex work. In the GPT-5.6 naming system the number marks the generation while Sol/Terra/Luna are tiers that can advance on their own cadence. Initially a limited preview via the API and Codex for a small group of vetted partners (after U.S. government review), with general availability across ChatGPT, Codex, and the API planned in the following weeks.

Balanced, everyday-work tier of OpenAI's GPT-5.6 series, officially previewed June 26, 2026. OpenAI positions Terra as competitive with GPT-5.5 while being roughly 2x cheaper. Shares the series' new `max` reasoning effort and `ultra` subagent mode and OpenAI's GPT-5.6 safety stack. Begins as a limited preview via the API and Codex for vetted partners after U.S. government review, with broad availability planned in the following weeks. Priced at $2.50 / $15 per million input/output tokens.

Flagship tier of OpenAI's GPT-5.6 series, officially previewed June 26, 2026 — OpenAI's strongest model to date. Adds a new `max` reasoning effort for the deepest reasoning and an `ultra` mode that uses subagents to accelerate complex work. Sets a new state of the art on Terminal-Bench 2.1 (command-line, agentic coding) and shows broad gains in long-horizon biology (GeneBench v1) and cybersecurity (ExploitBench, ExploitGym), paired with OpenAI's most robust safety stack and a phased release. Begins as a limited preview via the API and Codex for a small group of vetted partners after U.S. government review, with general availability planned in the following weeks; also launching on Cerebras at up to 750 tokens/sec in July. Priced at $5 / $30 per million input/output tokens.

Fast, low-cost tier of OpenAI's GPT-5.6 series, officially previewed June 26, 2026 — the most affordable model in the family, bringing strong capability at OpenAI's lowest cost. Shares the series' capability-tier naming and GPT-5.6 safety stack. Begins as a limited preview via the API and Codex for vetted partners after U.S. government review, with broader availability planned in the following weeks. Priced at $1 / $6 per million input/output tokens.

Meta Superintelligence Labs' first paid model, released July 9, 2026 in US public preview on the Meta Model API. A natively multimodal reasoning model (text, image, video, PDF, and audio input; text output) with explicit chain-of-thought reasoning and a 1M-token context window that the model actively compacts. Positioned for agentic work and coding — tool use, multi-step workflow coordination, and long-horizon autonomous tasks — and pitched by Meta as roughly a quarter of the price of comparable Anthropic and OpenAI models at $1.25 in / $4.25 out per Mtok (with $20 in free credits per new API account). Marks the first time Meta has charged businesses for one of its models, a departure from the open-weight Llama strategy. Closed weights, undisclosed size. Vendor and third-party benchmarks place it around the Opus 4.8 / GPT-5.5 tier — strongest as an agent/workflow model and in tool-augmented reasoning, competitive but not dominant on coding and multimodal tasks.

SpaceXAI's "Opus-class" agentic flagship, released July 8, 2026 — the first Grok model trained jointly with the coding startup Cursor (Anysphere), on trillions of tokens of real Cursor usage data plus STEM tasks, research papers, and other knowledge work. Targets software engineering, agentic tasks, and knowledge work, with explicit strength in legal and finance use cases (SpaceXAI claims the top spot on the Harvey Legal Agent Benchmark). A Mixture-of-Experts model reported to be built on a ~1.5-trillion-parameter "V9" foundation. Vendor benchmarks are mixed against Anthropic's Opus 4.8 — ahead on DeepSWE 1.0 and Terminal-Bench 2.1, behind on DeepSWE 1.1 and SWE-bench Pro — but markedly more token-efficient (~15,900 output tokens on SWE-bench Pro tasks, ~4.2x fewer than Opus 4.8) and served at ~80 tokens/sec. Base pricing $2/$6 per Mtok; Cursor lists a faster variant at $4/$18. Available in Grok Build (default model), Cursor (all plans), and the SpaceXAI console; initially unavailable in the EU (expected mid-July). xAI was absorbed by SpaceX in Feb 2026 and rebranded SpaceXAI.

Anthropic's most agentic Sonnet model yet, with performance approaching Claude Opus 4.8 at a lower price. Built for coding, tool use (browsers and terminals), and autonomous multi-step agentic work, with selectable effort levels up to xhigh. A substantial upgrade over its predecessor Sonnet 4.6 on reasoning, tool use, coding, and knowledge work, with gains shown on SWE-bench, OSWorld-Verified, BrowseComp, and Humanity's Last Exam. The default model on the Free and Pro plans and available to Max, Team, and Enterprise users; in Claude Code and via the Claude API as claude-sonnet-5. Uses an updated tokenizer (same approach as Opus 4.7). Ships with real-time cyber safeguards enabled by default. Introductory API pricing of $2/$10 per Mtok through Aug 31, 2026, then standard $3/$15.

The low-cost, low-latency tier of ByteDance's Seed 2.1 family (served as Doubao-Seed-2.1-turbo), built for large-scale production with full features and performance ByteDance positions as comparable to Seed 2.1 Pro. Shares the family's coding, long-chain agent, and multimodal-understanding focus and 256K-token context, priced for high-volume online calls. Proprietary; available via Doubao and Volcano Engine. List price ¥3 / ¥15 per million input/output tokens — roughly half the Pro tier.

ByteDance's flagship next-generation agent model (served as Doubao-Seed-2.1-pro), built for the "coding and agent era." A deep-thinking model tuned for strong demand understanding, long-horizon planning, and continuous self-repair across complex coding, long-chain agents, and multi-step engineering delivery, with a 256K-token context. ByteDance reports its core coding, agent, and multimodal capabilities are comparable to GPT-5.5, with the highest score on GDPVal, top-tier results on the Agents' Last Exam, the highest score on MobileWorld, and SOTA results across several visual and video-understanding benchmarks (CharXiv-RQ, MeasureBench, TVBench, TOMATO). Proprietary; available via Doubao and Volcano Engine. List price ¥6 / ¥30 per million input/output tokens.

Moonshot's open coding-focused agentic model built on K2.6, with native vision/video input, forced thinking mode, and stronger long-horizon software-engineering performance.

Native multimodal MiniMax model with a one-million-token context, sparse attention, and agentic coding/cowork positioning.

An open-weight text-diffusion model built on the Gemma 4 26B-A4B MoE backbone (25.2B total / 3.8B active). Denoises text in parallel 256-token blocks for up to ~4x faster generation (1,000+ tok/s on an H100), with a 256K context and text, image, and video input. Apache-2.0.

The public, guardrailed sibling of Claude Mythos 5 and Anthropic's most capable widely released model, built for long-horizon agentic work, coding, vision, and knowledge workflows. Launched June 9, 2026 across the Claude API, AWS, and Microsoft Foundry, then suspended three days later under a U.S. export-control directive. Anthropic says those controls were lifted June 30 and Fable 5 was restored globally on July 1 across Claude Platform, Claude.ai, Claude Code, and Claude Cowork, with cloud partner access being re-enabled. Its safeguards route flagged cybersecurity, biology/chemistry, and distillation requests to Claude Opus 4.8.

Multimodal sibling of Qwen3.7-Max that adds vision input and GUI grounding for screen perception, browser automation, and hybrid GUI+CLI agent workflows. 1M-token context; closed-weights and API-only. Previewed at the May 2026 Alibaba Cloud Summit and reached general availability in June 2026 at a low price point ($0.40/$1.60 per 1M tokens).

A dense 12B member of the Gemma 4 family with a unified, encoder-free multimodal architecture: vision and audio are projected straight into the LLM backbone. First medium-size Gemma to natively ingest audio; runs on a 16GB laptop. 256K context, Apache-2.0.

Nex AGI's open-weight agentic flagship, post-trained on Qwen3.5-397B-A17B (397B total / ~17B active MoE) by the Shanghai Innovation Institute-led Nex alliance. Built around an "Agentic Thinking" framework for long-horizon coding, deep research, tool calling, and terminal execution; accepts text and image input and emits text with explicit reasoning traces and function calling. Apache-2.0, ~262K context. Nex reports parity with GPT-5.5 and Claude Opus 4.7 on several agentic and coding evals. A smaller Nex-N2-mini (35B/3B-active) was announced but is not yet open-sourced.

StepFun's high-efficiency multimodal sparse-MoE successor to Step-3.5-Flash: a ~196B-total / ~11B-active vision-language model with native image and video understanding, a 256K context, and selectable reasoning tiers (high/medium/low). Tuned for coding agents and search workflows.

Anthropic's most capable model, with strengthened agentic and long-running task performance.

Announced at Google I/O 2026; emphasizes deep multimodal reasoning over a 2M-token context. Recent reporting says the broad launch slipped from June toward July while testers continue using it in Google Antigravity and LMArena.

Google's fast, cost-efficient Gemini 3.5 tier, unveiled at I/O 2026. Multimodal over a 1M-token context and tuned for agentic and coding workflows; Google says it beats Gemini 3.1 Pro on coding and tool-use while running ~4x faster.

OpenAI's May 2026 GPT-5.5 release: a stronger frontier workhorse positioned for deep reasoning, coding, multimodal analysis, and long-context agent workflows. OpenAI lists an 800K-token input context and 128K-token output limit, with API pricing at $3 / $20 per million input/output tokens.

xAI's agentic flagship with a 1M-token context and aggressive API pricing.

Meta's new frontier model behind Meta AI for U.S. users, identified in reporting as the public release of the former Avocado effort and positioned to compete with Gemini, GPT, and Claude on multimodal assistant tasks.

Google DeepMind's Gemma 4 advanced-reasoning open model for personal computers, part of the April 2026 Gemma 4 family.

Z.ai's native-multimodal vision agent: the first GLM model designed from the start as a multimodal agent, taking image, video, and text input and producing agent-oriented output (tool calling, task decomposition, and GUI interaction). Served via API with a ~203K-token context.

Moonshot's open native multimodal agentic model for long-horizon coding, visual interface generation, and autonomous tool orchestration.

Mistral's March 2026 Small release: the first Mistral model to unify reasoning (Magistral), multimodal understanding (Pixtral), and agentic coding (Devstral) into one Apache 2.0 model. A 119B-total / ~6B-active Mixture-of-Experts (128 experts, 4 active per token) with native text+image input, a 256K context, and a configurable reasoning_effort toggle for fast or deep responses. API pricing is $0.15 / $0.60 per million input/output tokens.

Historical rumor/codename row retained for provenance. Recent reporting identifies the public productized model as Muse Spark, now tracked separately in the catalog.

Workhorse GPT-5 release with a dedicated Thinking mode; widely deployed across ChatGPT and the API.

The flagship of Alibaba's small dense Qwen3.5 models. Independent analysis (Artificial Analysis) rated it the most intelligent model under 10B parameters at launch — roughly double the score of the next-closest sub-10B models — and the most intelligent multimodal model under 15B, leading peers on MMMU-Pro (~69%). A dense 9B with native vision, a 262K-token context, and the Qwen3.5 family's unified hybrid thinking / non-thinking mode. Native weights are BF16; in 4-bit it needs ~6GB, within reach of consumer laptops. High intelligence comes with heavy reasoning token usage (~260M output tokens to run the Intelligence Index).

A dense 4B in Alibaba's small Qwen3.5 family, rated by Artificial Analysis as the most intelligent model under 5B parameters at launch — outscoring several 7B–9B peers despite roughly half the parameters. Native vision, a 262K-token context, and the family's hybrid thinking / non-thinking mode; Apache-2.0 licensed. Scores ~65% on MMMU-Pro multimodal reasoning and runs in ~3GB at 4-bit, suitable for lightweight on-device agents.

A dense 2B Qwen3.5 model built for high-throughput, low-latency edge and on-device use. Despite its size it matches a 7B-class peer on Artificial Analysis's Intelligence Index. Apache-2.0, with native vision, a 262K-token context, and the family's hybrid thinking / non-thinking mode; runs in under 2GB at 4-bit, fitting laptops and smartphones.

The smallest Qwen3.5 model — a dense 0.8B designed for the most constrained on-device deployments, operating in non-thinking (instruct) mode by default. Apache-2.0, with native vision, a 262K-token context, and the family's hybrid thinking / non-thinking mode; needs roughly 2GB of VRAM and runs under 2GB at 4-bit, targeting smartphones and embedded hardware. Notable for a sub-1B model, it still scores ~26% on MMMU-Pro multimodal reasoning.

Native vision-language MoE supporting 201 languages with a 1M-token context.

Generally available multimodal flagship with native tool use and a 2M-token context.

Introduced genuinely autonomous multi-file coding and stronger computer use.

OpenAI's February 2026 Codex update, optimized for agentic software engineering in ChatGPT, Codex, and the API. GPT-5.3-Codex improves code quality, patch reliability, repository-scale reasoning, and long-running autonomous coding workflows while keeping the 400K-token input context and 128K-token output limit of the Codex line.

Open multimodal Kimi model that adds native visual agentic intelligence, instant and thinking modes, and agent-swarm workflows on top of the K2 base.

OpenAI's December 2025 Codex model for agentic coding, released after GPT-5.2 with stronger repository understanding, code generation, and tool-use behavior for software engineering agents. The API model is listed with a 400K-token input context, 128K-token output limit, and $1.25 / $10 per million input/output tokens.

OpenAI's December 2025 GPT-5.2 general model release, positioned as a stronger default for reasoning, coding, vision, instruction following, and long-context analysis. OpenAI lists a 400K-token input context, 128K-token output limit, and $2 / $12 per million input/output tokens.

Open 106B-class vision-language model with native multimodal function calling for visual agents.

Mistral's largest open-weight MoE, aimed at frontier reasoning while remaining self-hostable.

Anthropic's fast, low-cost Claude 4.5 model, released in October 2025 for latency-sensitive coding, tool-use, and customer-facing agent workloads. Anthropic positions it as bringing near-Sonnet capability to the Haiku tier at substantially lower cost and higher speed.

Anthropic's September 2025 Sonnet release, positioned as its strongest model for coding, agents, and computer-use workflows at launch. Proprietary API model with text, vision, and code capabilities, 200K context, and Sonnet-tier list pricing.

Google's open multimodal model: 128k context, 140+ languages, runs on a single GPU.

Vision-language GLM based on GLM-4.5-Air, covering image, video, document, grounding, and GUI-agent tasks.

Rumored next major Grok model. Elon Musk said after GPT-5's launch that Grok 5 would arrive before the end of 2025, but no broad public Grok 5 release is logged in this catalog yet.

Anthropic's August 2025 Opus point release, focused on stronger coding, reasoning, and agentic reliability over Claude Opus 4. Proprietary API model with text, vision, and code capabilities.

Google's enhanced Gemini 2.5 reasoning mode for harder math, science, coding, and multimodal analysis. Previewed at Google I/O 2025 and later made available to Gemini app subscribers, Deep Think uses more deliberative reasoning for complex prompts.

Google's lowest-latency, lowest-cost Gemini 2.5 tier, designed for summarization, classification, extraction, routing, and other high-volume production tasks. Proprietary API model with a 1M-token context and multimodal support.

xAI's fourth-generation Grok line, preceding the later 4.x API updates already tracked in the catalog.

Baidu's largest ERNIE 4.5 vision-language MoE, supporting text, image, and video inputs with thinking and non-thinking modes.

Updated MIT-licensed Kimi-VL reasoning model with better multimodal reasoning, video understanding, high-resolution perception, and lower thinking-token use.

Google's faster, lower-cost Gemini 2.5 model for high-throughput multimodal and agentic workloads. It brought Gemini 2.5's reasoning improvements to a production Flash tier with a 1M-token context and broad text, image, audio, video, and coding support.

First Claude 4 Opus model, positioned for long-running agentic and coding work before the 4.x point releases.

Google's mobile-first Gemma 3n model variant, built with a MatFormer-style architecture for efficient on-device multimodal inference. The E4B variant has roughly 4B effective parameters, supports text, vision, audio, and video-oriented use cases, and is released under Gemma terms.

Open audio foundation model for audio understanding, generation, speech recognition, audio QA, captioning, and speech conversation.

Efficient MIT-licensed vision-language MoE for OCR, image/video understanding, long documents, and OS-style agent tasks.

Reasoning model released alongside o4-mini with tool use, image reasoning, and stronger agentic problem solving.

API model family focused on coding, instruction following, and one-million-token long-context work.

Meta's flagship open-weight MoE; highest MMLU among open models at release.

Efficient open-weight MoE designed for very long context on modest hardware.

Meta's announced but unreleased Llama 4 teacher model: a multimodal MoE with 288B active parameters and nearly 2T total parameters. Meta says it was still training when Scout and Maverick shipped and that those released models were distilled from Behemoth.

Local omni-modal Qwen model that supports text, image, audio, video, and speech generation in a 7B package.

Reasoning-focused Gemini 2.5 model that made thinking a core part of Google's flagship model line.

Apache-licensed Small update adding vision and a 128K context window to the efficient 24B line.

Anthropic's first hybrid-reasoning Sonnet. Shut down May 11, 2026 as the 4.x line matured.

xAI's third-generation model family, introduced with stronger reasoning, search, and coding modes.

Vision-language Qwen2.5 model for image, document, video, and agentic visual grounding tasks.

Doubao 1.5 Pro update positioned for stronger multimodal, reasoning, and agentic work in Volcano Engine.

Moonshot's multimodal reinforcement-learning reasoning model, reported as matching OpenAI o1 on math, coding, and multimodal reasoning.

Open MiniMax generation with MiniMax-Text-01 and MiniMax-VL-01 long-context models.

Second-generation StepFun foundation model line with larger-scale multimodal and reasoning ambitions.

First Gemini 2.0 release, built for native multimodal input/output, tool use, and agentic product integrations.

General release of OpenAI's o1 reasoning model with stronger deliberative reasoning and multimodal ChatGPT integration.

AWS-native multimodal model with a 300k context; size and architecture undisclosed.

Lower-cost multimodal Nova understanding model for text, image, and video inputs.

Fast, lower-cost Claude 3.5 model for latency-sensitive coding, tool-use, and customer-facing workloads.

First Llama family release with native vision models, alongside smaller edge-oriented 1B and 3B text models.

Open multimodal model family trained for strong image understanding, pointing, and visual grounding.

Mistral's first open multimodal model, adding image understanding to a Mistral text backbone.

Second-generation Grok release with Grok-2 and Grok-2 mini for chat, coding, reasoning, and image-enabled product experiences.

8B vision-language model for local image, multi-image, OCR, and video understanding, with llama.cpp and Ollama support.

Major Sonnet upgrade that became Anthropic's default high-intelligence workhorse for coding, writing, and visual reasoning.

The 2024 omni-modal model that defined a generation of assistants. Deprecated in Feb 2026 and fully retired across ChatGPT on April 3, 2026.

Falcon 2 generation, including text and vision-language 11B models under a permissive TII license.

StepFun's first major vision-language model, released after the Step-1 language model.

Highest-capability Claude 3 model, launched with Sonnet and Haiku and Anthropic's first major vision-capable Claude family.

Gemini generation that introduced production-scale long context, eventually expanding to a two-million-token window.

Zhipu's GLM-4 flagship generation, launched as the successor to ChatGLM3 with stronger tool use and multimodal variants.

Google's first natively multimodal Gemini flagship, since superseded by the 1.5/2/3 lines.

Lower-cost GPT-4 generation with a 128K context window, introduced at OpenAI DevDay.

Baidu's fourth-generation ERNIE flagship, announced with stronger understanding, generation, reasoning, and memory.

Open vision-language assistant and one of the most widely run early local multimodal models.

Second EXAONE generation, improving bilingual Korean-English performance and enterprise deployment options.

The model that brought reliable multi-step reasoning to the mainstream; size never disclosed.

LG AI Research's first EXAONE foundation model generation, introduced as a large multimodal expert AI.